01641nas a2200109   4500008004100000245006800041210006700109520121800176100001901394700001701413856010101430       2025                        eng d00aExploiting Vehicular Data for Exposure-Aware Pedestrian Routing0 aExploiting Vehicular Data for ExposureAware Pedestrian Routing3 aVehicular traffic is one of the major sources of air pollution in urban settings, making it essential to clearly understand how much and where vehicle emissions impact residents. Recent approaches manage to yield pollution maps at the microscopic level by processing GPS trajectories of vehicles. That is achieved by applying mathematical models to estimate instantaneous emissions from GPS data, extending estimates to areas without data through missing data imputation, and further considering air dispersion factors. In this work, we leverage such inferred knowledge to implement an emission-aware pedestrian routing strategy and to study its impact on the reduction of exposure to vehicular pollutants and walking time. The study is realized through simulations of large masses of pedestrians over a medium-sized city in Italy, analyzing the interplay between the two factors - exposure versus walking time - in terms of time efficiency of paths and changes over existing habits both at a global and at an individual level. Experiments suggest that exposure-aware routing can yield a significant margin of improvement in health over most paths with minor effects on mobility, making it feasible and effective.1 aAliyev, Gurban1 aNanni, Mirco  uhttps://kdd.isti.cnr.it/publications/exploiting-vehicular-data-exposure-aware-pedestrian-routing00524nas a2200121   4500008004100000020002200041245008500063210006900148260003800217100001900255700001700274856011100291       2025                        eng d  a978-3-031-86370-700aFrom GPS Traces to Individual Emission Exposure: A Data-Driven Four-Step Process0 aFrom GPS Traces to Individual Emission Exposure A DataDriven Fou  aChambSpringer Nature Switzerland1 aAliyev, Gurban1 aNanni, Mirco  uhttps://kdd.isti.cnr.it/publications/gps-traces-individual-emission-exposure-data-driven-four-step-process02019nas a2200109   4500008004100000245007200041210006900113260005400182520161000236100001901846856004401865       2025                        eng d00aOptimization of Exposure-Aware Routing for Vehicles and Pedestrians0 aOptimization of ExposureAware Routing for Vehicles and Pedestria  aOsaka, JapanbAssociation for Computing Machinery3 aVehicular traffic is a significant source of air pollution in cities, exposing city residents and tourists to harmful emissions. 
To tackle this issue, our PhD research investigates how exposure to vehicular emissions in urban areas can be estimated and how this exposure can be mitigated through green routing.
The work builds on three research questions: how to estimate car emissions and their dispersion; how to reduce pedestrian exposure through rerouting with a reasonable trade-off; and how to jointly simulate pedestrians and vehicles that dynamically adapt to each other.
In line with these questions, we developed a pipeline to estimate vehicular emissions and dispersion, simulated exposure-aware pedestrian routing, and demonstrated its effectiveness in reducing exposure with minimal travel-time cost. 
Our most recent contribution is a joint simulation of pedestrians and vehicles, tested on real-world data, which achieved over 95\% reduction in emissions along pedestrian-dense roads.
We are planning to extend the framework by considering static resident populations in the co-evolutionary simulation. 
Additionally, a systematic comparison of the framework’s performance across diverse urban environments is necessary to assess its generalizability.
Furthermore, we aim to investigate how variations in vehicle speed influence emission levels and, consequently, affect the outcomes of our exposure-aware routing simulations. Finally, we plan to analyze how emission-aware routing impacts fairness in exposure distribution across both non-moving residents and moving pedestrians.1 aAliyev, Gurban  uhttps://doi.org/10.1145/3748777.374880801700nas a2200109   4500008004100000245007300041210006900114520130600183100001901489700001701508856006501525       2025                        eng d00aVehicle-Pedestrian Optimization Framework for Exposure-Aware Routing0 aVehiclePedestrian Optimization Framework for ExposureAware Routi3 aVehicular traffic is a major source of air pollution in urban areas, exposing pedestrians and residents to harmful emissions. Recent works have proposed exposure-aware pedestrian routing strategies based on static emission maps. In this study, we extend this approach to a dynamic, multi-agent simulation framework involving both cars and pedestrians. Starting from the initial fastest-path routing, we simulate the co-evolution of vehicular emissions and pedestrian exposure over multiple steps, where pedestrian flows dynamically influence car emissions, and vice versa. Two routing strategies are explored: global weighting, where a shared trade-off between travel time and exposure is selected, and local weighting, where each trip independently chooses its optimal trade-off. Experiments on real-world urban data of a medium-sized city in Italy show that both strategies achieve significant reductions in pedestrian exposure, but differ in their impact on vehicle emissions and travel times. Global weighting yields more coordinated adaptation but at a higher systemic cost, while local weighting achieves more balanced outcomes with lower disruption. These results provide insights into designing urban routing policies that jointly optimize mobility efficiency and environmental sustainability.1 aAliyev, Gurban1 aNanni, Mirco  uhttps://link.springer.com/article/10.1007/s11036-025-02459-400601nas a2200133   4500008004100000020002200041245013300063210006900196260001200265100001900277700001900296700002500315856012700340       2024                        eng d  a978-989-758-709-200aEnhancing Privacy and Utility in Federated Learning: A Hybrid P2P and Server-Based Approach with Differential Privacy Protection0 aEnhancing Privacy and Utility in Federated Learning A Hybrid P2P  bINSTICC1 aCorbucci, Luca1 aMonreale, Anna1 aPellungrini, Roberto  uhttps://kdd.isti.cnr.it/publications/enhancing-privacy-and-utility-federated-learning-hybrid-p2p-and-server-based-approach02088nas a2200241   4500008004100000245012300041210006900164260003400233520127400267100002301541700002501564700003001589700001801619700002101637700002301658700002001681700002001701700001701721700001901738700001701757700001801774856005401792       2024                        eng d00aAn Overview of Recent Approaches to Enable Diversity in Large Language Models through Aligning with Human Perspectives0 aOverview of Recent Approaches to Enable Diversity in Large Langu  aTorino, ItaliabELRA and ICCL3 aThe varied backgrounds and experiences of human annotators inject different opinions and potential biases into the data, inevitably leading to disagreements. Yet, traditional aggregation methods fail to capture individual judgments since they rely on the notion of a single ground truth. Our aim is to review prior contributions to pinpoint the shortcomings that might cause stereotypical content generation. As a preliminary study, our purpose is to investigate state-of-the-art approaches, primarily focusing on the following two research directions. First, we investigate how adding subjectivity aspects to LLMs might guarantee diversity. We then look into the alignment between humans and LLMs and discuss how to measure it. Considering existing gaps, our review explores possible methods to mitigate the perpetuation of biases targeting specific communities. However, we recognize the potential risk of disseminating sensitive information due to the utilization of socio-demographic data in the training process. These considerations underscore the inclusion of diverse perspectives while taking into account the critical importance of implementing robust safeguards to protect individuals' privacy and prevent the inadvertent propagation of sensitive information.1 aMuscato, Benedetta1 aMala, Chandana, Sree1 aManerba, Marta, Marchiori1 aGezici, Gizem1 aGiannotti, Fosca1 aAbercrombie, Gavin1 aBasile, Valerio1 aBernadi, Davide1 aDudy, Shiran1 aFrenda, Simona1 aHavens, Lucy1 aTonelli, Sara  uhttps://aclanthology.org/2024.nlperspectives-1.5/00468nas a2200133   4500008004100000245007500041210006900116100001900185700002300204700002700227700001900254700002400273856003700297       2024                        eng d00aPUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning0 aPUFFLE Balancing Privacy Utility and Fairness in Federated Learn1 aCorbucci, Luca1 aHeikkila, Mikko, A1 aNoguero, David, Solans1 aMonreale, Anna1 aKourtellis, Nicolas  uhttps://arxiv.org/abs/2407.1522400428nas a2200121   4500008004100000245005200041210005100093260001300144100001900157700002300176700001800199856008900217       2023                        eng d00aAgnostic Label-Only Membership Inference Attack0 aAgnostic LabelOnly Membership Inference Attack  bSpringer1 aMonreale, Anna1 aNaretto, Francesca1 aRizzo, Simone  uhttps://kdd.isti.cnr.it/publications/agnostic-label-only-membership-inference-attack01696nas a2200097   4500008004100000245011300041210006900154520131100223100001901534856004501553       2023                        eng d00aAnalysis, Prediction and Mitigation of Exposure to Vehicular Air Pollution Based on Multi-Source Urban Data.0 aAnalysis Prediction and Mitigation of Exposure to Vehicular Air 3 aAn increasing amount of vehicular emissions in urban air pollution create a health risk for urban residents. Meanwhile, calculation and analysis of vehicular pollution using GPS trajectories and microscopic models is getting more popular as this method proves to be more useful and reliable in comparison to other methods. However, GPS-trajectory-based estimations suffer from the lack of GPS data and absence of validation/calibration of estimated emission amounts. Another problem is in the assessment of pollution levels using GPS trajectories as previous studies only consider changes in total vehicular emissions and ignore air quality guideline levels. In this paper, the methodology and preliminary results of experiments conducted for imputation of missing emission data are reported. An existing graph convolutional network model which is designed to predict traffic flows is adopted to estimate vehicular emissions in Pisa. This approach is based on the assumption that the same model can predict traffic emissions as a traffic flow and resulting emission are correlated. In the end of the paper, there is a discussion of future research directions planned to be taken during my PhD period to address issues in the estimation, analysis and mitigation of exposure to vehicular emissions in cities.1 aAliyev, Gurban  uhttps://ceur-ws.org/Vol-3478/paper13.pdf00549nas a2200133   4500008004100000245009600041210006900137300001100206490000600217100001900223700002300242700002100265856012900286       2023                        eng d00aAttributed Stream Hypergraphs: temporal modeling of node-attributed high-order interactions0 aAttributed Stream Hypergraphs temporal modeling of nodeattribute  a1–190 v81 aFailla, Andrea1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/attributed-stream-hypergraphs-temporal-modeling-node-attributed-high-order-interactions01277nas a2200121   4500008004100000245003900041210003800080260000900118520092900127100002001056700002401076856005501100       2023                        eng d00aAUC-based Selective Classification0 aAUCbased Selective Classification  bPMLR3 aSelective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This framework trades off coverage (probability of accepting a prediction) with predictive performance, typically measured by distributive loss functions. In many application scenarios, such as credit scoring, performance is instead measured by ranking metrics, such as the Area Under the ROC Curve (AUC). We propose a model-agnostic approach to associate a selection function to a given probabilistic binary classifier. The approach is specifically targeted at optimizing the AUC. We provide both theoretical justifications and a novel algorithm, called AUCROSS, to achieve such a goal. Experiments show that our method succeeds in trading-off coverage for AUC, improving over existing selective classification methods targeted at optimizing accuracy.1 aPugnana, Andrea1 aRuggieri, Salvatore  uhttps://proceedings.mlr.press/v206/pugnana23a.html00475nas a2200109   4500008004100000245008100041210006900122100002300191700001900214700002100233856011100254       2023                        eng d00aEvaluating the Privacy Exposure of Interpretable Global and Local Explainers0 aEvaluating the Privacy Exposure of Interpretable Global and Loca1 aNaretto, Francesca1 aMonreale, Anna1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/evaluating-privacy-exposure-interpretable-global-and-local-explainers00505nas a2200121   4500008004100000245007600041210006900117100002300186700002500209700001900234700002200253856010800275       2023                        eng d00aEXPHLOT: EXplainable Privacy assessment for Human LOcation Trajectories0 aEXPHLOT EXplainable Privacy assessment for Human LOcation Trajec1 aNaretto, Francesca1 aPellungrini, Roberto1 aFadda, Daniele1 aRinzivillo, Salvo  uhttps://kdd.isti.cnr.it/publications/exphlot-explainable-privacy-assessment-human-location-trajectories01287nas a2200109   4500008004100000245004400041210004300085260001600128520096900144100001901113856004501132       2023                        eng d00aExplain and Interpret Few-Shot Learning0 aExplain and Interpret FewShot Learning  bCEUR-WS.org3 aRecent advancements in Artificial Intelligence have been fueled by vast datasets, powerful computing resources, and sophisticated algorithms. However, traditional Machine Learning models face limitations in handling scarce data. Few-Shot Learning (FSL) offers a promising solution by training models on a small number of examples per class. This manuscript introduces FXI-FSL, a framework for eXplainability and Interpretability in FSL, which aims to develop post-hoc explainability algorithms and interpretableby-design alternatives. A noteworthy contribution is the SIamese Network EXplainer (SINEX), a post-hoc approach shedding light on Siamese Network behavior. The proposed framework seeks to unveil the rationale behind FSL models, instilling trust in their real-world applications. Moreover, it emerges as a safeguard for developers, facilitating models fine-tuning prior to deployment, and as a guide for end users navigating the decisions of these models1 aFedele, Andrea  uhttps://ceur-ws.org/Vol-3554/paper38.pdf01467nas a2200145   4500008004100000020002200041245004900063210004800112260003800160520097900198100001901177700002301196700001901219856008301238       2023                        eng d  a978-3-031-44067-000aExplaining Black-Boxes in Federated Learning0 aExplaining BlackBoxes in Federated Learning  aChambSpringer Nature Switzerland3 aFederated Learning has witnessed increasing popularity in the past few years for its ability to train Machine Learning models in critical contexts, using private data without moving them. Most of the work in the literature proposes algorithms and architectures for training neural networks, which although they present high performance in different predicting tasks and are easy to be learned with a cooperative mechanism, their predictive reasoning is obscure. Therefore, in this paper, we propose a variant of SHAP, one of the most widely used explanation methods, tailored to Horizontal server-based Federated Learning. The basic idea is having the possibility to explain an instance's prediction performed by the trained Machine Leaning model as an aggregation of the explanations provided by the clients participating in the cooperation. We empirically test our proposal on two different tabular datasets, and we observe interesting and encouraging preliminary results.1 aCorbucci, Luca1 aGuidotti, Riccardo1 aMonreale, Anna  uhttps://kdd.isti.cnr.it/publications/explaining-black-boxes-federated-learning00391nas a2200085   4500008004100000245007800041210006900119100000500188856011200193       2023                        eng d00aFair Federated Learning methodology based on Multi-Objective Optimization0 aFair Federated Learning methodology based on MultiObjective Opti1 a  uhttps://kdd.isti.cnr.it/publications/fair-federated-learning-methodology-based-multi-objective-optimization02085nas a2200265   4500008004100000022001400041245010300055210006900158260001600227490000700243520114900250100002001399700002001419700001801439700002701457700001701484700002501501700001701526700002001543700001901563700002301582700002001605700001901625856017501644       2023                        eng d  a1388-195700aGenerative AI models should include detection mechanisms as a condition for public releaseAbstract0 aGenerative AI models should include detection mechanisms as a co  cJan-12-20230 v253 aThe new wave of ‘foundation models’—general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney)—represent a dramatic advance in the state of the art for AI. But their use also introduces a range of new risks, which has prompted an ongoing conversation about possible regulatory mechanisms. Here we propose a specific principle that should be incorporated into legislation: that any organization developing a foundation model intended for public use must demonstrate a reliable detection mechanism for the content it generates, as a condition of its public release. The detection mechanism should be made publicly available in a tool that allows users to query, for an arbitrary item of content, whether the item was generated (wholly or partly) by the model. In this paper, we argue that this requirement is technically feasible and would play an important role in reducing certain risks from new AI models in many domains. We also outline a number of options for the tool’s design, and summarize a number of points where further input from policymakers and researchers would be required.1 aKnott, Alistair1 aPedreschi, Dino1 aChatila, Raja1 aChakraborti, Tapabrata1 aLeavy, Susan1 aBaeza-Yates, Ricardo1 aEyers, David1 aTrotman, Andrew1 aTeal, Paul, D.1 aBiecek, Przemyslaw1 aRussell, Stuart1 aBengio, Yoshua  uhttps://link.springer.com/article/10.1007/s10676-023-09728-4?utm_source=rct_congratemailt&utm_medium=email&utm_campaign=oa_20231028&utm_content=10.1007/s10676-023-09728-401604nas a2200145   4500008004100000245004700041210004700088300001000135490000700145520116200152100002201314700002001336700002101356856008101377       2023                        eng d00aMobility Constraints in Segregation Models0 aMobility Constraints in Segregation Models  a120870 v133 aSince the development of the original Schelling model of urban segregation, several enhancements have been proposed, but none have considered the impact of mobility constraints on model dynamics. Recent studies have shown that human mobility follows specific patterns, such as a preference for short distances and dense locations. This paper proposes a segregation model incorporating mobility constraints to make agents select their location based on distance and location relevance. Our findings indicate that the mobility-constrained model produces lower segregation levels but takes longer to converge than the original Schelling model. We identified a few persistently unhappy agents from the minority group who cause this prolonged convergence time and lower segregation level as they move around the grid centre. Our study presents a more realistic representation of how agents move in urban areas and provides a novel and insightful approach to analyzing the impact of mobility constraints on segregation models. We highlight the significance of incorporating mobility constraints when policymakers design interventions to address urban segregation.1 aGambetta, Daniele1 aMauro, Giovanni1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/mobility-constraints-segregation-models01339nas a2200121   4500008004100000245006100041210005800102260001500160520095300175100002001128700002401148856004501172       2023                        eng d00aA Model-Agnostic Heuristics for Selective Classification0 aModelAgnostic Heuristics for Selective Classification  bAAAI Press3 aSelective classification (also known as classification with reject option) conservatively extends a classifier with a selection function to determine whether or not a prediction should be accepted (i.e., trusted, used, deployed). This is a highly relevant issue in socially sensitive tasks, such as credit scoring. State-of-the-art approaches rely on Deep Neural Networks (DNNs) that train at the same time both the classifier and the selection function. These approaches are model-specific and computationally expensive. We propose a model-agnostic approach, as it can work with any base probabilistic binary classification algorithm, and it can be scalable to large tabular datasets if the base classifier is so. The proposed algorithm, called SCROSS, exploits a cross-fitting strategy and theoretical results for quantile estimation to build the selection function. Experiments on real-world data show that SCROSS improves over existing methods.1 aPugnana, Andrea1 aRuggieri, Salvatore  uhttps://doi.org/10.1609/aaai.v37i8.2613301024nas a2200109   4500008004100000245003900041210003900080260001500119520071400134100002000848856004600868       2023                        eng d00aTopics in Selective Classification0 aTopics in Selective Classification  bAAAI Press3 aIn recent decades, advancements in information technology allowed Artificial Intelligence (AI) systems to predict future outcomes with unprecedented success. This brought the widespread deployment of these methods in many fields, intending to support decision-making. A pressing question is how to make AI systems robust to common challenges in real-life scenarios and trustworthy. In my work, I plan to explore ways to enhance the trustworthiness of AI through the selective classification framework. In this setting, the AI system can refrain from predicting whenever it is not confident enough, allowing it to trade off coverage, i.e. the percentage of instances that receive a prediction, for performance.1 aPugnana, Andrea  uhttps://doi.org/10.1609/aaai.v37i13.2692500482nas a2200121   4500008004100000245007000041210006800111260001300179100001900192700002200211700002300233856010400256       2022                        eng d00aAttribute-aware Community Events in Feature-rich Dynamic Networks0 aAttributeaware Community Events in Featurerich Dynamic Networks  bSpringer1 aFailla, Andrea1 aMazzoni, Federico1 aCitraro, Salvatore  uhttps://kdd.isti.cnr.it/publications/attribute-aware-community-events-feature-rich-dynamic-networks00549nas a2200121   4500008004100000245012000041210006900161260001300230100001900243700002300262700002100285856012100306       2022                        eng d00aAttributed stream-hypernetwork analysis: homophilic behaviors in pairwise and group political discussions on reddit0 aAttributed streamhypernetwork analysis homophilic behaviors in p  bSpringer1 aFailla, Andrea1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/attributed-stream-hypernetwork-analysis-homophilic-behaviors-pairwise-and-group01300nas a2200217   4500008004100000020001800041245008900059210006900148260005900217520057700276100002100853700001900874700001600893700002500909700002900934700001700963700002100980700001901001700001801020856004401038       2022                        eng d  a978145039529800aConnected Vehicle Simulation Framework for Parking Occupancy Prediction (Demo Paper)0 aConnected Vehicle Simulation Framework for Parking Occupancy Pre  aNew York, NY, USAbAssociation for Computing Machinery3 aThis paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.1 aResce, Pierpaolo1 aVorwerk, Lukas1 aHan, Zhiwei1 aCornacchia, Giuliano1 aAlamdari, Omid, Isfahani1 aNanni, Mirco1 aPappalardo, Luca1 aWeimer, Daniel1 aLiu, Yuanting  uhttps://doi.org/10.1145/3557915.356099501472nas a2200157   4500008004100000245006500041210006500106520092000171100002201091700002601113700001901139700002301158700002101181700002001202856009201222       2022                        eng d00aExplaining Black Box with visual exploration of Latent Space0 aExplaining Black Box with visual exploration of Latent Space3 aAutoencoders are a powerful yet opaque feature reduction technique, on top of which we propose a novel way for the joint visual exploration of both latent and real space. By interactively exploiting the mapping between latent and real features, it is possible to unveil the meaning of latent features while providing deeper insight into the original variables. To achieve this goal, we exploit and re-adapt existing approaches from eXplainable Artificial Intelligence (XAI) to understand the relationships between the input and latent features. The uncovered relationships between input features and latent ones allow the user to understand the data structure concerning external variables such as the predictions of a classification model. We developed an interactive framework that visually explores the latent space and allows the user to understand the relationships of the input features with model prediction.1 aBodria, Francesco1 aRinzivillo, Salvatore1 aFadda, Daniele1 aGuidotti, Riccardo1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://diglib.eg.org/xmlui/bitstream/handle/10.2312/evs20221098/085-089.pdf?sequence=101553nas a2200133   4500008004100000245006800041210006700109260001300176520111900189100001901308700002301327700002001350856004901370       2022                        eng d00aExplaining Siamese Networks in Few-Shot Learning for Audio Data0 aExplaining Siamese Networks in FewShot Learning for Audio Data  bSpringer3 aMachine learning models are not able to generalize correctly when queried on samples belonging to class distributions that were never seen during training. This is a critical issue, since real world applications might need to quickly adapt without the necessity of re-training. To overcome these limitations, few-shot learning frameworks have been proposed and their applicability has been studied widely for computer vision tasks. Siamese Networks learn pairs similarity in form of a metric that can be easily extended on new unseen classes. Unfortunately, the downside of such systems is the lack of explainability. We propose a method to explain the outcomes of Siamese Networks in the context of few-shot learning for audio data. This objective is pursued through a local perturbation-based approach that evaluates segments-weighted-average contributions to the final outcome considering the interplay between different areas of the audio spectrogram. Qualitative and quantitative results demonstrate that our method is able to show common intra-class characteristics and erroneous reliance on silent sections.1 aFedele, Andrea1 aGuidotti, Riccardo1 aPedreschi, Dino  uhttps://doi.org/10.1007/978-3-031-18840-4_3600485nas a2200109   4500008004100000245008900041210006900130100002600199700002100225700001900246856011000265       2022                        eng d00aFrom Mean-Field to Complex Topologies: Network Effects on the Algorithmic Bias Model0 aFrom MeanField to Complex Topologies Network Effects on the Algo1 aPansanella, Valentina1 aRossetti, Giulio1 aMilli, Letizia  uhttps://kdd.isti.cnr.it/publications/mean-field-complex-topologies-network-effects-algorithmic-bias-model01517nas a2200169   4500008004100000245007000041210006900111300000700180490000700187520095100194100002001145700002301165700001901188700001701207700002101224856010201245       2022                        eng d00aGenerating mobility networks with generative adversarial networks0 aGenerating mobility networks with generative adversarial network  a580 v113 aThe increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city’s entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people’s movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis.1 aMauro, Giovanni1 aLuca, Massimiliano1 aLonga, Antonio1 aLepri, Bruno1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/generating-mobility-networks-generative-adversarial-networks00526nas a2200121   4500008004100000245009700041210006900138260001700207100001900224700002100243700001800264856012200282       2022                        eng d00aGET-Viz: a library for automatic generation of visual dashboard for geographical time series0 aGETViz a library for automatic generation of visual dashboard fo  aChicago, USA1 aFadda, Daniele1 aNatilli, Michela1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/get-viz-library-automatic-generation-visual-dashboard-geographical-time-series-001538nas a2200181   4500008004100000020001800041245005000059210005000109260005900159520097300218100002501191700001801216700002001234700001701254700002001271700002101291856004401312       2022                        eng d  a978145039529800aHow Routing Strategies Impact Urban Emissions0 aHow Routing Strategies Impact Urban Emissions  aNew York, NY, USAbAssociation for Computing Machinery3 aNavigation apps use routing algorithms to suggest the best path to reach a user's desired destination. Although undoubtedly useful, navigation apps' impact on the urban environment (e.g., CO2 emissions and pollution) is still largely unclear. In this work, we design a simulation framework to assess the impact of routing algorithms on carbon dioxide emissions within an urban environment. Using APIs from TomTom and OpenStreetMap, we find that settings in which either all vehicles or none of them follow a navigation app's suggestion lead to the worst impact in terms of CO2 emissions. In contrast, when just a portion (around half) of vehicles follow these suggestions, and some degree of randomness is added to the remaining vehicles' paths, we observe a reduction in the overall CO2 emissions over the road network. Our work is a first step towards designing next-generation routing principles that may increase urban well-being while satisfying individual needs.1 aCornacchia, Giuliano1 aBöhm, Matteo1 aMauro, Giovanni1 aNanni, Mirco1 aPedreschi, Dino1 aPappalardo, Luca  uhttps://doi.org/10.1145/3557915.356097701635nas a2200181   4500008004100000245011000041210006900151300001100220490000600231520103600237100002101273700001901294700002001313700002101333700002201354700002201376856005501398       2022                        eng d00aThe long-tail effect of the COVID-19 lockdown on Italians’ quality of life, sleep and physical activity0 alongtail effect of the COVID19 lockdown on Italians quality of l  a1–100 v93 aFrom March 2020 to May 2021, several lockdown periods caused by the COVID-19 pandemic have limited people’s usual activities and mobility in Italy, as well as around the world. These unprecedented confinement measures dramatically modified citizens’ daily lifestyles and behaviours. However, with the advent of summer 2021 and thanks to the vaccination campaign that significantly prevents serious illness and death, and reduces the risk of contagion, all the Italian regions finally returned to regular behaviours and routines. Anyhow, it is unclear if there is a long-tail effect on people’s quality of life, sleep- and physical activity-related behaviours. Thanks to the dataset described in this paper, it will be possible to obtain accurate insights of the changes induced by the lockdown period in the Italians’ health that will permit to provide practical suggestions at local, regional, and state institutions and companies to improve infrastructures and services that could be beneficial to Italians’ well being.1 aNatilli, Michela1 aRossi, Alessio1 aTrecroci, Athos1 aCavaggioni, Luca1 aMerati, Giampiero1 aFormenti, Damiano  uhttps://www.nature.com/articles/s41597-022-01376-501528nas a2200157   4500008004100000245006400041210006400105490000700169520099300176100002401169700002001193700002401213700002001237700001601257856009701273       2022                        eng d00aMethods and tools for causal discovery and causal inference0 aMethods and tools for causal discovery and causal inference0 v123 aCausality is a complex concept, which roots its developments across several fields, such as statistics, economics, epidemiology, computer science, and philosophy. In recent years, the study of causal relationships has become a crucial part of the Artificial Intelligence community, as causality can be a key tool for overcoming some limitations of correlation-based Machine Learning systems. Causality research can generally be divided into two main branches, that is, causal discovery and causal inference. The former focuses on obtaining causal knowledge directly from observational data. The latter aims to estimate the impact deriving from a change of a certain variable over an outcome of interest. This article aims at covering several methodologies that have been developed for both tasks. This survey does not only focus on theoretical aspects. But also provides a practical toolkit for interested researchers and practitioners, including software, datasets, and running examples.1 aNogueira, Ana, Rita1 aPugnana, Andrea1 aRuggieri, Salvatore1 aPedreschi, Dino1 aGama, João  uhttps://kdd.isti.cnr.it/publications/methods-and-tools-causal-discovery-and-causal-inference00481nas a2200169   4500008004100000245003300041210003300074260001400107100002100121700002300142700001900165700002100184700002200205700002500227700002000252856003900272       2022                        eng d00aMonitoring Fairness in HOLDA0 aMonitoring Fairness in HOLDA  bIOS Press1 aFontana, Michele1 aNaretto, Francesca1 aMonreale, Anna1 aGiannotti, Fosca1 aSchlobach, Stefan1 aPérez-Ortiz, María1 aTielman, Myrthe  uhttps://doi.org/10.3233/FAIA22020502096nas a2200157   4500008004100000245005900041210005900100520156600159100001901725700001901744700002301763700002101786700002201807700002001829856008901849       2022                        eng d00aSemantic Enrichment of XAI Explanations for Healthcare0 aSemantic Enrichment of XAI Explanations for Healthcare3 aExplaining black-box models decisions is crucial to increase doctors' trust in AI-based clinical decision support systems. However, current eXplainable Artificial Intelligence (XAI) techniques usually provide explanations that are not easily understandable by experts outside of AI. Furthermore, most of the them produce explanations that consider only the input features of the algorithm. However, broader information about the clinical context of a patient is usually available even if not processed by the AI-based clinical decision support system for its decision. Enriching the explanations with relevant clinical information concerning the health status of a patient would increase the ability of human experts to assess the reliability of the AI decision. Therefore, in this paper we present a methodology that aims to enable clinical reasoning by semantically enriching AI explanations. Starting from a medical AI explanation based only on the input features provided to the algorithm, our methodology leverages medical ontologies and NLP embedding techniques to link relevant information present in the patient's clinical notes to the original explanation. We validate our methodology with two experiments involving a human expert. Our results highlight promising performance in correctly identifying relevant information about the diseases of the patients, in particular about the associated morphology. This suggests that the presented methodology could be a first step toward developing a natural language explanation of AI decision support systems.1 aCorbucci, Luca1 aMonreale, Anna1 aPanigutti, Cecilia1 aNatilli, Michela1 aSmiraglio, Simona1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/semantic-enrichment-xai-explanations-healthcare02302nas a2200145   4500008004100000245009400041210006900135260001900204520172300223100002201946700002001968700002101988700002402009856012302033       2022                        eng d00aSoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics.0 aSoBigData European Integrated Infrastructure for Social Mining a  aTirrenia, Pisa3 aSoBigData RI has the ambition to support the rising demand for cross-disciplinary research and innovation on the multiple aspects of social complexity from combined data and model-driven perspectives and the increasing importance of ethics and data scientists’ responsibility as pillars of trustworthy use of Big Data and analytical technology. Digital traces of human activities offer a considerable opportunity to scrutinize the ground truth of individual and collective behaviour at an unprecedented detail and on a global scale. This increasing wealth of data is a chance to understand social complexity, provided we can rely on social mining, i.e., adequate means for accessing big social data and models for extracting knowledge from them. SoBigData RI, with its tools and services, empowers researchers and innovators through a platform for the design and execution of large-scale social mining experiments, open to users with diverse backgrounds, accessible on the cloud (aligned with EOSC), and also exploiting supercomputing facilities. Pushing the FAIR (Findable, Accessible, Interoperable) and FACT (Fair, Accountable, Confidential, and Transparent) principles will render social mining experiments more efficiently designed, adjusted, and repeatable by domain experts that are not data scientists. SoBigData RI moves forward from the simple awareness of ethical and legal challenges in social mining to the development of concrete tools that operationalize ethics with value-sensitive design, incorporating values and norms for privacy protection, fairness, transparency, and pluralism. SoBigData RI is the result of two H2020 grants (g.a. n.654024 and 871042), and it is part of the ESFRI 2021 Roadmap.1 aTrasarti, Roberto1 aGrossi, Valerio1 aNatilli, Michela1 aRapisarda, Beatrice  uhttps://kdd.isti.cnr.it/publications/sobigdata-european-integrated-infrastructure-social-mining-and-big-data-analytics00645nas a2200157   4500008004100000245010000041210006900141100002300210700001900233700002400252700002300276700001900299700002000318700002100338856012800359       2022                        eng d00aStable and actionable explanations of black-box models through factual and counterfactual rules0 aStable and actionable explanations of blackbox models through fa1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aNaretto, Francesca1 aTurini, Franco1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/stable-and-actionable-explanations-black-box-models-through-factual-and-counterfactual00523nas a2200157   4500008004100000245007200041210006900113490001900182100002200201700002100223700002300244700002300267700002000290700001800310856003700328       2021                        eng d00aBenchmarking and Survey of Explanation Methods for Black Box Models0 aBenchmarking and Survey of Explanation Methods for Black Box Mod0 vabs/2102.130761 aBodria, Francesco1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aNaretto, Francesca1 aPedreschi, Dino1 aRinzivillo, S  uhttps://arxiv.org/abs/2102.1307600595nas a2200133   4500008004100000245011600041210006900157100002600226700002300252700002100275700002100296700002000317856012400337       2021                        eng d00aCognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities0 aCognitive network science quantifies feelings expressed in suici1 aJoseph, Simmi, Marina1 aCitraro, Salvatore1 aMorini, Virginia1 aRossetti, Giulio1 aStella, Massimo  uhttps://kdd.isti.cnr.it/publications/cognitive-network-science-quantifies-feelings-expressed-suicide-letters-and-reddit01450nas a2200157   4500008004100000020001400041245007600055210006900131260000900200300001000209520096100219100002101180700002301201700001901224856004901243       2021                        eng d  a1941-129400aConformity: a Path-Aware Homophily measure for Node-Attributed Networks0 aConformity a PathAware Homophily measure for NodeAttributed Netw  c2021  a1 - 13 aUnveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Differently from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.1 aRossetti, Giulio1 aCitraro, Salvatore1 aMilli, Letizia  uhttps://ieeexplore.ieee.org/document/932134801387nas a2200121   4500008004100000245006200041210006200103300000800165520099600173100001401169700002401183856005801207       2021                        eng d00aEstimating the Total Volume of Queries to a Search Engine0 aEstimating the Total Volume of Queries to a Search Engine  a1-13 aWe study the problem of estimating the total number of searches (volume) of queries in a specific domain, which were submitted to a search engine in a given time period. Our statistical model assumes that the distribution of searches follows a Zipf's law, and that the observed sample volumes are biased accordingly to three possible scenarios. These assumptions are consistent with empirical data, with keyword research practices, and with approximate algorithms used to take counts of query frequencies. A few estimators of the parameters of the distribution are devised and experimented, based on the nature of the empirical/simulated data. We apply the methods on the domain of recipes and cooking queries searched in Italian in 2017. The observed volumes of sample queries are collected from Google Trends (continuous data) and SearchVolume (binned data). The estimated total number of queries and total volume are computed for the two cases, and the results are compared and discussed.1 aLillo, F.1 aRuggieri, Salvatore  uhttps://ieeexplore.ieee.org/abstract/document/933624500361nas a2200121   4500008004100000245003500041210003500076260001300111100002100124700002300145700002200168856004900190       2021                        eng d00aExplainable for Trustworthy AI0 aExplainable for Trustworthy AI  bSpringer1 aGiannotti, Fosca1 aNaretto, Francesca1 aBodria, Francesco  uhttps://doi.org/10.1007/978-3-031-24349-3_1001670nas a2200181   4500008004100000245006900041210006500110260001600175300001300191490000700204520109000211100002101301700001901322700002101341700001801362700003001380856007801410       2021                        eng d00aExplaining the difference between men’s and women’s football0 aExplaining the difference between men s and women s football  cApr-08-2021  ae02554070 v163 aWomen’s football is gaining supporters and practitioners worldwide, raising questions about what the differences are with men’s football. While the two sports are often compared based on the players’ physical attributes, we analyze the spatio-temporal events during matches in the last World Cups to compare male and female teams based on their technical performance. We train an artificial intelligence model to recognize if a team is male or female based on variables that describe a match’s playing intensity, accuracy, and performance quality. Our model accurately distinguishes between men’s and women’s football, revealing crucial technical differences, which we investigate through the extraction of explanations from the classifier’s decisions. The differences between men’s and women’s football are rooted in play accuracy, the recovery time of ball possession, and the players’ performance quality. Our methodology may help journalists and fans understand what makes women’s football a distinct sport and coaches design tactics tailored to female teams.1 aPappalardo, Luca1 aRossi, Alessio1 aNatilli, Michela1 aCintia, Paolo1 aConstantinou, Anthony, C.  uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.025540703888nas a2200589   4500008004100000020001400041245010600055210006900161260001500230520215400245100001702399700002302416700002802439700002102467700002202488700001802510700002702528700002302555700001702578700001602595700001802611700002102629700002602650700002102676700002102697700002302718700001802741700001702759700002102776700001802797700001702815700001902832700001702851700002902868700001902897700002102916700001802937700002302955700002202978700002003000700001903020700001903039700002303058700001803081700002403099700001703123700001803140700002203158700002603180700002703206856006503233       2021                        eng d  a1572-843900aGive more data, awareness and control to individual citizens, and they will help COVID-19 containment0 aGive more data awareness and control to individual citizens and   c2021/02/023 aThe rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.1 aNanni, Mirco1 aAndrienko, Gennady1 aBarabasi, Albert-Laszlo1 aBoldrini, Chiara1 aBonchi, Francesco1 aCattuto, Ciro1 aChiaromonte, Francesca1 aComandé, Giovanni1 aConti, Marco1 aCoté, Mark1 aDignum, Frank1 aDignum, Virginia1 aDomingo-Ferrer, Josep1 aFerragina, Paolo1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aHelbing, Dirk1 aKaski, Kimmo1 aKertész, János1 aLehmann, Sune1 aLepri, Bruno1 aLukowicz, Paul1 aMatwin, Stan1 aJiménez, David, Megías1 aMonreale, Anna1 aMorik, Katharina1 aOliver, Nuria1 aPassarella, Andrea1 aPasserini, Andrea1 aPedreschi, Dino1 aPentland, Alex1 aPianesi, Fabio1 aPratesi, Francesca1 aRinzivillo, S1 aRuggieri, Salvatore1 aSiebes, Arno1 aTorra, Vicenc1 aTrasarti, Roberto1 avan den Hoven, Jeroen1 aVespignani, Alessandro  uhttps://link.springer.com/article/10.1007/s10676-020-09572-w02525nas a2200205   4500008004100000020001400041245007100055210006900126260001600195300001100211490000800222520189700230100001802127700002302145700001902168700001902187700002002206700002102226856007202247       2021                        eng d  a0004-370200aGLocalX - From Local to Global Explanations of Black Box AI Models0 aGLocalX From Local to Global Explanations of Black Box AI Models  c2021/05/01/  a1034570 v2943 aArtificial Intelligence (AI) has come to prominence as one of the major components of our society, with applications in most aspects of our lives. In this field, complex and highly nonlinear machine learning models such as ensemble models, deep neural networks, and Support Vector Machines have consistently shown remarkable accuracy in solving complex tasks. Although accurate, AI models often are “black boxes” which we are not able to understand. Relying on these models has a multifaceted impact and raises significant concerns about their transparency. Applications in sensitive and critical domains are a strong motivational factor in trying to understand the behavior of black boxes. We propose to address this issue by providing an interpretable layer on top of black box models by aggregating “local” explanations. We present GLocalX, a “local-first” model agnostic explanation method. Starting from local explanations expressed in form of local decision rules, GLocalX iteratively generalizes them into global explanations by hierarchically aggregating them. Our goal is to learn accurate yet simple interpretable models to emulate the given black box, and, if possible, replace it entirely. We validate GLocalX in a set of experiments in standard and constrained settings with limited or no access to either data or local explanations. Experiments show that GLocalX is able to accurately emulate several models with simple and small models, reaching state-of-the-art performance against natively global solutions. Our findings show how it is often possible to achieve a high level of both accuracy and comprehensibility of classification models, even in complex domains with high-dimensional data, without necessarily trading one property for the other. This is a key requirement for a trustworthy AI, necessary for adoption in high-stakes decision making applications.1 aSetzu, Mattia1 aGuidotti, Riccardo1 aMonreale, Anna1 aTurini, Franco1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://www.sciencedirect.com/science/article/pii/S000437022100008400494nas a2200133   4500008004100000020001400041245011300055210006900168260001500237100002100252700002000273700002000293856004700313       2021                        eng d  a2364-416800aIntroduction to the special issue on social mining and big data ecosystem for open, responsible data science0 aIntroduction to the special issue on social mining and big data   c2021/03/051 aPappalardo, Luca1 aGrossi, Valerio1 aPedreschi, Dino  uhttps://doi.org/10.1007/s41060-021-00253-501965nas a2200145   4500008004100000022001400041245013400055210006900189300000800258490000700266520145600273100002501729700002101754856004401775       2021                        eng d  a2220-996400aA Mechanistic Data-Driven Approach to Synthesize Human Mobility Considering the Spatial, Temporal, and Social Dimensions Together0 aMechanistic DataDriven Approach to Synthesize Human Mobility Con  a5990 v103 aModelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. Those models that capture some social perspectives of human mobility utilize trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose the Spatial, Temporal and Social Exploration and Preferential Return model (STS-EPR), which embeds mechanisms to capture the spatial, temporal, and social aspects together. We compare the trajectories produced by STS-EPR with respect to real-world trajectories and synthetic trajectories generated by two state-of-the-art generative models on a set of standard mobility measures. Our experiments conducted on an open dataset show that STS-EPR, overall, outperforms existing spatial-temporal or social models demonstrating the importance of modelling adequately the sociality to capture precisely all the other dimensions of human mobility. We further investigate the impact of the tile shape of the spatial tessellation on the performance of our model. STS-EPR, which is open-source and tested on open data, represents a step towards the design of a mechanistic data-driven model that captures all the aspects of human mobility comprehensively.1 aCornacchia, Giuliano1 aPappalardo, Luca  uhttps://www.mdpi.com/2220-9964/10/9/59900466nas a2200109   4500008004100000245007500041210006900116100002100185700002300206700001900229856010800248       2021                        eng d00aA new approach for cross-silo federated learning and its privacy risks0 anew approach for crosssilo federated learning and its privacy ri1 aFontana, Michele1 aNaretto, Francesca1 aMonreale, Anna  uhttps://kdd.isti.cnr.it/publications/new-approach-cross-silo-federated-learning-and-its-privacy-risks-000429nas a2200121   4500008004100000245007500041210006900116260000900185100002100194700002300215700001900238856005000257       2021                        eng d00aA new approach for cross-silo federated learning and its privacy risks0 anew approach for crosssilo federated learning and its privacy ri  bIEEE1 aFontana, Michele1 aNaretto, Francesca1 aMonreale, Anna  uhttps://doi.org/10.1109/PST52912.2021.964775300476nas a2200145   4500008004100000245006400041210006400105260001300169100002100182700001900203700002300222700001900245700001700264856004900281       2021                        eng d00aPrivacy Risk Assessment of Individual Psychometric Profiles0 aPrivacy Risk Assessment of Individual Psychometric Profiles  bSpringer1 aMariani, Giacomo1 aMonreale, Anna1 aNaretto, Francesca1 aSoares, Carlos1 aTorgo, Luís  uhttps://doi.org/10.1007/978-3-030-88942-5_3200501nas a2200109   4500008004100000245010900041210006900150260000700219100002500226700002100251856011900272       2021                        eng d00aSTS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together0 aSTSEPR Modelling individual mobility considering the spatial tem  c051 aCornacchia, Giuliano1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/sts-epr-modelling-individual-mobility-considering-spatial-temporal-and-social00506nas a2200133   4500008004100000245007700041210006900118300000900187490000700196100002100203700002000224700002100244856010700265       2021                        eng d00aToward a Standard Approach for Echo Chamber Detection: Reddit Case Study0 aToward a Standard Approach for Echo Chamber Detection Reddit Cas  a53900 v111 aMorini, Virginia1 aPollacci, Laura1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/toward-standard-approach-echo-chamber-detection-reddit-case-study00710nas a2200181   4500008004100000245011900041210006900160300001400229490000800243100002500251700002000276700002200296700002200318700002100340700002000361700002400381856012300405       2021                        eng d00aUnderstanding eating choices among university students: A study using data from cafeteria cashiers’ transactions0 aUnderstanding eating choices among university students A study u  a665–6730 v1251 aLorenzoni, Valentina1 aTriulzi, Isotta1 aMartinucci, Irene1 aToncelli, Letizia1 aNatilli, Michela1 aBarale, Roberto1 aTurchetti, Giuseppe  uhttps://kdd.isti.cnr.it/publications/understanding-eating-choices-among-university-students-study-using-data-cafeteria02976nas a2200373   4500008004100000020002200041245008700063210006900150260005200219520183300271100002102104700001902125700001802144700002002162700002102182700002102203700001902224700002402243700001802267700002302285700001902308700001902327700002102346700001802367700001802385700001902403700002002422700001602442700002002458700001902478700002002497700001802517856006702535       2020                        eng d  a978-3-030-54994-700aAnalysis and Visualization of Performance Indicators in University Admission Tests0 aAnalysis and Visualization of Performance Indicators in Universi  aChambSpringer International Publishingc2020//3 aThis paper presents an analytical platform for evaluation of the performance and anomaly detection of tests for admission to public universities in Italy. Each test is personalized for each student and is composed of a series of questions, classified on different domains (e.g. maths, science, logic, etc.). Since each test is unique for composition, it is crucial to guarantee a similar level of difficulty for all the tests in a session. For this reason, to each question, it is assigned a level of difficulty from a domain expert. Thus, the general difficultness of a test depends on the correct classification of each item. We propose two approaches to detect outliers. A visualization-based approach using dynamic filter and responsive visual widgets. A data mining approach to evaluate the performance of the different questions for five years. We used clustering to group the questions according to a set of performance indicators to provide labeling of the data-driven level of difficulty. The measured level is compared with the a priori assigned by experts. The misclassifications are then highlighted to the expert, who will be able to refine the question or the classification. Sequential pattern mining is used to check if biases are present in the composition of the tests and their performance. This analysis is meant to exclude overlaps or direct dependencies among questions. Analyzing co-occurrences we are able to state that the composition of each test is fair and uniform for all the students, even on several sessions. The analytical results are presented to the expert through a visual web application that loads the analytical data and indicators and composes an interactive dashboard. The user may explore the patterns and models extracted by filtering and changing thresholds and analytical parameters.1 aNatilli, Michela1 aFadda, Daniele1 aRinzivillo, S1 aPedreschi, Dino1 aLicari, Federica1 aSekerinski, Emil1 aMoreira, Nelma1 aOliveira, José, N.1 aRatiu, Daniel1 aGuidotti, Riccardo1 aFarrell, Marie1 aLuckcuck, Matt1 aMarmsoler, Diego1 aCampos, José1 aAstarte, Troy1 aGonnord, Laure1 aCerone, Antonio1 aCouto, Luis1 aDongol, Brijesh1 aKutrib, Martin1 aMonteiro, Pedro1 aDelmas, David  uhttps://link.springer.com/chapter/10.1007/978-3-030-54994-7_1401480nas a2200121   4500008004100000245010100041210006900142300001100211490000600222520104400228100002101272856006501293       2020                        eng d00aANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks0 aANGEL efficient and effective nodecentric community discovery in  a1–230 v53 aCommunity discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.1 aRossetti, Giulio  uhttps://link.springer.com/article/10.1007/s41109-020-00270-601223nas a2200121   4500008004100000245008900041210006900130260009200199520067800291100002000969700001900989856009301008       2020                        eng d00aArtificial Intelligence (AI): new developments and innovations applied to e-commerce0 aArtificial Intelligence AI new developments and innovations appl  bEuropean Parliament's committee on the Internal Market and Consumer Protectionc05/20203 aThis in-depth analysis discusses the opportunities and challenges brought by the recent and the foreseeable developments of Artificial Intelligence into online platforms and marketplaces. The paper advocates the importance to support tustworthy, explainable AI (in order to fight discrimination and manipulation, and empower citizens), and societal-aware AI (in order to fight polarisation, monopolistic concentration and excessive inequality, and pursue diversity and openness).
This document was provided by the Policy Department for Economic, Scientific and Quality of Life Policies at the request of the committee on the Internal Market and Consumer Protection (IMCO).1 aPedreschi, Dino1 aMiliou, Ioanna  uhttps://www.europarl.europa.eu/thinktank/en/document.html?reference=IPOL_IDA(2020)64879102309nas a2200205   4500008004100000022001400041245005800055210005800113260001600171300001400187490000700201520153700208100001801745700001401763700001901777700002001796700002101816700001501837856025101852       2020                        eng d  a1545-597100aAuthenticated Outlier Mining for Outsourced Databases0 aAuthenticated Outlier Mining for Outsourced Databases  cJan-03-2020  a222 - 2350 v173 aThe Data-Mining-as-a-Service (DMaS) paradigm is becoming the focus of research, as it allows the data owner (client) who lacks expertise and/or computational resources to outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises some issues about result integrity: how could the client verify the mining results returned by the server are both sound and complete? In this paper, we focus on outlier mining, an important mining task. Previous verification techniques use an authenticated data structure (ADS) for correctness authentication, which may incur much space and communication cost. In this paper, we propose a novel solution that returns a probabilistic result integrity guarantee with much cheaper verification cost. The key idea is to insert a set of artificial records (ARs) into the dataset, from which it constructs a set of artificial outliers (AOs) and artificial non-outliers (ANOs). The AOs and ANOs are used by the client to detect any incomplete and/or incorrect mining results with a probabilistic guarantee. The main challenge that we address is how to construct ARs so that they do not change the (non-)outlierness of original records, while guaranteeing that the client can identify ANOs and AOs without executing mining. Furthermore, we build a strategic game and show that a Nash equilibrium exists only when the server returns correct outliers. Our implementation and experiments demonstrate that our verification solution is efficient and lightweight.1 aDong, Boxiang1 aWang, Hui1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuo, Wenge  uhttps://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=8858https://ieeexplore.ieee.org/document/8048342/http://xplorestaging.ieee.org/ielx7/8858/9034462/08048342.pdf?arnumber=8048342https://ieeexplore.ieee.org/ielam/8858/9034462/8048342-aam.pdf01938nas a2200241   4500008004100000245008100041210007100122300001000193490000700203520118800210100002001398700002101418700002001439700002501459700002001484700002401504700002401528700001901552700002501571700002601596700001101622856006301633       2020                        eng d00aBias in data-driven artificial intelligence systems—An introductory survey0 aBias in datadriven artificial intelligence systems—An introducto  ae13560 v103 aArtificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions that have far‐reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well‐grounded in a legal frame. In this survey, we focus on data‐driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth.1 aNtoutsi, Eirini1 aFafalios, Pavlos1 aGadiraju, Ujwal1 aIosifidis, Vasileios1 aNejdl, Wolfgang1 aVidal, Maria-Esther1 aRuggieri, Salvatore1 aTurini, Franco1 aPapadopoulos, Symeon1 aKrasanakis, Emmanouil1 aothers  uhttps://onlinelibrary.wiley.com/doi/full/10.1002/widm.135601953nas a2200229   4500008004100000020002200041245008200063210006900145260005200214520119500266100002301461700001901484700001701503700002001520700001701540700001901557700001901576700001701595700002201612700002201634856006701656       2020                        eng d  a978-3-030-46150-800aBlack Box Explanation by Learning Image Exemplars in the Latent Feature Space0 aBlack Box Explanation by Learning Image Exemplars in the Latent   aChambSpringer International Publishingc2020//3 aWe present an approach to explain the decisions of black box models for image classification. While using the black box to label images, our explanation method exploits the latent feature space learned through an adversarial autoencoder. The proposed method first generates exemplar images in the latent feature space and learns a decision tree classifier. Then, it selects and decodes exemplars respecting local decision rules. Finally, it visualizes them in a manner that shows to the user how the exemplars can be modified to either stay within their class, or to become counter-factuals by “morphing” into another class. Since we focus on black box decision systems for image classification, the explanation obtained from the exemplars also provides a saliency map highlighting the areas of the image that contribute to its classification, and areas of the image that push it into another class. We present the results of an experimental evaluation on three datasets and two black box models. Besides providing the most useful and interpretable explanations, we show that the proposed method outperforms existing explainers in terms of fidelity, relevance, coherence, and stability.1 aGuidotti, Riccardo1 aMonreale, Anna1 aMatwin, Stan1 aPedreschi, Dino1 aBrefeld, Ulf1 aFromont, Elisa1 aHotho, Andreas1 aKnobbe, Arno1 aMaathuis, Marloes1 aRobardet, Céline  uhttps://link.springer.com/chapter/10.1007/978-3-030-46150-8_1200461nas a2200109   4500008004100000245007600041210006900117100002100186700002100207700002000228856010300248       2020                        eng d00aCapturing Political Polarization of Reddit Submissions in the Trump Era0 aCapturing Political Polarization of Reddit Submissions in the Tr1 aRossetti, Giulio1 aMorini, Virginia1 aPollacci, Laura  uhttps://kdd.isti.cnr.it/publications/capturing-political-polarization-reddit-submissions-trump-era01567nas a2200193   4500008004100000020001400041245005700055210005700112260001500169300001400184490000700198520100400205100001901209700002001228700001601248700002401264700002001288856006501308       2020                        eng d  a1573-767500aCausal inference for social discrimination reasoning0 aCausal inference for social discrimination reasoning  c2020/04/01  a425 - 4370 v543 aThe discovery of discriminatory bias in human or automated decision making is a task of increasing importance and difficulty, exacerbated by the pervasive use of machine learning and data mining. Currently, discrimination discovery largely relies upon correlation analysis of decisions records, disregarding the impact of confounding biases. We present a method for causal discrimination discovery based on propensity score analysis, a statistical tool for filtering out the effect of confounding variables. We introduce causal measures of discrimination which quantify the effect of group membership on the decisions, and highlight causal discrimination/favoritism patterns by learning regression trees over the novel measures. We validate our approach on two real world datasets. Our proposed framework for causal discrimination has the potential to enhance the transparency of machine learning with tools for detecting discriminatory bias both in the training data and in the learning algorithms.1 aQureshi, Bilal1 aKamiran, Faisal1 aKarim, Asim1 aRuggieri, Salvatore1 aPedreschi, Dino  uhttps://link.springer.com/article/10.1007/s10844-019-00580-x00465nas a2200109   4500008004100000245007600041210006900117100002100186700002300207700001900230856010600249       2020                        eng d00aConformity: A Path-Aware Homophily Measure for Node-Attributed Networks0 aConformity A PathAware Homophily Measure for NodeAttributed Netw1 aRossetti, Giulio1 aCitraro, Salvatore1 aMilli, Letizia  uhttps://kdd.isti.cnr.it/publications/conformity-path-aware-homophily-measure-node-attributed-networks01834nas a2200145   4500008004100000245006100041210006100102260001300163520137000176100001401546700001701560700002101577700002301598856006701621       2020                        eng d00aDigital Footprints of International Migration on Twitter0 aDigital Footprints of International Migration on Twitter  bSpringer3 aStudying migration using traditional data has some limitations. To date, there have been several studies proposing innovative methodologies to measure migration stocks and flows from social big data. Nevertheless, a uniform definition of a migrant is difficult to find as it varies from one work to another depending on the purpose of the study and nature of the dataset used. In this work, a generic methodology is developed to identify migrants within the Twitter population. This describes a migrant as a person who has the current residence different from the nationality. The residence is defined as the location where a user spends most of his/her time in a certain year. The nationality is inferred from linguistic and social connections to a migrant’s country of origin. This methodology is validated first with an internal gold standard dataset and second with two official statistics, and shows strong performance scores and correlation coefficients. Our method has the advantage that it can identify both immigrants and emigrants, regardless of the origin/destination countries. The new methodology can be used to study various aspects of migration, including opinions, integration, attachment, stocks and flows, motivations for migration, etc. Here, we exemplify how trending topics across and throughout different migrant communities can be observed.1 aKim, Jisu1 aSirbu, Alina1 aGiannotti, Fosca1 aGabrielli, Lorenzo  uhttps://link.springer.com/chapter/10.1007/978-3-030-44584-3_2201537nas a2200121   4500008004100000245010000041210006900141520107500210100002301285700001801308700002001326856006901346       2020                        eng d00aDoctor XAI: an ontology-based approach to black-box sequential data classification explanations0 aDoctor XAI an ontologybased approach to blackbox sequential data3 aSeveral recent advancements in Machine Learning involve black-box models: algorithms that do not provide human-understandable explanations in support of their decisions. This limitation hampers the fairness, accountability and transparency of these models; the field of eXplainable Artificial Intelligence (XAI) tries to solve this problem providing human-understandable explanations for black-box models. However, healthcare datasets (and the related learning tasks) often present peculiar features, such as sequential data, multi-label predictions, and links to structured background knowledge. In this paper, we introduce Doctor XAI, a model-agnostic explainability technique able to deal with multi-labeled, sequential, ontology-linked data. We focus on explaining Doctor AI, a multilabel classifier which takes as input the clinical history of a patient in order to predict the next visit. Furthermore, we show how exploiting the temporal dimension in the data and the domain knowledge encoded in the medical ontology improves the quality of the mined explanations.1 aPanigutti, Cecilia1 aPerotti, Alan1 aPedreschi, Dino  uhttps://dl.acm.org/doi/pdf/10.1145/3351095.3372855?download=true02302nas a2200169   4500008004100000022001400041245012100055210006900176300000900245490000700254520174300261100001902004700002002023700002302043700002002066856004602086       2020                        eng d  a1424-822000aError Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts0 aError Estimation of UltraShort Heart Rate Variability Parameters  a71220 v203 aApplication of ultra&ndash;short Heart Rate Variability (HRV) is desirable in order to increase the applicability of HRV features to wrist-worn wearable devices equipped with heart rate sensors that are nowadays becoming more and more popular in people&rsquo;s daily life. This study is focused in particular on the the two most used HRV parameters, i.e., the standard deviation of inter-beat intervals (SDNN) and the root Mean Squared error of successive inter-beat intervals differences (rMSSD). The huge problem of extracting these HRV parameters from wrist-worn devices is that their data are affected by the motion artifacts. For this reason, estimating the error caused by this huge quantity of missing values is fundamental to obtain reliable HRV parameters from these devices. To this aim, we simulate missing values induced by motion artifacts (from 0 to 70%) in an ultra-short time window (i.e., from 4 min to 30 s) by the random walk Gilbert burst model in 22 young healthy subjects. In addition, 30 s and 2 min ultra-short time windows are required to estimate rMSSD and SDNN, respectively. Moreover, due to the fact that ultra-short time window does not permit assessing very low frequencies, and the SDNN is highly affected by these frequencies, the bias for estimating SDNN continues to increase as the time window length decreases. On the contrary, a small error is detected in rMSSD up to 30 s due to the fact that it is highly affected by high frequencies which are possible to be evaluated even if the time window length decreases. Finally, the missing values have a small effect on rMSSD and SDNN estimation. As a matter of fact, the HRV parameter errors increase slightly as the percentage of missing values increase.1 aRossi, Alessio1 aPedreschi, Dino1 aClifton, David, A.1 aMorelli, Davide  uhttps://www.mdpi.com/1424-8220/20/24/712201528nas a2200145   4500008004100000245009700041210006900138260000900207520102400216100001901240700002101259700002301280700002101303856005801324       2020                        eng d00aEstimating countries’ peace index through the lens of the world news as monitored by GDELT0 aEstimating countries peace index through the lens of the world n  c20203 aPeacefulness is a principal dimension of well-being, and its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital data streams have drastically changed research in this field. In the current study, we exploit information extracted from Global Data on Events, Location, and Tone (GDELT) digital news database, to capture peacefulness through the Global Peace Index (GPI). Applying machine learning techniques, we demonstrate that news media attention, sentiment, and social stability from GDELT can be used as proxies for measuring GPI at a monthly level. Additionally, through the variable importance analysis, we show that each country's socio-economic, political, and military profile emerges. This could bring added value to researchers interested in "Data Science for Social Good", to policy-makers, and peacekeeping organizations since they could monitor peacefulness almost real-time, and therefore facilitate timely and more efficient policy-making.
1 aVoukelatou, V.1 aPappalardo, Luca1 aGabrielli, Lorenzo1 aGiannotti, Fosca  uhttps://ieeexplore.ieee.org/abstract/document/926005201588nas a2200229   4500008004100000020001400041245005400055210005000109260001500159520091000174100002101084700002201105700002601127700001801153700002001171700001801191700001901209700002001228700002301248700002201271856006501293       2020                        eng d  a2364-416800aAn ethico-legal framework for social data science0 aethicolegal framework for social data science  c2020/03/313 aThis paper presents a framework for research infrastructures enabling ethically sensitive and legally compliant data science in Europe. Our goal is to describe how to design and implement an open platform for big data social science, including, in particular, personal data. To this end, we discuss a number of infrastructural, organizational and methodological principles to be developed for a concrete implementation. These include not only systematically tools and methodologies that effectively enable both the empirical evaluation of the privacy risk and data transformations by using privacy-preserving approaches, but also the development of training materials (a massive open online course) and organizational instruments based on legal and ethical principles. This paper provides, by way of example, the implementation that was adopted within the context of the SoBigData Research Infrastructure.1 aForgó, Nikolaus1 aHänold, Stefanie1 avan den Hoven, Jeroen1 aKrügel, Tina1 aLishchuk, Iryna1 aMahieu, René1 aMonreale, Anna1 aPedreschi, Dino1 aPratesi, Francesca1 avan Putten, David  uhttps://link.springer.com/article/10.1007/s41060-020-00211-700474nas a2200109   4500008004100000245008000041210006900121100001900190700002100209700002100230856011300251       2020                        eng d00aEvaluating community detection algorithms for progressively evolving graphs0 aEvaluating community detection algorithms for progressively evol1 aCazabet, Rémy1 aBoudebza, Souaad1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/evaluating-community-detection-algorithms-progressively-evolving-graphs00534nas a2200121   4500008004100000245009600041210006900137100002200206700002100228700001800249700002100267856012400288       2020                        eng d00aExplainability Methods for Natural Language Processing: Applications to Sentiment Analysis.0 aExplainability Methods for Natural Language Processing Applicati1 aBodria, Francesco1 aPanisson, André1 aPerotti, Alan1 aPiaggesi, Simone  uhttps://kdd.isti.cnr.it/publications/explainability-methods-natural-language-processing-applications-sentiment-analysis01756nas a2200193   4500008004100000020002200041245008700063210006900150260005200219520106600271100002301337700002301360700002401383700002101407700002501428700002501453700001701478856006701495       2020                        eng d  a978-3-030-61527-700aExplaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars0 aExplaining Sentiment Classification with Synthetic Exemplars and  aChambSpringer International Publishingc2020//3 aWe present xspells, a model-agnostic local approach for explaining the decisions of a black box model for sentiment classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences – albeit they are synthetically generated. xspells generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. We report experiments on two datasets showing that xspells outperforms the well-known lime method in terms of quality of explanations, fidelity, and usefulness, and that is comparable to it in terms of stability.1 aLampridis, Orestis1 aGuidotti, Riccardo1 aRuggieri, Salvatore1 aAppice, Annalisa1 aTsoumakas, Grigorios1 aManolopoulos, Yannis1 aMatwin, Stan  uhttps://link.springer.com/chapter/10.1007/978-3-030-61527-7_2401510nas a2200181   4500008004100000020002200041245004300063210004300106260005200149520094000201100001801141700002301159700001901182700001901201700001901220700002001239856006901259       2020                        eng d  a978-3-030-43823-400aGlobal Explanations with Local Scoring0 aGlobal Explanations with Local Scoring  aChambSpringer International Publishingc2020//3 aArtificial Intelligence systems often adopt machine learning models encoding complex algorithms with potentially unknown behavior. As the application of these “black box” models grows, it is our responsibility to understand their inner working and formulate them in human-understandable explanations. To this end, we propose a rule-based model-agnostic explanation method that follows a local-to-global schema: it generalizes a global explanation summarizing the decision logic of a black box starting from the local explanations of single predicted instances. We define a scoring system based on a rule relevance score to extract global explanations from a set of local explanations in the form of decision rules. Experiments on several datasets and black boxes show the stability, and low complexity of the global explanations provided by the proposed solution in comparison with baselines and state-of-the-art global explainers.1 aSetzu, Mattia1 aGuidotti, Riccardo1 aMonreale, Anna1 aTurini, Franco1 aCellier, Peggy1 aDriessens, Kurt  uhttps://link.springer.com/chapter/10.1007%2F978-3-030-43823-4_1401787nas a2200313   4500008004100000020001400041245004600055210004500101260001500146300001100161520090000172100001701072700002301089700002301112700002101135700001701156700002101173700002301194700002001217700001401237700002901251700002101280700002301301700002001324700002001344700002301364700001901387856006701406       2020                        eng d  a2364-416800aHuman migration: the big data perspective0 aHuman migration the big data perspective  c2020/03/23  a1–203 aHow can big data help to understand the migration phenomenon? In this paper, we try to answer this question through an analysis of various phases of migration, comparing traditional and novel data sources and models at each phase. We concentrate on three phases of migration, at each phase describing the state of the art and recent developments and ideas. The first phase includes the journey, and we study migration flows and stocks, providing examples where big data can have an impact. The second phase discusses the stay, i.e. migrant integration in the destination country. We explore various data sets and models that can be used to quantify and understand migrant integration, with the final aim of providing the basis for the construction of a novel multi-level integration index. The last phase is related to the effects of migration on the source countries and the return of migrants.1 aSirbu, Alina1 aAndrienko, Gennady1 aAndrienko, Natalia1 aBoldrini, Chiara1 aConti, Marco1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aBertoli, Simone1 aKim, Jisu1 aMuntean, Cristina, Ioana1 aPappalardo, Luca1 aPassarella, Andrea1 aPedreschi, Dino1 aPollacci, Laura1 aPratesi, Francesca1 aSharma, Rajesh  uhttps://link.springer.com/article/10.1007%2Fs41060-020-00213-501426nas a2200133   4500008004100000245007500041210006900116300001100185490000600196520096700202100002301169700002101192856007901213       2020                        eng d00aIdentifying and exploiting homogeneous communities in labeled networks0 aIdentifying and exploiting homogeneous communities in labeled ne  a1–200 v53 aAttribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a challenging task presenting EVA, an algorithmic approach designed to maximize a quality function tailoring both structural and homophilic clustering criteria. We evaluate EVA on several real-world labeled networks carrying both nominal and ordinal information, and we compare our approach to other classic and attribute-aware algorithms. Our results suggest that EVA is the only method, among the compared ones, able to discover homogeneous clusters without considerably degrading partition modularity.We also investigate two well-defined applicative scenarios to characterize better EVA: i) the clustering of a mental lexicon, i.e., a linguistic network modeling human semantic memory, and (ii) the node label prediction task, namely the problem of inferring the missing label of a node.1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://appliednetsci.springeropen.com/articles/10.1007/s41109-020-00302-102009nas a2200337   4500008004100000020002200041245008900063210006900152260005200221520095500273100002301228700002101251700002101272700001901293700002401312700001801336700002301354700001901377700001901396700002101415700001801436700001801454700001901472700002001491700001601511700002001527700001901547700002001566700001801586856006701604       2020                        eng d  a978-3-030-54994-700a“Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network0 aKnow Thyself How Personal Music Tastes Shape the LastFm Online S  aChambSpringer International Publishingc2020//3 aAs Nietzsche once wrote “Without music, life would be a mistake” (Twilight of the Idols, 1889.). The music we listen to reflects our personality, our way to approach life. In order to enforce self-awareness, we devised a Personal Listening Data Model that allows for capturing individual music preferences and patterns of music consumption. We applied our model to 30k users of Last.Fm for which we collected both friendship ties and multiple listening. Starting from such rich data we performed an analysis whose final aim was twofold: (i) capture, and characterize, the individual dimension of music consumption in order to identify clusters of like-minded Last.Fm users; (ii) analyze if, and how, such clusters relate to the social structure expressed by the users in the service. Do there exist individuals having similar Personal Listening Data Models? If so, are they directly connected in the social graph or belong to the same community?.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aSekerinski, Emil1 aMoreira, Nelma1 aOliveira, José, N.1 aRatiu, Daniel1 aGuidotti, Riccardo1 aFarrell, Marie1 aLuckcuck, Matt1 aMarmsoler, Diego1 aCampos, José1 aAstarte, Troy1 aGonnord, Laure1 aCerone, Antonio1 aCouto, Luis1 aDongol, Brijesh1 aKutrib, Martin1 aMonteiro, Pedro1 aDelmas, David  uhttps://link.springer.com/chapter/10.1007/978-3-030-54994-7_1102283nas a2200265   4500008004100000245013900041210006900180520142700249100001901676700001801695700002201713700001901735700002101754700002501775700001901800700001701819700002101836700002001857700002201877700001801899700002101917700002301938700001901961856003701980       2020                        eng d00aMobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown0 aMobile phone data analytics against the COVID19 epidemics in Ita3 aUnderstanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements of people between Italian provinces and municipalities, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modelling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. We address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?1 aBonato, Pietro1 aCintia, Paolo1 aFabbri, Francesco1 aFadda, Daniele1 aGiannotti, Fosca1 aLopalco, Pier, Luigi1 aMazzilli, Sara1 aNanni, Mirco1 aPappalardo, Luca1 aPedreschi, Dino1 aPenone, Francesco1 aRinzivillo, S1 aRossetti, Giulio1 aSavarese, Marcello1 aTavoschi, Lara  uhttps://arxiv.org/abs/2004.1127801545nas a2200169   4500008004100000020001400041245006400055210006400119260000900183300001100192520103400203100002501237700002101262700001501283700001901298856005801317       2020                        eng d  a1558-001600aModeling Adversarial Behavior Against Mobility Data Privacy0 aModeling Adversarial Behavior Against Mobility Data Privacy  c2020  a1 - 143 aPrivacy risk assessment is a crucial issue in any privacy-aware analysis process. Traditional frameworks for privacy risk assessment systematically generate the assumed knowledge for a potential adversary, evaluating the risk without realistically modelling the collection of the background knowledge used by the adversary when performing the attack. In this work, we propose Simulated Privacy Annealing (SPA), a new adversarial behavior model for privacy risk assessment in mobility data. We model the behavior of an adversary as a mobility trajectory and introduce an optimization approach to find the most effective adversary trajectory in terms of privacy risk produced for the individuals represented in a mobility data set. We use simulated annealing to optimize the movement of the adversary and simulate a possible attack on mobility data. We finally test the effectiveness of our approach on real human mobility data, showing that it can simulate the knowledge gathering process for an adversary in a more realistic way.1 aPellungrini, Roberto1 aPappalardo, Luca1 aSimini, F.1 aMonreale, Anna  uhttps://ieeexplore.ieee.org/abstract/document/919989300485nas a2200109   4500008004100000245008100041210006900122100002500191700002100216700002100237856011700258       2020                        eng d00aModelling Human Mobility considering Spatial, Temporal and Social Dimensions0 aModelling Human Mobility considering Spatial Temporal and Social1 aCornacchia, Giuliano1 aRossetti, Giulio1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/modelling-human-mobility-considering-spatial-temporal-and-social-dimensions01328nas a2200133   4500008004100000245005300041210005300094260001300147520090400160100002301064700001901087700002101106856006701127       2020                        eng d00aOpinion Dynamic Modeling of Fake News Perception0 aOpinion Dynamic Modeling of Fake News Perception  bSpringer3 aFake news diffusion represents one of the most pressing issues of our online society. In recent years, fake news has been analyzed from several points of view, primarily to improve our ability to separate them from the legit ones as well as identify their sources. Among such vast literature, a rarely discussed theme is likely to play uttermost importance in our understanding of such a controversial phenomenon: the analysis of fake news’ perception. In this work, we approach such a problem by proposing a family of opinion dynamic models tailored to study how specific social interaction patterns concur to the acceptance, or refusal, of fake news by a population of interacting individuals. To discuss the peculiarities of the proposed models, we tested them on several synthetic network topologies, thus underlying when/how they affect the stable states reached by the performed simulations.1 aToccaceli, Cecilia1 aMilli, Letizia1 aRossetti, Giulio  uhttps://link.springer.com/chapter/10.1007/978-3-030-65347-7_3102034nas a2200217   4500008004100000020002200041245006900063210006900132260005200201520129400253100002301547700002501570700001901595700002701614700002001641700002101661700002501682700002501707700001701732856006701749       2020                        eng d  a978-3-030-61527-700aPredicting and Explaining Privacy Risk Exposure in Mobility Data0 aPredicting and Explaining Privacy Risk Exposure in Mobility Data  aChambSpringer International Publishingc2020//3 aMobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the sharing of people’s whereabouts may arise serious privacy concerns. Existing frameworks for privacy risk assessment provide tools to identify and measure privacy risks, but they often (i) have high computational complexity; and (ii) are not able to provide users with a justification of the reported risks. In this paper, we propose expert, a new framework for the prediction and explanation of privacy risk on mobility data. We empirically evaluate privacy risk on real data, simulating a privacy attack with a state-of-the-art privacy risk assessment framework. We then extract individual mobility profiles from the data for predicting their risk. We compare the performance of several machine learning algorithms in order to identify the best approach for our task. Finally, we show how it is possible to explain privacy risk prediction on real data, using two algorithms: Shap, a feature importance-based method and Lore, a rule-based method. Overall, expert is able to provide a user with the privacy risk and an explanation of the risk itself. The experiments show excellent performance for the prediction task.1 aNaretto, Francesca1 aPellungrini, Roberto1 aMonreale, Anna1 aNardini, Franco, Maria1 aMusolesi, Mirco1 aAppice, Annalisa1 aTsoumakas, Grigorios1 aManolopoulos, Yannis1 aMatwin, Stan  uhttps://link.springer.com/chapter/10.1007/978-3-030-61527-7_2702783nas a2200517   4500008004100000020002200041245008500063210006900148260005200217520120400269100002301473700002501496700002701521700002101548700002101569700001801590700002101608700002201629700001901651700002501670700002301695700002201718700002201740700002101762700001601783700002001799700002601819700002401845700002001869700002201889700002301911700002001934700001901954700002201973700002001995700002002015700002002035700001802055700001902073700002302092700001802115700002002133700002402153700002102177856006702198       2020                        eng d  a978-3-030-65965-300aPrediction and Explanation of Privacy Risk on Mobility Data with Neural Networks0 aPrediction and Explanation of Privacy Risk on Mobility Data with  aChambSpringer International Publishingc2020//3 aThe analysis of privacy risk for mobility data is a fundamental part of any privacy-aware process based on such data. Mobility data are highly sensitive. Therefore, the correct identification of the privacy risk before releasing the data to the public is of utmost importance. However, existing privacy risk assessment frameworks have high computational complexity. To tackle these issues, some recent work proposed a solution based on classification approaches to predict privacy risk using mobility features extracted from the data. In this paper, we propose an improvement of this approach by applying long short-term memory (LSTM) neural networks to predict the privacy risk directly from original mobility data. We empirically evaluate privacy risk on real data by applying our LSTM-based approach. Results show that our proposed method based on a LSTM network is effective in predicting the privacy risk with results in terms of F1 of up to 0.91. Moreover, to explain the predictions of our model, we employ a state-of-the-art explanation algorithm, Shap. We explore the resulting explanation, showing how it is possible to provide effective predictions while explaining them to the end-user.1 aNaretto, Francesca1 aPellungrini, Roberto1 aNardini, Franco, Maria1 aGiannotti, Fosca1 aKoprinska, Irena1 aKamp, Michael1 aAppice, Annalisa1 aLoglisci, Corrado1 aAntonie, Luiza1 aZimmermann, Albrecht1 aGuidotti, Riccardo1 aÖzgöbek, Özlem1 aRibeiro, Rita, P.1 aGavaldà, Ricard1 aGama, João1 aAdilova, Linara1 aKrishnamurthy, Yamuna1 aFerreira, Pedro, M.1 aMalerba, Donato1 aMedeiros, Ibéria1 aCeci, Michelangelo1 aManco, Giuseppe1 aMasciari, Elio1 aRas, Zbigniew, W.1 aChristen, Peter1 aNtoutsi, Eirini1 aSchubert, Erich1 aZimek, Arthur1 aMonreale, Anna1 aBiecek, Przemyslaw1 aRinzivillo, S1 aKille, Benjamin1 aLommatzsch, Andreas1 aGulla, Jon, Atle  uhttps://link.springer.com/chapter/10.1007/978-3-030-65965-3_3401574nas a2200193   4500008004100000020001400041245005500055210005400110260001600164300001100180490000800191520100500199100002301204700002301227700001801250700001901268700002101287856007201308       2020                        eng d  a0169-023X00aPRIMULE: Privacy risk mitigation for user profiles0 aPRIMULE Privacy risk mitigation for user profiles  c2020/01/01/  a1017860 v1253 aThe availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the inference of personal and private information. In this paper, we address the privacy issues related to the sharing of user profiles, derived from mobile phone data, by proposing PRIMULE, a privacy risk mitigation strategy. Such a method relies on PRUDEnce (Pratesi et al., 2018), a privacy risk assessment framework that provides a methodology for systematically identifying risky-users in a set of data. An extensive experimentation on real-world data shows the effectiveness of PRIMULE strategy in terms of both quality of mobile user profiles and utility of these profiles for analytical services such as the Sociometer (Furletti et al., 2013), a data mining tool for city users classification.1 aPratesi, Francesca1 aGabrielli, Lorenzo1 aCintia, Paolo1 aMonreale, Anna1 aGiannotti, Fosca  uhttps://www.sciencedirect.com/science/article/pii/S0169023X1830534203169nas a2200469   4500008004100000245011000041210006900151520175800220100001801978700001901996700002102015700002102036700002102057700002002078700001802098700001902116700002202135700002202157700002302179700002102202700002702223700001802250700002202268700002102290700002402311700001902335700002202354700001902376700002202395700002302417700002102440700002202461700001902483700002902502700002702531700002302558700002002581700002302601700001802624700002002642856003702662       2020                        eng d00aThe relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy0 arelationship between human mobility and viral transmissibility d3 aWe describe in this report our studies to understand the relationship between human mobility and the spreading of COVID-19, as an aid to manage the restart of the social and economic activities after the lockdown and monitor the epidemics in the coming weeks and months. We compare the evolution (from January to May 2020) of the daily mobility flows in Italy, measured by means of nation-wide mobile phone data, and the evolution of transmissibility, measured by the net reproduction number, i.e., the mean number of secondary infections generated by one primary infector in the presence of control interventions and human behavioural adaptations. We find a striking relationship between the negative variation of mobility flows and the net reproduction number, in all Italian regions, between March 11th and March 18th, when the country entered the lockdown. This observation allows us to quantify the time needed to "switch off" the country mobility (one week) and the time required to bring the net reproduction number below 1 (one week). A reasonably simple regression model provides evidence that the net reproduction number is correlated with a region's incoming, outgoing and internal mobility. We also find a strong relationship between the number of days above the epidemic threshold before the mobility flows reduce significantly as an effect of lockdowns, and the total number of confirmed SARS-CoV-2 infections per 100k inhabitants, thus indirectly showing the effectiveness of the lockdown and the other non-pharmaceutical interventions in the containment of the contagion. Our study demonstrates the value of "big" mobility data to the monitoring of key epidemic indicators to inform choices as the epidemics unfolds in the coming months.1 aCintia, Paolo1 aFadda, Daniele1 aGiannotti, Fosca1 aPappalardo, Luca1 aRossetti, Giulio1 aPedreschi, Dino1 aRinzivillo, S1 aBonato, Pietro1 aFabbri, Francesco1 aPenone, Francesco1 aSavarese, Marcello1 aChecchi, Daniele1 aChiaromonte, Francesca1 aVineis, Paolo1 aGuzzetta, Giorgio1 aRiccardo, Flavia1 aMarziano, Valentina1 aPoletti, Piero1 aTrentini, Filippo1 aBella, Antonio1 aAndrianou, Xanthi1 aDel Manso, Martina1 aFabiani, Massimo1 aBellino, Stefania1 aBoros, Stefano1 aUrdiales, Alberto, Mateo1 aVescio, Maria, Fenicia1 aBrusaferro, Silvio1 aRezza, Giovanni1 aPezzotti, Patrizio1 aAjelli, Marco1 aMerler, Stefano  uhttps://arxiv.org/abs/2006.0314102108nas a2200217   4500008004100000245005300041210005100094520145500145100002301600700002301623700002101646700002201667700001801689700002001707700002101727700002101748700002201769700002301791700001101814856006501825       2020                        eng d00a(So) Big Data and the transformation of the city0 aSo Big Data and the transformation of the city3 aThe exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.1 aAndrienko, Gennady1 aAndrienko, Natalia1 aBoldrini, Chiara1 aCaldarelli, Guido1 aCintia, Paolo1 aCresci, Stefano1 aFacchini, Angelo1 aGiannotti, Fosca1 aGionis, Aristides1 aGuidotti, Riccardo1 aothers  uhttps://link.springer.com/article/10.1007/s41060-020-00207-300533nas a2200121   4500008004100000245009400041210006900135100002100204700001900225700002300244700002100267856012300288       2020                        eng d00aUTLDR: an agent-based framework for modeling infectious diseases and public interventions0 aUTLDR an agentbased framework for modeling infectious diseases a1 aRossetti, Giulio1 aMilli, Letizia1 aCitraro, Salvatore1 aMorini, Virginia  uhttps://kdd.isti.cnr.it/publications/utldr-agent-based-framework-modeling-infectious-diseases-and-public-interventions00389nas a2200121   4500008004100000245004100041210003700082300001200119100002300131700001900154700002000173856007400193       2019                        eng d00aThe AI black box Explanation Problem0 aAI black box Explanation Problem  a12–131 aGuidotti, Riccardo1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/ai-black-box-explanation-problem01975nas a2200157   4500008004100000245009800041210006900139300001300208490000700221520143200228100001701660700002001677700002101697700002101718856007801739       2019                        eng d00aAlgorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model0 aAlgorithmic bias amplifies opinion fragmentation and polarizatio  ae02132460 v143 aThe flow of information reaching us via the online media platforms is optimized not by the information content or relevance but by popularity and proximity to the target. This is typically performed in order to maximise platform usage. As a side effect, this introduces an algorithmic bias that is believed to enhance fragmentation and polarization of the societal debate. To study this phenomenon, we modify the well-known continuous opinion dynamics model of bounded confidence in order to account for the algorithmic bias and investigate its consequences. In the simplest version of the original model the pairs of discussion participants are chosen at random and their opinions get closer to each other if they are within a fixed tolerance level. We modify the selection rule of the discussion partners: there is an enhanced probability to choose individuals whose opinions are already close to each other, thus mimicking the behavior of online media which suggest interaction with similar peers. As a result we observe: a) an increased tendency towards opinion fragmentation, which emerges also in conditions where the original model would predict consensus, b) increased polarisation of opinions and c) a dramatic slowing down of the speed at which the convergence at the asymptotic state is reached, which makes the system highly unstable. Fragmentation and polarization are augmented by a fragmented initial population.1 aSirbu, Alina1 aPedreschi, Dino1 aGiannotti, Fosca1 aKertész, János  uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.021324602991nas a2200157   4500008004100000245013100041210006900172300000900241490000700250520245000257100002002707700001902727700001902746700002202765856004602787       2019                        eng d00aAnalysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations0 aAnalysis of the Impact of Interpolation Methods of Missing RRint  a31630 v193 aWearable physiological monitors have become increasingly popular, often worn during people’s daily life, collecting data 24 hours a day, 7 days a week. In the last decade, these devices have attracted the attention of the scientific community as they allow us to automatically extract information about user physiology (e.g., heart rate, sleep quality and physical activity) enabling inference on their health. However, the biggest issue about the data recorded by wearable devices is the missing values due to motion and mechanical artifacts induced by external stimuli during data acquisition. This missing data could negatively affect the assessment of heart rate (HR) response and estimation of heart rate variability (HRV), that could in turn provide misleading insights concerning the health status of the individual. In this study, we focus on healthy subjects with normal heart activity and investigate the effects of missing variation of the timing between beats (RR-intervals) caused by motion artifacts on HRV features estimation by randomly introducing missing values within a five min time windows of RR-intervals obtained from the nsr2db PhysioNet dataset by using Gilbert burst method. We then evaluate several strategies for estimating HRV in the presence of missing values by interpolating periods of missing values, covering the range of techniques often deployed in the literature, via linear, quadratic, cubic, and cubic spline functions. We thereby compare the HRV features obtained by handling missing data in RR-interval time series against HRV features obtained from the same data without missing values. Finally, we assess the difference between the use of interpolation methods on time (i.e., the timestamp when the heartbeats happen) and on duration (i.e., the duration of the heartbeats), in order to identify the best methodology to handle the missing RR-intervals. The main novel finding of this study is that the interpolation of missing data on time produces more reliable HRV estimations when compared to interpolation on duration. Hence, we can conclude that interpolation on duration modifies the power spectrum of the RR signal, negatively affecting the estimation of the HRV features as the amount of missing values increases. We can conclude that interpolation in time is the optimal method among those considered for handling data with large amounts of missing values, such as data from wearable sensors.1 aMorelli, Davide1 aRossi, Alessio1 aCairo, Massimo1 aClifton, David, A  uhttps://www.mdpi.com/1424-8220/19/14/316301492nas a2200157   4500008004100000245005700041210005700098300001100155520100400166100001901170700002001189700001601209700002401225700002001249856006501269       2019                        eng d00aCausal inference for social discrimination reasoning0 aCausal inference for social discrimination reasoning  a1–133 aThe discovery of discriminatory bias in human or automated decision making is a task of increasing importance and difficulty, exacerbated by the pervasive use of machine learning and data mining. Currently, discrimination discovery largely relies upon correlation analysis of decisions records, disregarding the impact of confounding biases. We present a method for causal discrimination discovery based on propensity score analysis, a statistical tool for filtering out the effect of confounding variables. We introduce causal measures of discrimination which quantify the effect of group membership on the decisions, and highlight causal discrimination/favoritism patterns by learning regression trees over the novel measures. We validate our approach on two real world datasets. Our proposed framework for causal discrimination has the potential to enhance the transparency of machine learning with tools for detecting discriminatory bias both in the training data and in the learning algorithms.1 aQureshi, Bilal1 aKamiran, Faisal1 aKarim, Asim1 aRuggieri, Salvatore1 aPedreschi, Dino  uhttps://link.springer.com/article/10.1007/s10844-019-00580-x01289nas a2200169   4500008004100000020001400041245009500055210006900150260001500219300000700234490000600241520074900247100002100996700001901017700001901036856006401055       2019                        eng d  a2364-822800aCDLIB: a python library to extract, compare and evaluate communities from complex networks0 aCDLIB a python library to extract compare and evaluate communiti  c2019/07/29  a520 v43 aCommunity Discovery is among the most studied problems in complex network analysis. During the last decade, many algorithms have been proposed to address such task; however, only a few of them have been integrated into a common framework, making it hard to use and compare different solutions. To support developers, researchers and practitioners, in this paper we introduce a python library - namely CDlib - designed to serve this need. The aim of CDlib is to allow easy and standardized access to a wide variety of network clustering algorithms, to evaluate and compare the results they provide, and to visualize them. It notably provides the largest available collection of community detection implementations, with a total of 39 algorithms.1 aRossetti, Giulio1 aMilli, Letizia1 aCazabet, Rémy  uhttps://link.springer.com/article/10.1007/s41109-019-0165-901446nas a2200133   4500008004100000245005900041210005900100260001300159300001400172520101900186100001901205700002101224856006701245       2019                        eng d00aChallenges in community discovery on temporal networks0 aChallenges in community discovery on temporal networks  bSpringer  a181–1973 aCommunity discovery is one of the most studied problems in network science. In recent years, many works have focused on discovering communities in temporal networks, thus identifying dynamic communities. Interestingly, dynamic communities are not mere sequences of static ones; new challenges arise from their dynamic nature. Despite the large number of algorithms introduced in the literature, some of these challenges have been overlooked or little studied until recently. In this chapter, we will discuss some of these challenges and recent propositions to tackle them. We will, among other topics, discuss of community events in gradually evolving networks, on the notion of identity through change and the ship of Theseus paradox, on dynamic communities in different types of networks including link streams, on the smoothness of dynamic communities, and on the different types of complexity of algorithms for their discovery. We will also list available tools and libraries adapted to work with this problem.1 aCazabet, Rémy1 aRossetti, Giulio  uhttps://link.springer.com/chapter/10.1007/978-3-030-23495-9_1000442nas a2200109   4500008004100000245006800041210006600109260001300175100001900188700002100207856010400228       2019                        eng d00aCommunity-Aware Content Diffusion: Embeddednes and Permeability0 aCommunityAware Content Diffusion Embeddednes and Permeability  bSpringer1 aMilli, Letizia1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/community-aware-content-diffusion-embeddednes-and-permeability00443nas a2200097   4500008004100000245008000041210006900121100002300190700002100213856011100234       2019                        eng d00aA complex network approach to semantic spaces: How meaning organizes itself0 acomplex network approach to semantic spaces How meaning organize1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/complex-network-approach-semantic-spaces-how-meaning-organizes-itself00539nas a2200109   4500008004100000245012600041210006900167100002200236700002100258700002300279856012700302       2019                        eng d00aDefining Geographic Markets from Probabilistic Clusters: A Machine Learning Algorithm Applied to Supermarket Scanner Data0 aDefining Geographic Markets from Probabilistic Clusters A Machin1 aBruestle, Stephen1 aPappalardo, Luca1 aGuidotti, Riccardo  uhttps://kdd.isti.cnr.it/publications/defining-geographic-markets-probabilistic-clusters-machine-learning-algorithm-applied03055nas a2200157   4500008004100000245014800041210006900189300000900258490000700267520245900274100002402733700002802757700001902785700002202804856007102826       2019                        eng d00aDo “girls just wanna have fun”? Participation trends and motivational profiles of women in Norway’s ultimate mass participation ski event0 aDo girls just wanna have fun Participation trends and motivation  a25480 v103 aMass participation sporting events (MPSEs) are viewed as encouraging regular exercise in the population, but concerns have been expressed about the extent to which they are inclusive for women. This study focuses on an iconic cross-country skiing MPSE in Norway, the Birkebeiner race (BR), which includes different variants (main, Friday, half-distance, and women-only races). In order to shed light on women’s participation in this specific MPSE, as well as add to the understanding of women’s MPSEs participation in general, this study was set up to: (i) analyze trends in women’s participation, (ii) examine the characteristics, and (iii) identify key factors characterizing the motivational profile of women in different BR races, with emphasis on the full-distance vs. the women-only races. Entries in the different races throughout the period 1996–2018 were analyzed using an autoregressive model. Information on women’s sociodemographic characteristics, sport and exercise participation, and a range of psychological variables (motives, perceptions, overall satisfaction, and future participation intention) were extracted from a market survey and analyzed using a machine learning (ML) approach (n = 1,149). Additionally, qualitative information generated through open-ended questions was analyzed thematically (n = 116). The relative prevalence of women in the main BR was generally low (< 20%). While the other variants contributed to boosting women’s participation in the overall event, a future increment of women in the main BR was predicted, with women’s ratings possibly matching the men’s by the year 2034. Across all races, most of the women were physically active, of medium-high income, and living in the most urbanized region of Norway. Satisfaction and future participation intention were relatively high, especially among the participants in the women-only races. “Exercise goal” was the predominant participation motive. The participants in women-only races assigned greater importance to social aspects, and perceived the race as a tradition, whereas those in the full-distance races were younger and gave more importance to performance aspects. These findings corroborate known trends and challenges in MPSE participation, but also contribute to greater understanding in this under-researched field. Further research is needed in order to gain more knowledge on how to foster women’s participation in MPSEs.1 aCalogiuri, Giovanna1 aJohansen, Patrick, Foss1 aRossi, Alessio1 aThurston, Miranda  uhttps://www.frontiersin.org/articles/10.3389/fpsyg.2019.02548/full00543nas a2200133   4500008004100000245007400041210007100115100003000186700001700216700002100233700002100254700002100275856011300296       2019                        eng d00aDynComm R Package–Dynamic Community Detection for Evolving Networks0 aDynComm R Package–Dynamic Community Detection for Evolving Netwo1 aSarmento, Rui, Portocarre1 aLemos, Luís1 aCordeiro, Mário1 aRossetti, Giulio1 aCardoso, Douglas  uhttps://kdd.isti.cnr.it/publications/dyncomm-r-package%E2%80%93dynamic-community-detection-evolving-networks01236nas a2200121   4500008004100000245004600041210004400087260001300131520084400144100002300988700002101011856008201032       2019                        eng d00aEva: Attribute-Aware Network Segmentation0 aEva AttributeAware Network Segmentation  bSpringer3 aIdentifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.1 aCitraro, Salvatore1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/eva-attribute-aware-network-segmentation00425nas a2200097   4500008004100000245007600041210006900117260001300186100002100199856010700220       2019                        eng d00aExorcising the Demon: Angel, Efficient Node-Centric Community Discovery0 aExorcising the Demon Angel Efficient NodeCentric Community Disco  bSpringer1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/exorcising-demon-angel-efficient-node-centric-community-discovery01664nas a2200145   4500008004100000245007300041210006900114260001300183520117100196100002301367700002301390700001901413700002001432856006601452       2019                        eng d00aExplaining multi-label black-box classifiers for health applications0 aExplaining multilabel blackbox classifiers for health applicatio  bSpringer3 aToday the state-of-the-art performance in classification is achieved by the so-called “black boxes”, i.e. decision-making systems whose internal logic is obscure. Such models could revolutionize the health-care system, however their deployment in real-world diagnosis decision support systems is subject to several risks and limitations due to the lack of transparency. The typical classification problem in health-care requires a multi-label approach since the possible labels are not mutually exclusive, e.g. diagnoses. We propose MARLENA, a model-agnostic method which explains multi-label black box decisions. MARLENA explains an individual decision in three steps. First, it generates a synthetic neighborhood around the instance to be explained using a strategy suitable for multi-label decisions. It then learns a decision tree on such neighborhood and finally derives from it a decision rule that explains the black box decision. Our experiments show that MARLENA performs well in terms of mimicking the black box behavior while gaining at the same time a notable amount of interpretability through compact decision rules, i.e. rules with limited length.1 aPanigutti, Cecilia1 aGuidotti, Riccardo1 aMonreale, Anna1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-030-24409-5_901554nas a2200157   4500008004100000245007400041210006900115520102800184100002301212700001901235700002101254700002001275700002401295700001901319856005801338       2019                        eng d00aFactual and Counterfactual Explanations for Black Box Decision Making0 aFactual and Counterfactual Explanations for Black Box Decision M3 aThe rise of sophisticated machine learning models has brought accurate but obscure decision systems, which hide their logic, thus undermining transparency, trust, and the adoption of artificial intelligence (AI) in socially sensitive and safety-critical contexts. We introduce a local rule-based explanation method, providing faithful explanations of the decision made by a black box classifier on a specific instance. The proposed method first learns an interpretable, local classifier on a synthetic neighborhood of the instance under investigation, generated by a genetic algorithm. Then, it derives from the interpretable classifier an explanation consisting of a decision rule, explaining the factual reasons of the decision, and a set of counterfactuals, suggesting the changes in the instance features that would lead to a different outcome. Experimental results show that the proposed method outperforms existing approaches in terms of the quality of the explanations and of the accuracy in mimicking the black box.1 aGuidotti, Riccardo1 aMonreale, Anna1 aGiannotti, Fosca1 aPedreschi, Dino1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://ieeexplore.ieee.org/abstract/document/892013801227nas a2200133   4500008004100000245007400041210006900115520077700184100002100961700002200982700002501004700002001029856004401049       2019                        eng d00aHuman Mobility from theory to practice: Data, Models and Applications0 aHuman Mobility from theory to practice Data Models and Applicati3 aThe inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library ”scikit-mobility” developed by the presenters of the tutorial.1 aPappalardo, Luca1 aBarlacchi, Gianni1 aPellungrini, Roberto1 aSimini, Filippo  uhttps://doi.org/10.1145/3308560.332009901257nas a2200133   4500008004100000245009600041210006900137260001300206520077300219100002300992700001901015700002301034856006601057       2019                        eng d00aInvestigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers0 aInvestigating Neighborhood Generation Methods for Explanations o  bSpringer3 aGiven the wide use of machine learning approaches based on opaque prediction models, understanding the reasons behind decisions of black box decision systems is nowadays a crucial topic. We address the problem of providing meaningful explanations in the widely-applied image classification tasks. In particular, we explore the impact of changing the neighborhood generation function for a local interpretable model-agnostic explanator by proposing four different variants. All the proposed methods are based on a grid-based segmentation of the images, but each of them proposes a different strategy for generating the neighborhood of the image for which an explanation is required. A deep experimentation shows both improvements and weakness of each proposed approach.1 aGuidotti, Riccardo1 aMonreale, Anna1 aCariaggi, Leonardo  uhttps://link.springer.com/chapter/10.1007/978-3-030-16148-4_500501nas a2200109   4500008004100000245009000041210006900131260001300200100002300213700002100236856013400257       2019                        eng d00a“Know Thyself” How Personal Music Tastes Shape the Last. Fm Online Social Network0 aKnow Thyself How Personal Music Tastes Shape the Last Fm Online   bSpringer1 aGuidotti, Riccardo1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/%E2%80%9Cknow-thyself%E2%80%9D-how-personal-music-tastes-shape-last-fm-online-social-network01943nas a2200157   4500008004100000245006100041210006100102520143800163100002001601700002101621700002301642700001901665700002401684700001901708856005801727       2019                        eng d00aMeaningful explanations of Black Box AI decision systems0 aMeaningful explanations of Black Box AI decision systems3 aBlack box AI systems for automated decision making, often based on machine learning over (big) data, map a user’s features into a class or a score without exposing the reasons why. This is problematic not only for lack of transparency, but also for possible biases inherited by the algorithms from human prejudices and collection artifacts hidden in the training data, which may lead to unfair or wrong decisions. We focus on the urgent open challenge of how to construct meaningful explanations of opaque AI/ML systems, introducing the local-toglobal framework for black box explanation, articulated along three lines: (i) the language for expressing explanations in terms of logic rules, with statistical and causal interpretation; (ii) the inference of local explanations for revealing the decision rationale for a specific case, by auditing the black box in the vicinity of the target instance; (iii), the bottom-up generalization of many local explanations into simple global ones, with algorithms that optimize for quality and comprehensibility. We argue that the local-first approach opens the door to a wide variety of alternative solutions along different dimensions: a variety of data sources (relational, text, images, etc.), a variety of learning problems (multi-label classification, regression, scoring, ranking), a variety of languages for expressing meaningful explanations, a variety of means to audit a black box.1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://aaai.org/ojs/index.php/AAAI/article/view/505001999nas a2200181   4500008004100000245011100041210006900152300001100221490000700232520140700239100002101646700001801667700002101685700002301706700002001729700002101749856004701770       2019                        eng d00aPlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach0 aPlayeRank datadriven performance evaluation and player ranking i  a1–270 v103 aThe problem of evaluating the performance of soccer players is attracting the interest of many companies and the scientific community, thanks to the availability of massive data capturing all the events generated during a match (e.g., tackles, passes, shots, etc.). Unfortunately, there is no consolidated and widely accepted metric for measuring performance quality in all of its facets. In this article, we design and implement PlayeRank, a data-driven framework that offers a principled multi-dimensional and role-aware evaluation of the performance of soccer players. We build our framework by deploying a massive dataset of soccer-logs and consisting of millions of match events pertaining to four seasons of 18 prominent soccer competitions. By comparing PlayeRank to known algorithms for performance evaluation in soccer, and by exploiting a dataset of players’ evaluations made by professional soccer scouts, we show that PlayeRank significantly outperforms the competitors. We also explore the ratings produced by PlayeRank and discover interesting patterns about the nature of excellent performances and what distinguishes the top players from the others. At the end, we explore some applications of PlayeRank—i.e. searching players and player versatility—showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.1 aPappalardo, Luca1 aCintia, Paolo1 aFerragina, Paolo1 aMassucco, Emanuele1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://dl.acm.org/doi/abs/10.1145/334317201650nas a2200301   4500008004100000020002200041245004800063210004800111260005200159520072600211100002500937700001900962700002300981700001901004700001901023700001901042700002101061700002001082700002201102700002101124700002301145700002101168700002401189700002301213700002201236700002301258856006701281       2019                        eng d  a978-3-030-13463-100aPrivacy Risk for Individual Basket Patterns0 aPrivacy Risk for Individual Basket Patterns  aChambSpringer International Publishingc2019//3 aRetail data are of fundamental importance for businesses and enterprises that want to understand the purchasing behaviour of their customers. Such data is also useful to develop analytical services and for marketing purposes, often based on individual purchasing patterns. However, retail data and extracted models may also provide very sensitive information to possible malicious third parties. Therefore, in this paper we propose a methodology for empirically assessing privacy risk in the releasing of individual purchasing data. The experiments on real-world retail data show that although individual patterns describe a summary of the customer activity, they may be successful used for the customer re-identifiation.1 aPellungrini, Roberto1 aMonreale, Anna1 aGuidotti, Riccardo1 aAlzate, Carlos1 aMonreale, Anna1 aBioglio, Livio1 aBitetta, Valerio1 aBordino, Ilaria1 aCaldarelli, Guido1 aFerretti, Andrea1 aGuidotti, Riccardo1 aGullo, Francesco1 aPascolutti, Stefano1 aPensa, Ruggero, G.1 aRobardet, Céline1 aSquartini, Tiziano  uhttps://link.springer.com/chapter/10.1007/978-3-030-13463-1_1101689nas a2200193   4500008004100000245007700041210006900118300001100187490000600198520109400204100002101298700001801319700001901337700002301356700002101379700002001400700002101420856005401441       2019                        eng d00aA public data set of spatio-temporal match events in soccer competitions0 apublic data set of spatiotemporal match events in soccer competi  a1–150 v63 aSoccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure.1 aPappalardo, Luca1 aCintia, Paolo1 aRossi, Alessio1 aMassucco, Emanuele1 aFerragina, Paolo1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://www.nature.com/articles/s41597-019-0247-700404nas a2200121   4500008004100000245004000041210004000081100001700121700002100138700002000159700002100179856008200200       2019                        eng d00aPublic opinion and Algorithmic bias0 aPublic opinion and Algorithmic bias1 aSirbu, Alina1 aGiannotti, Fosca1 aPedreschi, Dino1 aKertész, János  uhttps://ercim-news.ercim.eu/en116/special/public-opinion-and-algorithmic-bias01847nas a2200169   4500008004100000245014600041210006900187300000900256490000600265520126200271100001901533700001801552700002101570700001801591700001901609856004901628       2019                        eng d00aRelationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load0 aRelationship between External and Internal Workloads in Elite So  a51740 v93 aThe use of machine learning (ML) in soccer allows for the management of a large amount of data deriving from the monitoring of sessions and matches. Although the rate of perceived exertion (RPE), training load (S-RPE), and global position system (GPS) are standard methodologies used in team sports to assess the internal and external workload; how the external workload affects RPE and S-RPE remains still unclear. This study explores the relationship between both RPE and S-RPE and the training workload through ML. Data were recorded from 22 elite soccer players, in 160 training sessions and 35 matches during the 2015/2016 season, by using GPS tracking technology. A feature selection process was applied to understand which workload features influence RPE and S-RPE the most. Our results show that the training workloads performed in the previous week have a strong effect on perceived exertion and training load. On the other hand, the analysis of our predictions shows higher accuracy for medium RPE and S-RPE values compared with the extremes. These results provide further evidence of the usefulness of ML as a support to athletic trainers and coaches in understanding the relationship between training load and individual-response in team sports.1 aRossi, Alessio1 aPerri, Enrico1 aPappalardo, Luca1 aCintia, Paolo1 aIaia, Marcello  uhttps://www.mdpi.com/2076-3417/9/23/5174/htm00475nas a2200145   4500008004100000245005700041210005700098100001600155700001300171700001200184700001300196700001400209700001900223856008700242       2019                        eng d00aSAI a Sensible Artificial Intelligence that plays Go0 aSAI a Sensible Artificial Intelligence that plays Go1 aMorandin, F1 aAmato, G1 aGini, R1 aMetta, C1 aParton, M1 aPascutto, G.C.  uhttps://kdd.isti.cnr.it/publications/sai-sensible-artificial-intelligence-plays-go01233nam a2200133   4500008004100000022002200041245008400063210007100147260003900218300000700257520073800264100002001002856007701022       2019                        eng d  a978-88-3339-252-300aSarò Franco - Vita di Franco Turini, executive chef dell’Università di Pisa0 aSarò Franco Vita di Franco Turini executive chef dell Università  aPisa, ItalybPisa University Press  a203 aChi è Franco Turini? Come molti sanno, uno dei pionieri dell’informatica italiana. Ma non è questa la domanda che ci interessa. Quella a cui questo breve saggio si propone di rispondere è una questione molto più importante: chi avrebbe voluto essere Franco Turini?
In questo scritto, la vita e la carriera di Turini vengono ripercorse alla luce della sua vera, unica e irredimibile passione: la cucina. In un intreccio romanzesco, denso di colpi di scena e assolutamente falso e tendenzioso, il contributo di Franco Turini all’informatica e all’intelligenza artiﬁciale si dipana, indissolubilmente intrecciato alla sua passione per i fornelli, attraverso le molte intuizioni geniali che lo hanno colpito mentre cucinava.1 aMalvaldi, Marco  uhttps://store.streetlib.com/it/marco-malvaldi/saro-franco-9788833392523/01172nas a2200121   4500008004100000245004500041210003800086260000900124520081200133100002300945700002400968856005800992       2019                        eng d00aOn The Stability of Interpretable Models0 aStability of Interpretable Models  bIEEE3 aInterpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process. Bias in data collection and preparation, or in model's construction may severely affect the accountability of the design process. We conduct an experimental study of the stability of interpretable models with respect to feature selection, instance selection, and model selection. Our conclusions should raise awareness and attention of the scientific community on the need of a stability impact assessment of interpretable models.1 aGuidotti, Riccardo1 aRuggieri, Salvatore  uhttps://ieeexplore.ieee.org/abstract/document/885215800523nas a2200133   4500008004100000245008400041210006900125300001200194490000700206100001900213700002200232700002100254856011400275       2019                        eng d00aTowards the dynamic community discovery in decentralized online social networks0 aTowards the dynamic community discovery in decentralized online   a23–440 v171 aGuidi, Barbara1 aMichienzi, Andrea1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/towards-dynamic-community-discovery-decentralized-online-social-networks00434nas a2200109   4500008004100000245004800041210004800089100002000137700002200157700002100179856012400200       2019                        eng d00aTransparency in Algorithmic Decision Making0 aTransparency in Algorithmic Decision Making1 aRauber, Andreas1 aTrasarti, Roberto1 aGiannotti, Fosca  uhttps://ercim-news.ercim.eu/en116/special/transparency-in-algorithmic-decision-making-introduction-to-the-special-theme00741nas a2200193   4500008004100000245010300041210006900144100002500213700002000238700002500258700001900283700002100302700002000323700002100343700002100364700002000385700001800405856012400423       2019                        eng d00aA Visual Analytics Platform to Measure Performance on University Entrance Tests (Discussion Paper)0 aVisual Analytics Platform to Measure Performance on University E1 aBoncoraglio, Daniele1 aDeri, Francesca1 aDistefano, Francesco1 aFadda, Daniele1 aFilippi, Giorgio1 aForte, Giuseppe1 aLicari, Federica1 aNatilli, Michela1 aPedreschi, Dino1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/visual-analytics-platform-measure-performance-university-entrance-tests-discussion01882nas a2200157   4500008004100000245006300041210006300104300000700167490000600174520139900180100001901579700002101598700002001619700002101639856006401660       2018                        eng d00aActive and passive diffusion processes in complex networks0 aActive and passive diffusion processes in complex networks  a420 v33 aIdeas, information, viruses: all of them, with their mechanisms, spread over the complex social information, viruses: all tissues described by our interpersonal relations. Usually, to simulate and understand the unfolding of such complex phenomena are used general mathematical models; these models act agnostically from the object of which they simulate the diffusion, thus considering spreading of virus, ideas and innovations alike. Indeed, such degree of abstraction makes it easier to define a standard set of tools that can be applied to heterogeneous contexts; however, it can also lead to biased, incorrect, simulation outcomes. In this work we introduce the concepts of active and passive diffusion to discriminate the degree in which individuals choice affect the overall spreading of content over a social graph. Moving from the analysis of a well-known passive diffusion schema, the Threshold model (that can be used to model peer-pressure related processes), we introduce two novel approaches whose aim is to provide active and mixed schemas applicable in the context of innovations/ideas diffusion simulation.

Our analysis, performed both in synthetic and real-world data, underline that the adoption of exclusively passive/active models leads to conflicting results, thus highlighting the need of mixed approaches to capture the real complexity of the simulated system better.1 aMilli, Letizia1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/article/10.1007/s41109-018-0100-501262nas a2200133   4500008004100000245005000041210005000091520085000141100002500991700002101016700002301037700001901060856004901079       2018                        eng d00aAnalyzing Privacy Risk in Human Mobility Data0 aAnalyzing Privacy Risk in Human Mobility Data3 aMobility data are of fundamental importance for understanding the patterns of human movements, developing analytical services and modeling human dynamics. Unfortunately, mobility data also contain individual sensitive information, making it necessary an accurate privacy risk assessment for the individuals involved. In this paper, we propose a methodology for assessing privacy risk in human mobility data. Given a set of individual and collective mobility features, we define the minimum data format necessary for the computation of each feature and we define a set of possible attacks on these data formats. We perform experiments computing the empirical risk in a real-world mobility dataset, and show how the distributions of the considered mobility features are affected by the removal of individuals with different levels of privacy risk.1 aPellungrini, Roberto1 aPappalardo, Luca1 aPratesi, Francesca1 aMonreale, Anna  uhttps://doi.org/10.1007/978-3-030-04771-9_1001264nas a2200109   4500008004100000245005200041210005200093520088000145100002301025700002401048856008201072       2018                        eng d00aAssessing the Stability of Interpretable Models0 aAssessing the Stability of Interpretable Models3 aInterpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process, which, in particular, comprises data collection and filtering. Selection bias in data collection or in data pre-processing may affect the model learned. Although model induction algorithms are designed to learn to generalize, they pursue optimization of predictive accuracy. It remains unclear how interpretability is instead impacted. We conduct an experimental analysis to investigate whether interpretable models are able to cope with data selection bias as far as interpretability is concerned.1 aGuidotti, Riccardo1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/assessing-stability-interpretable-models01699nas a2200121   4500008004100000245005400041210005300095490000700148520133500155100002101490700001901511856004701530       2018                        eng d00aCommunity Discovery in Dynamic Networks: a Survey0 aCommunity Discovery in Dynamic Networks a Survey0 v513 aNetworks built to model real world phenomena are characeterised by some properties that have attracted the attention of the scientific community: (i) they are organised according to community structure and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and challenging problem started capturing researcher interest recently: the identification of evolving communities. To model the evolution of a system, dynamic networks can be used: nodes and edges are mutable and their presence, or absence, deeply impacts the community structure that composes them. The aim of this survey is to present the distinctive features and challenges of dynamic community discovery, and propose a classification of published approaches. As a "user manual", this work organizes state of art methodologies into a taxonomy, based on their rationale, and their specific instanciation. Given a desired definition of network dynamics, community characteristics and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers to choose in which direction should future research be oriented. 1 aRossetti, Giulio1 aCazabet, Rémy  uhttps://dl.acm.org/citation.cfm?id=317286701271nas a2200145   4500008004100000245006500041210006300106260003600169520077200205100001900977700002100996700002001017700002101037856006701058       2018                        eng d00aDiffusive Phenomena in Dynamic Networks: a data-driven study0 aDiffusive Phenomena in Dynamic Networks a datadriven study  aBoston March 5-8 2018bSpringer3 aEveryday, ideas, information as well as viruses spread over complex social tissues described by our interpersonal relations. So far, the network contexts upon which diffusive phenomena unfold have usually considered static, composed by a fixed set of nodes and edges. Recent studies describe social networks as rapidly changing topologies. In this work – following a data-driven approach – we compare the behaviors of classical spreading models when used to analyze a given social network whose topological dynamics are observed at different temporal-granularities. Our goal is to shed some light on the impacts that the adoption of a static topology has on spreading simulations as well as to provide an alternative formulation of two classical diffusion models.1 aMilli, Letizia1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/chapter/10.1007/978-3-319-73198-8_1301603nas a2200193   4500008004100000245007700041210006900118260001300187520097300200100002301173700001901196700002101215700001701236700002401253700002001277700002101297700002401318856006701342       2018                        eng d00aDiscovering Mobility Functional Areas: A Mobility Data Analysis Approach0 aDiscovering Mobility Functional Areas A Mobility Data Analysis A  bSpringer3 aHow do we measure the borders of urban areas and therefore decide which are the functional units of the territory? Nowadays, we typically do that just looking at census data, while in this work we aim to identify functional areas for mobility in a completely data-driven way. Our solution makes use of human mobility data (vehicle trajectories) and consists in an agglomerative process which gradually groups together those municipalities that maximize internal vehicular traffic while minimizing external one. The approach is tested against a dataset of trips involving individuals of an Italian Region, obtaining a new territorial division which allows us to identify mobility attractors. Leveraging such partitioning and external knowledge, we show that our method outperforms the state-of-the-art algorithms. Indeed, the outcome of our approach is of great value to public administrations for creating synergies within the aggregations of the territories obtained.1 aGabrielli, Lorenzo1 aFadda, Daniele1 aRossetti, Giulio1 aNanni, Mirco1 aPiccinini, Leonardo1 aPedreschi, Dino1 aGiannotti, Fosca1 aLattarulo, Patrizia  uhttps://link.springer.com/chapter/10.1007/978-3-319-73198-8_2702705nas a2200181   4500008004100000245007900041210006900120260001200189300000600201490000600207520211900213100002302332700002302355700001902378700002002397700002102417856008502438       2018                        eng d00aDiscovering temporal regularities in retail customers’ shopping behavior0 aDiscovering temporal regularities in retail customers shopping b  c01/2018  a60 v73 aIn this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customer’s temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity.1 aGuidotti, Riccardo1 aGabrielli, Lorenzo1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0133-001671nas a2200181   4500008004100000245008700041210006900128300001300197490000700210520107500217100001901292700002101311700001801332700001901350700002301369700001901392856007801411       2018                        eng d00aEffective injury forecasting in soccer with GPS training data and machine learning0 aEffective injury forecasting in soccer with GPS training data an  ae02012640 v133 aInjuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer.1 aRossi, Alessio1 aPappalardo, Luca1 aCintia, Paolo1 aIaia, Marcello1 aFernàndez, Javier1 aMedina, Daniel  uhttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.020126401125nas a2200145   4500008004100000245007000041210006900111260001900180520063500199100002300834700002000857700001700877700001900894856006600913       2018                        eng d00aExplaining successful docker images using pattern mining analysis0 aExplaining successful docker images using pattern mining analysi  bSpringer, Cham3 aDocker is on the rise in today’s enterprise IT. It permits shipping applications inside portable containers, which run from so-called Docker images. Docker images are distributed in public registries, which also monitor their popularity. The popularity of an image directly impacts on its usage, and hence on the potential revenues of its developers. In this paper, we present a frequent pattern mining-based approach for understanding how to improve an image to increase its popularity. The results in this work can provide valuable insights to Docker image providers, helping them to design more competitive software products.1 aGuidotti, Riccardo1 aSoldani, Jacopo1 aNeri, Davide1 aBrogi, Antonio  uhttps://link.springer.com/chapter/10.1007/978-3-030-04771-9_900558nas a2200133   4500008004100000245009000041210006900131260001300200100002100213700001900234700002300253700002100276856012700297       2018                        eng d00aExploring Students Eating Habits Through Individual Profiling and Clustering Analysis0 aExploring Students Eating Habits Through Individual Profiling an  bSpringer1 aNatilli, Michela1 aMonreale, Anna1 aGuidotti, Riccardo1 aPappalardo, Luca  uhttps://kdd.isti.cnr.it/publications/exploring-students-eating-habits-through-individual-profiling-and-clustering-analysis01229nas a2200157   4500008004100000245008100041210006900122260001300191520069500204100002000899700002300919700002100942700002100963700002000984856006701004       2018                        eng d00aThe Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis0 aFractal Dimension of Music Geography Popularity and Sentiment An  bSpringer3 aNowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a “fractal” musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians’ popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_1903273nas a2200229   4500008004100000245015000041210006900191300000800260490000700268520247100275100002202746700002102768700002502789700002102814700001902835700002402854700002002878700002002898700002002918700002302938856008202961       2018                        eng d00aGastroesophageal reflux symptoms among Italian university students: epidemiology and dietary correlates using automatically recorded transactions0 aGastroesophageal reflux symptoms among Italian university studen  a1160 v183 aBackground: Gastroesophageal reflux disease (GERD) is one of the most common gastrointestinal disorders
worldwide, with relevant impact on the quality of life and health care costs.The aim of our study is to assess the
prevalence of GERD based on self-reported symptoms among university students in central Italy. The secondary aim is
to evaluate lifestyle correlates, particularly eating habits, in GERD students using automatically recorded transactions
through cashiers at university canteen.
Methods: A web-survey was created and launched through an app, ad-hoc developed for an interactive exchange of
information with students, including anthropometric data and lifestyle habits. Moreover, the web-survey allowed
users a self-diagnosis of GERD through a simple questionnaire. As regard eating habits, detailed collection of meals
consumed, including number and type of dishes, were automatically recorded through cashiers at the university
canteen equipped with an automatic registration system.
Results: We collected 3012 questionnaires. A total of 792 students (26.2% of the respondents) reported typical GERD
symptoms occurring at least weekly. Female sex was more prevalent than male sex. In the set of students with GERD,
the percentage of smokers was higher, and our results showed that when BMI tends to higher values the percentage
of students with GERD tends to increase. When evaluating correlates with diet, we found, among all users, a lower
frequency of legumes choice in GERD students and, among frequent users, a lower frequency of choice of pasta and
rice in GERD students.
Discussion: The results of our study are in line with the values reported in the literature. Nowadays, GERD is a common
problem in our communities, and can potentially lead to serious medical complications; the economic burden
involved in the diagnostic and therapeutic management of the disease has a relevant impact on healthcare costs.
Conclusions: To our knowledge, this is the first study evaluating the prevalence of typical GERD–related symptoms
in a young population of University students in Italy. Considering the young age of enrolled subjects, our prevalence
rate, relatively high compared to the usual estimates, could represent a further negative factor for the future
economic sustainability of the healthcare system.
Keywords: Gastroesophageal reflux disease, GERD, Heartburn, Regurgitation, Diet, Prevalence, University students1 aMartinucci, Irene1 aNatilli, Michela1 aLorenzoni, Valentina1 aPappalardo, Luca1 aMonreale, Anna1 aTurchetti, Giuseppe1 aPedreschi, Dino1 aMarchi, Santino1 aBarale, Roberto1 ade Bortoli, Nicola  uhttps://bmcgastroenterol.biomedcentral.com/articles/10.1186/s12876-018-0832-901833nas a2200169   4500008004100000245005500041210005500096260000700151300000900158490000700167520134100174100002701515700002101542700002301563700002801586856004901614       2018                        eng d00aGravity and scaling laws of city to city migration0 aGravity and scaling laws of city to city migration  c07  a1-190 v133 aModels of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities. Building upon these findings we develop a scaling model which describes internal migration as a two-step decision process, demonstrating that it can partially explain migration fluxes based solely on city size. We then consider the impact of distance and construct a gravity-scaling model by combining the observed scaling patterns with the gravity law of migration. Results show that the scaling laws are a significant feature of human migration and that the inclusion of scaling can overcome the limits of the gravity and the radiation models of human migration.1 aCuriel, Rafael, Prieto1 aPappalardo, Luca1 aGabrielli, Lorenzo1 aBishop, Steven, Richard  uhttps://doi.org/10.1371/journal.pone.019989201318nas a2200157   4500008004100000245006900041210006900110260001300179520080200192100002300994700002001017700001701037700001901054700002001073856006701093       2018                        eng d00aHelping your docker images to spread based on explainable models0 aHelping your docker images to spread based on explainable models  bSpringer3 aDocker is on the rise in today’s enterprise IT. It permits shipping applications inside portable containers, which run from so-called Docker images. Docker images are distributed in public registries, which also monitor their popularity. The popularity of an image impacts on its actual usage, and hence on the potential revenues for its developers. In this paper, we present a solution based on interpretable decision tree and regression trees for estimating the popularity of a given Docker image, and for understanding how to improve an image to increase its popularity. The results presented in this work can provide valuable insights to Docker developers, helping them in spreading their images. Code related to this paper is available at: https://github.com/di-unipi-socc/DockerImageMiner.1 aGuidotti, Riccardo1 aSoldani, Jacopo1 aNeri, Davide1 aBrogi, Antonio1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-030-10997-4_1301581nas a2200397   4500008004100000020002200041245009100063210006900154260004400223300001400267520041400281100001400695700001600709700001700725700001400742700001500756700001600771700002100787700001900808700001700827700001500844700002100859700002000880700002300900700001600923700001800939700002100957700002400978700001901002700001601021700001901037700001801056700001901074700002101093856006901114       2018                        eng d  a978-3-319-61893-700aHow Data Mining and Machine Learning Evolved from Relational Data Base to Data Science0 aHow Data Mining and Machine Learning Evolved from Relational Dat  aChambSpringer International Publishing  a287 - 3063 aDuring the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.1 aAmato, G.1 aCandela, L.1 aCastelli, D.1 aEsuli, A.1 aFalchi, F.1 aGennaro, C.1 aGiannotti, Fosca1 aMonreale, Anna1 aNanni, Mirco1 aPagano, P.1 aPappalardo, Luca1 aPedreschi, Dino1 aPratesi, Francesca1 aRabitti, F.1 aRinzivillo, S1 aRossetti, Giulio1 aRuggieri, Salvatore1 aSebastiani, F.1 aTesconi, M.1 aFlesca, Sergio1 aGreco, Sergio1 aMasciari, Elio1 aSaccà, Domenico  uhttps://link.springer.com/chapter/10.1007%2F978-3-319-61893-7_1701364nas a2200157   4500008004100000245003700041210003300078300001100111520091500122100002001037700002301057700002101080700002101101700002001122856006401142       2018                        eng d00aThe italian music superdiversity0 aitalian music superdiversity  a1–233 aGlobalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs’ melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/article/10.1007/s11042-018-6511-601443nas a2200133   4500008004100000245002500041210002500066260000900091520110000100100002301200700001901223700001801242856004901260       2018                        eng d00aLearning Data Mining0 aLearning Data Mining  c20183 aIn the last decade the usage and study of data mining and machine learning algorithms have received an increasing attention from several and heterogeneous fields of research. Learning how and why a certain algorithm returns a particular result, and understanding which are the main problems connected to its execution is a hot topic in the education of data mining methods. In order to support data mining beginners, students, teachers, and researchers we introduce a novel didactic environment. The Didactic Data Mining Environment (DDME) allows to execute a data mining algorithm on a dataset and to observe the algorithm behavior step by step to learn how and why a certain result is returned. DDME can be practically exploited by teachers and students for having a more interactive learning of data mining. Indeed, on top of the core didactic library, we designed a visual platform that allows online execution of experiments and the visualization of the algorithm steps. The visual platform abstracts the coding activity and makes available the execution of algorithms to non-technicians.
1 aGuidotti, Riccardo1 aMonreale, Anna1 aRinzivillo, S  uhttps://ieeexplore.ieee.org/document/863145300538nas a2200145   4500008004100000245006400041210006300105100002300168700001900191700002400210700002000234700001900254700002100273856009800294       2018                        eng d00aLocal Rule-Based Explanations of Black Box Decision Systems0 aLocal RuleBased Explanations of Black Box Decision Systems1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aPedreschi, Dino1 aTurini, Franco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/local-rule-based-explanations-black-box-decision-systems01236nas a2200181   4500008004100000245009100041210006900132300001200201490000600213520065500219100002100874700001900895700001800914700001700932700002000949700002100969856006400990       2018                        eng d00aNDlib: a python library to model and analyze diffusion processes over complex networks0 aNDlib a python library to model and analyze diffusion processes   a61–790 v53 aNowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground. To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.1 aRossetti, Giulio1 aMilli, Letizia1 aRinzivillo, S1 aSirbu, Alina1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/article/10.1007/s41060-017-0086-600599nas a2200157   4500008004100000245007700041210006900118100002000187700002100207700002300228700001900251700002100270700002400291700001900315856010700334       2018                        eng d00aOpen the Black Box Data-Driven Explanation of Black Box Decision Systems0 aOpen the Black Box DataDriven Explanation of Black Box Decision 1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuidotti, Riccardo1 aMonreale, Anna1 aPappalardo, Luca1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/open-black-box-data-driven-explanation-black-box-decision-systems01676nas a2200145   4500008004100000245008600041210006900127520117000196100002301366700002101389700002101410700002101431700002001452856005801472       2018                        eng d00aPersonalized Market Basket Prediction with Temporal Annotated Recurring Sequences0 aPersonalized Market Basket Prediction with Temporal Annotated Re3 aNowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer's decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aPappalardo, Luca1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://ieeexplore.ieee.org/abstract/document/847715701950nas a2200181   4500008004100000245008800041210006900129260001200198490000700210520137400217100002301591700001901614700002201633700002101655700002001676700002401696856004801720       2018                        eng d00aPRUDEnce: a system for assessing privacy risk vs utility in data sharing ecosystems0 aPRUDEnce a system for assessing privacy risk vs utility in data   c08/20180 v113 aData describing human activities are an important source of knowledge useful for understanding individual and collective behavior and for developing a wide range of user services. Unfortunately, this kind of data is sensitive, because people’s whereabouts may allow re-identification of individuals in a de-identified database. Therefore, Data Providers, before sharing those data, must apply any sort of anonymization to lower the privacy risks, but they must be aware and capable of controlling also the data quality, since these two factors are often a trade-off. In this paper we propose PRUDEnce (Privacy Risk versus Utility in Data sharing Ecosystems), a system enabling a privacy-aware ecosystem for sharing personal data. It is based on a methodology for assessing both the empirical (not theoretical) privacy risk associated to users represented in the data, and the data quality guaranteed only with users not at risk. Our proposal is able to support the Data Provider in the exploration of a repertoire of possible data transformations with the aim of selecting one specific transformation that yields an adequate trade-off between data quality and privacy risk. We study the practical effectiveness of our proposal over three data formats underlying many services, defined on real mobility data, i.e., presence data, trajectory data and road segment data.1 aPratesi, Francesca1 aMonreale, Anna1 aTrasarti, Roberto1 aGiannotti, Fosca1 aPedreschi, Dino1 aYanagihara, Tadashi  uhttp://www.tdp.cat/issues16/tdp.a284a17.pdf02707nas a2200145   4500008004100000245005000041210004700091260006400138520221100202100002102413700002202434700002202456700002002478856006302498       2018                        eng d00aSoBigData: Social Mining & Big Data Ecosystem0 aSoBigData Social Mining Big Data Ecosystem  bInternational World Wide Web Conferences Steering Committee3 aOne of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena. Such challenge requires high-level analytics, modeling and reasoning across all the social dimensions above. There is a need to harness these opportunities for scientific advancement and for the social good, compared to the currently prevalent exploitation of big data for commercial purposes or, worse, social control and surveillance. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open ecosystem where big data and social mining research can be carried out. The SoBigData Research Infrastructure (RI) provides an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life as recorded by "big data". The research community uses the SoBigData facilities as a "secure digital wind-tunnel" for large-scale social data analysis and simulation experiments. SoBigData promotes repeatable and open science and supports data science research projects by providing: i) an ever-growing, distributed data ecosystem for procurement, access and curation and management of big social data, to underpin social data mining research within an ethic-sensitive context; ii) an ever-growing, distributed platform of interoperable, social data mining methods and associated skills: tools, methodologies and services for mining, analysing, and visualising complex and massive datasets, harnessing the techno-legal barriers to the ethically safe deployment of big data for social mining; iii) an ecosystem where protection of personal information and the respect for fundamental human rights can coexist with a safe use of the same information for scientific purposes of broad and central societal interest. SoBigData has a dedicated ethical and legal board, which is implementing a legal and ethical framework.1 aGiannotti, Fosca1 aTrasarti, Roberto1 aBontcheva, Kalina1 aGrossi, Valerio  uhttp://www.sobigdata.eu/sites/default/files/www%202018.pdf01683nas a2200181   4500008004100000245005600041210005400097300000700151490000700158520116300165100002301328700001901351700002401370700001901394700002101413700002001434856004701454       2018                        eng d00aA survey of methods for explaining black box models0 asurvey of methods for explaining black box models  a930 v513 aIn recent years, many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness, sometimes at the cost of sacrificing accuracy for interpretability. The applications in which black box decision systems can be used are various, and each approach is typically developed to provide a solution for a specific problem and, as a consequence, it explicitly or implicitly delineates its own definition of interpretability and explanation. The aim of this article is to provide a classification of the main problems addressed in the literature with respect to the notion of explanation and the type of black box system. Given a problem definition, a black box type, and a desired explanation, this survey should help the researcher to find the proposals more useful for his own work. The proposed classification of approaches to open black box models should also be useful for putting the many research open questions in perspective.1 aGuidotti, Riccardo1 aMonreale, Anna1 aRuggieri, Salvatore1 aTurini, Franco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://dl.acm.org/doi/abs/10.1145/323600901714nas a2200157   4500008004100000245009000041210006900131260000900200520116900209100002201378700002201400700002101422700002201443700001701465856007401482       2018                        eng d00aWeak nodes detection in urban transport systems: Planning for resilience in Singapore0 aWeak nodes detection in urban transport systems Planning for res  bIEEE3 aThe availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g., floods, terrorist attacks, etc. . . ). In this perspective, we propose ACHILLES, an application to models people's movements in a given transport mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones, and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.1 aFerretti, Michele1 aBarlacchi, Gianni1 aPappalardo, Luca1 aLucchini, Lorenzo1 aLepri, Bruno  uhttps://ieeexplore.ieee.org/abstract/document/8631413/authors#authors01007nas a2200181   4500008004100000245005700041210005700098260001300155300001400168520046300182100002300645700001900668700001900687700002100706700001600727700001700743856006500760       2017                        eng d00aApplications for Environmental Sensing in EveryAware0 aApplications for Environmental Sensing in EveryAware  bSpringer  a135–1553 aThis chapter provides a technical description of the EveryAware applications for air quality and noise monitoring. Specifically, we introduce AirProbe, for measuring air quality, and WideNoise Plus for estimating environmental noise. We also include an overview on hardware components and smartphone-based measurement technology, and we present the according web backend, e.g., providing for real-time tracking, data storage, analysis and visualizations.

1 aAtzmueller, Martin1 aBecker, Martin1 aMolino, Andrea1 aMueller, Juergen1 aPeters, Jan1 aSirbu, Alina  uhttp://link.springer.com/chapter/10.1007/978-3-319-25658-0_700999nas a2200121   4500008004100000245004200041210004200083520063500125100002500760700002300785700002100808856004800829       2017                        eng d00aAssessing Privacy Risk in Retail Data0 aAssessing Privacy Risk in Retail Data3 aRetail data are one of the most requested commodities by commercial companies. Unfortunately, from this data it is possible to retrieve highly sensitive information about individuals. Thus, there exists the need for accurate individual privacy risk evaluation. In this paper, we propose a methodology for assessing privacy risk in retail data. We define the data formats for representing retail data, the privacy framework for calculating privacy risk and some possible privacy attacks for this kind of data. We perform experiments in a real-world retail dataset, and show the distribution of privacy risk for the various attacks.1 aPellungrini, Roberto1 aPratesi, Francesca1 aPappalardo, Luca  uhttps://doi.org/10.1007/978-3-319-71970-2_302036nas a2200169   4500008004100000245011000041210006900151300001100220490000600231520139500237100002301632700002101655700001901676700002201695700002201717856012701739       2017                        eng d00aAssessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models0 aAssessing the use of mobile phone data to describe recurrent mob  a1609500 v43 aThe recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.1 aPanigutti, Cecilia1 aTizzoni, Michele1 aBajardi, Paolo1 aSmoreda, Zbigniew1 aColizza, Vittoria  uhttps://kdd.isti.cnr.it/publications/assessing-use-mobile-phone-data-describe-recurrent-mobility-patterns-spatial-epidemic02012nas a2200157   4500008004100000245005800041210005800099520153700157100001801694700002101712700001901733700002001752700002101772700001101793856005001804       2017                        eng d00aAuthenticated Outlier Mining for Outsourced Databases0 aAuthenticated Outlier Mining for Outsourced Databases3 aThe Data-Mining-as-a-Service (DMaS) paradigm is becoming the focus of research, as it allows the data owner (client) who lacks expertise and/or computational resources to outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises some issues about result integrity: how could the client verify the mining results returned by the server are both sound and complete? In this paper, we focus on outlier mining, an important mining task. Previous verification techniques use an authenticated data structure (ADS) for correctness authentication, which may incur much space and communication cost. In this paper, we propose a novel solution that returns a probabilistic result integrity guarantee with much cheaper verification cost. The key idea is to insert a set of artificial records (ARs) into the dataset, from which it constructs a set of artificial outliers (AOs) and artificial non-outliers (ANOs). The AOs and ANOs are used by the client to detect any incomplete and/or incorrect mining results with a probabilistic guarantee. The main challenge that we address is how to construct ARs so that they do not change the (non-)outlierness of original records, while guaranteeing that the client can identify ANOs and AOs without executing mining. Furthermore, we build a strategic game and show that a Nash equilibrium exists only when the server returns correct outliers. Our implementation and experiments demonstrate that our verification solution is efficient and lightweight.1 aDong, Boxiang1 aWang, Hui, Wendy1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuo, W  uhttps://ieeexplore.ieee.org/document/8048342/01663nas a2200157   4500008004100000245006500041210006500106260000800171520113100179100002301310700001901333700001701352700002101369700002001390856009501410       2017                        eng d00aClustering Individual Transactional Data for Masses of Users0 aClustering Individual Transactional Data for Masses of Users  bACM3 aMining a large number of datasets recording human activities for making sense of individual data is the key enabler of a new wave of personalized knowledge-based services. In this paper we focus on the problem of clustering individual transactional data for a large mass of users. Transactional data is a very pervasive kind of information that is collected by several services, often involving huge pools of users. We propose txmeans, a parameter-free clustering algorithm able to efficiently partitioning transactional data in a completely automatic way. Txmeans is designed for the case where clustering must be applied on a massive number of different datasets, for instance when a large set of users need to be analyzed individually and each of them has generated a long history of transactions. A deep experimentation on both real and synthetic datasets shows the practical effectiveness of txmeans for the mass clustering of different personal datasets, and suggests that txmeans outperforms existing methods in terms of quality and efficiency. Finally, we present a personal cart assistant application based on txmeans1 aGuidotti, Riccardo1 aMonreale, Anna1 aNanni, Mirco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/clustering-individual-transactional-data-masses-users01645nas a2200169   4500008004100000022001400041245007300055210006900128300001700197490000600214520112800220100002501348700002101373700002301394700001901417856003901436       2017                        eng d  a2157-690400aA Data Mining Approach to Assess Privacy Risk in Human Mobility Data0 aData Mining Approach to Assess Privacy Risk in Human Mobility Da  a31:1–31:270 v93 aHuman mobility data are an important proxy to understand human mobility dynamics, develop analytical services, and design mathematical models for simulation and what-if analysis. Unfortunately mobility data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment provide data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be recomputed every time new data records become available and for every selection of individuals, geographic areas, or time windows. In this article, we propose a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.1 aPellungrini, Roberto1 aPappalardo, Luca1 aPratesi, Francesca1 aMonreale, Anna  uhttp://doi.acm.org/10.1145/310677402550nas a2200133   4500008004100000245005900041210005800100260002400158520208400182100001902266700002102285700002002306856009002326       2017                        eng d00aData Science a Game-changer for Science and Innovation0 aData Science a Gamechanger for Science and Innovation  bG7 Academyc03/20173 aDigital technology is ubiquitous and very much part of public and private organizations and of individuals’ lives. People and things are becoming increasingly interconnected. Smartphones, smart buildings, smart factories, smart cities, autonomous vehicles and other smart environments and devices are filled with digital sensors, all of them creating an abundance of data. Governance and health care collect, generate and use data in an unprecedented quantity. New high- throughput scientific instruments and methods, like telescopes, satellites, accelerators, supercomputers, sensor networks and gene sequencing methods as well as large scale simulations generate massive amounts of data. Often referred to as data deluge, or Big Data, massive datasets revolutionize the way research is carried out, resulting in the emergence of a new, fourth paradigm of science based on data-intensive computing and data driven discovery4. Accordingly, the path to the solution of the problem of sustainable development will lead through Big Data, as maintaining the whole complexity of our modern society, including communication and traffic services, manufacturing, trade and commerce, financial services, health security, science, education and policy making requires this novel approach.
The new availability of huge amounts of data, along with advanced tools of exploratory data analysis, data mining/machine learning, and data visualization, and scalable infrastructures, has produced a spectacular change in the scientific method: all this is Data Science. This paper describes the main issues around Data Science as it will play out in the coming years in science and society. It focus on the scientific, technical and ethical challenges (A), on its role for disruptive innovation for science, industry, policy and people (B), on its scientific, technological and educational challenges (C) and finally, on the quantitative expectations of its economic impact (D). In our work we could count on many reports and studies on the subject, particularly on the BDVA5 and ERCIM6 reports.1 aBeltram, Fabio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/data-science-game-changer-science-and-innovation01949nas a2200133   4500008004100000022001400041245007300055210006900128260000800197520152300205100002101728700002001749856004601769       2017                        eng d  a1573-756X00aData-driven generation of spatio-temporal routines in human mobility0 aDatadriven generation of spatiotemporal routines in human mobili  cDec3 aThe generation of realistic spatio-temporal trajectories of human mobility is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad-hoc networks or what-if analysis in urban ecosystems. Current generative algorithms fail in accurately reproducing the individuals' recurrent schedules and at the same time in accounting for the possibility that individuals may break the routine during periods of variable duration. In this article we present Ditras (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility. Ditras operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. We propose a data-driven algorithm which constructs a diary generator from real data, capturing the tendency of individuals to follow or break their routine. We also propose a trajectory generator based on the concept of preferential exploration and preferential return. We instantiate Ditras with the proposed diary and trajectory generators and compare the resulting algorithm with real data and synthetic data produced by other generative algorithms, built by instantiating Ditras with several combinations of diary and trajectory generators. We show that the proposed algorithm reproduces the statistical properties of real trajectories in the most accurate way, making a step forward the understanding of the origin of the spatio-temporal patterns of human mobility.1 aPappalardo, Luca1 aSimini, Filippo  uhttps://doi.org/10.1007/s10618-017-0548-401594nas a2200169   4500008004100000245007800041210006900119260001200188300000700200490000600207520108600213100002201299700002201321700001801343700002301361856004001384       2017                        eng d00aDiscovering and Understanding City Events with Big Data: The Case of Rome0 aDiscovering and Understanding City Events with Big Data The Case  c06/2017  a740 v83 aThe increasing availability of large amounts of data and digital footprints has given rise
to ambitious research challenges in many fields, which spans from medical research, financial and
commercial world, to people and environmental monitoring. Whereas traditional data sources and
census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge
providing useful and hidden information to analysts and decision makers. With this paper, we focus
on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study
and evaluate the impact of these events over the typical city dynamics. We present an analytical
process able to discover, understand and characterize city events from Call Detail Record, designing
a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users.
The methodology provides an useful tool for city mobility manager to manage the events and taking
future decisions on specific classes of users, i.e., residents, commuters and tourists.1 aFurletti, Barbara1 aTrasarti, Roberto1 aCintia, Paolo1 aGabrielli, Lorenzo  uhttps://doi.org/10.3390/info803007400483nas a2200121   4500008004100000245007100041210006900112260001300181100001900194700002200213700002100235856010500256       2017                        eng d00aDynamic community analysis in decentralized online social networks0 aDynamic community analysis in decentralized online social networ  bSpringer1 aGuidi, Barbara1 aMichienzi, Andrea1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/dynamic-community-analysis-decentralized-online-social-networks01602nas a2200133   4500008004100000245005600041210005600097520113100153100002301284700001901307700002501326700002401351856009301375       2017                        eng d00aEfficiently Clustering Very Large Attributed Graphs0 aEfficiently Clustering Very Large Attributed Graphs3 aAttributed graphs model real networks by enriching their nodes with attributes accounting for properties. Several techniques have been proposed for partitioning these graphs into clusters that are homogeneous with respect to both semantic attributes and to the structure of the graph. However, time and space complexities of state of the art algorithms limit their scalability to medium-sized graphs. We propose SToC (for Semantic-Topological Clustering), a fast and scalable algorithm for partitioning large attributed graphs. The approach is robust, being compatible both with categorical and with quantitative attributes, and it is tailorable, allowing the user to weight the semantic and topological components. Further, the approach does not require the user to guess in advance the number of clusters. SToC relies on well known approximation techniques such as bottom-k sketches, traditional graph-theoretic concepts, and a new perspective on the composition of heterogeneous distance measures. Experimental results demonstrate its ability to efficiently compute high-quality partitions of large scale attributed graphs.1 aBaroni, Alessandro1 aConte, Alessio1 aPatrignani, Maurizio1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/efficiently-clustering-very-large-attributed-graphs02220nas a2200145   4500008004100000245019400041210006900235300001600304490000700320520162400327100002001951700002001971700001901991856006402010       2017                        eng d00aAn empirical verification of a-priori learning models on mailing archives in the context of online learning activities of participants in free\libre open source software (FLOSS) communities0 aempirical verification of apriori learning models on mailing arc  a3207–32290 v223 aFree\Libre Open Source Software (FLOSS) environments are increasingly dubbed as learning environments where practical software engineering skills can be acquired. Numerous studies have extensively investigated how knowledge is acquired in these environments through a collaborative learning model that define a learning process. Such a learning process, identified either as a result of surveys or by means of questionnaires, can be depicted through a series of graphical representations indicating the steps FLOSS community members go through as they acquire and exchange skills. These representations are referred to as a-priori learning models. They are Petri net-like workflow nets (WF-net) that provide a visual representation of the learning process as it is expected to occur. These models are representations of a learning framework or paradigm in FLOSS communities. As such, the credibility of any models is estimated through a process of model verification and validation. Therefore in this paper, we analyze these models in comparison with the real behavior captured in FLOSS repositories by means of conformance verification in process mining. The purpose of our study is twofold. Firstly, the results of our analysis provide insights on the possible discrepancies that are observed between the initial theoretical representations of learning processes and the real behavior captured in FLOSS event logs, constructed from mailing archives. Secondly, this comparison helps foster the understanding on how learning actually takes place in FLOSS environments based on empirical evidence directly from the data.1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttps://link.springer.com/article/10.1007/s10639-017-9573-600831nas a2200097   4500008004100000245004000041210004000081520053400121100002400655856005400679       2017                        eng d00aEnumerating Distinct Decision Trees0 aEnumerating Distinct Decision Trees3 aThe search space for the feature selection problem in decision tree learning is the lattice of subsets of the available features. We provide an exact enumeration procedure of the subsets that lead to all and only the distinct decision trees. The procedure can be adopted to prune the search space of complete and heuristics search methods in wrapper models for feature selection. Based on this, we design a computational optimization of the sequential backward elimination heuristics with a performance improvement of up to 100X.1 aRuggieri, Salvatore  uhttp://proceedings.mlr.press/v70/ruggieri17a.html00434nas a2200109   4500008004100000245006600041210005900107260001900166100002300185700002000208856009600228       2017                        eng d00aOn the Equivalence Between Community Discovery and Clustering0 aEquivalence Between Community Discovery and Clustering  bSpringer, Cham1 aGuidotti, Riccardo1 aCoscia, Michele  uhttps://kdd.isti.cnr.it/publications/equivalence-between-community-discovery-and-clustering01046nas a2200169   4500008004100000245012100041210006900162260001300231300001400244520045100258100002000709700001700729700001900746700002400765700002100789856006600810       2017                        eng d00aExperimental Assessment of the Emergence of Awareness and Its Influence on Behavioral Changes: The Everyaware Lesson0 aExperimental Assessment of the Emergence of Awareness and Its In  bSpringer  a337–3623 aThe emergence of awareness is deeply connected to the process of learning. In fact, by learning that high sound levels may harm one’s health, that noise levels that we estimate as innocuous may be dangerous, that there exist an alternative path we can walk to go to work and minimize our exposure to air pollution, etc., citizens will be able to understand the environment around them and act consequently to go toward a more sustainable world.1 aGravino, Pietro1 aSirbu, Alina1 aBecker, Martin1 aServedio, Vito, D P1 aLoreto, Vittorio  uhttp://link.springer.com/chapter/10.1007/978-3-319-25658-0_1601483nas a2200145   4500008004100000020002200041245005900063210005900122520097800181100002501159700002101184700002301205700001901228856009001247       2017                        eng d  a978-3-319-66283-100aFast Estimation of Privacy Risk in Human Mobility Data0 aFast Estimation of Privacy Risk in Human Mobility Data3 aMobility data are an important proxy to understand the patterns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobility data are also very sensitive, since they may contain personal information about the individuals involved. Existing frameworks for privacy risk assessment enable the data providers to quantify and mitigate privacy risks, but they suffer two main limitations: (i) they have a high computational complexity; (ii) the privacy risk must be re-computed for each new set of individuals, geographic areas or time windows. In this paper we explore a fast and flexible solution to estimate privacy risk in human mobility data, using predictive models to capture the relation between an individual’s mobility patterns and her privacy risk. We show the effectiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods. 1 aPellungrini, Roberto1 aPappalardo, Luca1 aPratesi, Francesca1 aMonreale, Anna  uhttps://kdd.isti.cnr.it/publications/fast-estimation-privacy-risk-human-mobility-data01579nas a2200157   4500008004100000245007400041210006900115300001300184490000700197520103200204100002101236700001901257700002101276700002001297856010401317       2017                        eng d00aForecasting success via early adoptions analysis: A data-driven study0 aForecasting success via early adoptions analysis A datadriven st  ae01890960 v123 aInnovations are continuously launched over markets, such as new products over the retail market or new artists over the music scene. Some innovations become a success; others don’t. Forecasting which innovations will succeed at the beginning of their lifecycle is hard. In this paper, we provide a data-driven, large-scale account of the existence of a special niche among early adopters, individuals that consistently tend to adopt successful innovations before they reach success: we will call them Hit-Savvy. Hit-Savvy can be discovered in very different markets and retain over time their ability to anticipate the success of innovations. As our second contribution, we devise a predictive analytical process, exploiting Hit-Savvy as signals, which achieves high accuracy in the early-stage prediction of successful innovations, far beyond the reach of state-of-the-art time series forecasting models. Indeed, our findings and predictive model can be fruitfully used to support marketing strategies and product placement.1 aRossetti, Giulio1 aMilli, Letizia1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/forecasting-success-early-adoptions-analysis-data-driven-study01239nas a2200157   4500008004100000245008100041210006900122260001900191520069900210100002000909700002300929700002100952700002100973700002000994856006701014       2017                        eng d00aThe Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis0 aFractal Dimension of Music Geography Popularity and Sentiment An  bSpringer, Cham3 aNowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a “fractal” musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians’ popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.

1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_1901602nas a2200157   4500008004100000245007000041210006800111490000700179520110500186100002201291700001801313700002101331700002001352700002201372856005001394       2017                        eng d00aHyWare: a HYbrid Workflow lAnguage for Research E-infrastructures0 aHyWare a HYbrid Workflow lAnguage for Research Einfrastructures0 v233 aResearch e-infrastructures are "systems of systems", patchworks of tools, services and data sources, evolving over time to address the needs of the scientific process. Accordingly, in such environments, researchers implement their scientific processes by means of workflows made of a variety of actions, including for example usage of web services, download and execution of shared software libraries or tools, or local and manual manipulation of data. Although scientists may benefit from sharing their scientific process, the heterogeneity underpinning e-infrastructures hinders their ability to represent, share and eventually reproduce such workflows. This work presents HyWare, a language for representing scientific process in highly-heterogeneous e-infrastructures in terms of so-called hybrid workflows. HyWare lays in between "business process modeling languages", which offer a formal and high-level description of a reasoning, protocol, or procedure, and "workflow execution languages", which enable the fully automated execution of a sequence of computational steps via dedicated engines.1 aCandela, Leonardo1 aManghi, Paolo1 aGiannotti, Fosca1 aGrossi, Valerio1 aTrasarti, Roberto  uhttp://dx.doi.org/10.1045/january2017-candela01445nas a2200169   4500008004100000245003500041210003500076300000800111490001000119520096400129100001701093700001901110700002301129700002201152700002001174856008101194       2017                        eng d00aICON Loop Carpooling Show Case0 aICON Loop Carpooling Show Case  a3100 v101013 aIn this chapter we describe a proactive carpooling service that combines induction and optimization mechanisms to maximize the impact of carpooling within a community. The approach autonomously infers the mobility demand of the users through the analysis of their mobility traces (i.e. Data Mining of GPS trajectories) and builds the network of all possible ride sharing opportunities among the users. Then, the maximal set of carpooling matches that satisfy some standard requirements (maximal capacity of vehicles, etc.) is computed through Constraint Programming models, and the resulting matches are proactively proposed to the users. Finally, in order to maximize the expected impact of the service, the probability that each carpooling match is accepted by the users involved is inferred through Machine Learning mechanisms and put in the CP model. The whole process is reiterated at regular intervals, thus forming an instance of the general ICON loop.1 aNanni, Mirco1 aKotthoff, Lars1 aGuidotti, Riccardo1 aO'Sullivan, Barry1 aPedreschi, Dino  uhttps://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf#page=31401289nas a2200181   4500008004100000245004600041210004200087300000800129490001000137520075100147100001700898700002300915700002200938700002600960700002000986700002001006856008101026       2017                        eng d00aThe Inductive Constraint Programming Loop0 aInductive Constraint Programming Loop  a3030 v101013 aConstraint programming is used for a variety of real-world optimization problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, that we call the Inductive Constraint Programming (ICON) loop. In this approach data is gathered and analyzed systematically in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other end.1 aNanni, Mirco1 aNijssen, Siegfried1 aO'Sullivan, Barry1 aPaparrizou, Anastasia1 aPedreschi, Dino1 aSimonis, Helmut  uhttps://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf#page=30701359nas a2200205   4500008004100000245004600041210004200087520074100129100002400870700001800894700001500912700001900927700001700946700002300963700002200986700002601008700002001034700002001054856007901074       2017                        eng d00aThe Inductive Constraint Programming Loop0 aInductive Constraint Programming Loop3 aConstraint programming is used for a variety of real-world optimization problems, such as planning, scheduling and resource allocation problems. At the same time, one continuously gathers vast amounts of data about these problems. Current constraint programming software does not exploit such data to update schedules, resources and plans. We propose a new framework, which we call the inductive constraint programming loop. In this approach data is gathered and analyzed systematically in order to dynamically revise and adapt constraints and optimization criteria. Inductive Constraint Programming aims at bridging the gap between the areas of data mining and machine learning on the one hand, and constraint programming on the other.1 aBessiere, Christian1 aDe Raedt, Luc1 aGuns, Tias1 aKotthoff, Lars1 aNanni, Mirco1 aNijssen, Siegfried1 aO'Sullivan, Barry1 aPaparrizou, Anastasia1 aPedreschi, Dino1 aSimonis, Helmut  uhttps://kdd.isti.cnr.it/publications/inductive-constraint-programming-loop01474nas a2200145   4500008004100000245007600041210006900117260001300186520098100199100001901180700002101199700002001220700002101240856006701261       2017                        eng d00aInformation diffusion in complex networks: The active/passive conundrum0 aInformation diffusion in complex networks The activepassive conu  bSpringer3 aIdeas, information, viruses: all of them, with their mechanisms, can spread over the complex social tissues described by our interpersonal relations. Classical spreading models can agnostically from the object of which they simulate the diffusion, thus considering spreading of virus, ideas and innovations alike. Indeed, such simplification makes easier to define a standard set of tools that can be applied to heterogeneous contexts; however, it can also lead to biased, partial, simulation outcomes. In this work we discuss the concepts of active and passive diffusion: moving from analysis of a well-known passive model, the Threshold one, we introduce two novel approaches whose aim is to provide active and mixed schemas applicable in the context of innovations/ideas diffusion simulation. Our data-driven analysis shows how, in such context, the adoption of exclusively passive/active models leads to conflicting results, thus highlighting the need of mixed approaches.1 aMilli, Letizia1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/chapter/10.1007/978-3-319-72150-7_2501977nas a2200181   4500008004100000245007000041210006900111260001300180300001400193520139800207100002401605700002201629700002001651700002101671700001701692700002001709856006601729       2017                        eng d00aLarge Scale Engagement Through Web-Gaming and Social Computations0 aLarge Scale Engagement Through WebGaming and Social Computations  bSpringer  a237–2543 aIn the last few years the Web has progressively acquired the status of an infrastructure for social computation that allows researchers to coordinate the cognitive abilities of human agents, so to steer the collective user activity towards predefined goals. This general trend is also triggering the adoption of web-games as an alternative laboratory to run experiments in the social sciences and whenever the contribution of human beings can be effectively used for research purposes. Web-games introduce a playful aspect in scientific experiments with the result of increasing participation of people and of keeping their attention steady in time. The aim of this chapter is to suggest a general purpose web-based platform scheme for web-gaming and social computation. This platform will simplify the realization of web-games and will act as a repository of different scientific experiments, thus realizing a sort of showcase that stimulates users’ curiosity and helps researchers in recruiting volunteers. A platform built by following these criteria has been developed within the EveryAware project, the Experimental Tribe (XTribe) platform, which is operational and ready to be used. Finally, a sample web-game hosted by the XTribe platform will be presented with the aim of reporting the results, in terms of participation and motivation, of two different player recruiting strategies.1 aServedio, Vito, D P1 aCaminiti, Saverio1 aGravino, Pietro1 aLoreto, Vittorio1 aSirbu, Alina1 aTria, Francesca  uhttp://link.springer.com/chapter/10.1007/978-3-319-25658-0_1201536nas a2200157   4500008004100000245008700041210006900128260000900197520094200206100002301148700002101171700002101192700002101213700002001234856012401254       2017                        eng d00aMarket Basket Prediction using User-Centric Temporal Annotated Recurring Sequences0 aMarket Basket Prediction using UserCentric Temporal Annotated Re  bIEEE3 aNowadays, a hot challenge for supermarket chains is to offer personalized services to their customers. Market basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable of capturing at the same time the different factors influencing the customer’s decision process: co-occurrence, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern named Temporal Annotated Recurring Sequence (TARS). We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to understand the level of the customer’s stocks and recommend the set of most necessary items. A deep experimentation shows that TARS can explain the customers’ purchase behavior, and that TBP outperforms the state-of-the-art competitors.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aPappalardo, Luca1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/market-basket-prediction-using-user-centric-temporal-annotated-recurring-sequences00371nas a2200109   4500008004100000245005600041210005600097100001700153700002200170700002100192856004800213       2017                        eng d00aMovement Behaviour Recognition for Water Activities0 aMovement Behaviour Recognition for Water Activities1 aNanni, Mirco1 aTrasarti, Roberto1 aGiannotti, Fosca  uhttps://doi.org/10.1007/978-3-319-71970-2_701609nas a2200169   4500008004100000245005400041210005300095260001200148300001400160490000700174520108700181100002201268700002301290700001901313700002101332856008601353       2017                        eng d00aMyWay: Location prediction via mobility profiling0 aMyWay Location prediction via mobility profiling  c03/2017  a350–3670 v643 aForecasting the future positions of mobile users is a valuable task allowing us to operate efficiently a myriad of different applications which need this type of information. We propose MyWay, a prediction system which exploits the individual systematic behaviors modeled by mobility profiles to predict human movements. MyWay provides three strategies: the individual strategy uses only the user individual mobility profile, the collective strategy takes advantage of all users individual systematic behaviors, and the hybrid strategy that is a combination of the previous two. A key point is that MyWay only requires the sharing of individual mobility profiles, a concise representation of the user׳s movements, instead of raw trajectory data revealing the detailed movement of the users. We evaluate the prediction performances of our proposal by a deep experimentation on large real-world data. The results highlight that the synergy between the individual and collective knowledge is the key for a better prediction and allow the system to outperform the state-of-art methods.1 aTrasarti, Roberto1 aGuidotti, Riccardo1 aMonreale, Anna1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/myway-location-prediction-mobility-profiling01274nas a2200169   4500008004100000245009100041210006900132300001100201520065400212100002100866700001900887700001800906700001700924700002000941700002100961856012200982       2017                        eng d00aNDlib: a python library to model and analyze diffusion processes over complex networks0 aNDlib a python library to model and analyze diffusion processes   a1–193 aNowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground.To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.1 aRossetti, Giulio1 aMilli, Letizia1 aRinzivillo, S1 aSirbu, Alina1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/ndlib-python-library-model-and-analyze-diffusion-processes-over-complex-networks01119nas a2200169   4500008004100000245004700041210004600088260001000134520063100144100002100775700001900796700001800815700001700833700002000850700002100870856005800891       2017                        eng d00aNDlib: Studying Network Diffusion Dynamics0 aNDlib Studying Network Diffusion Dynamics  aTokyo3 aNowadays the analysis of diffusive phenomena occurring on top of complex networks represents a hot topic in the Social Network Analysis playground. In order to support students, teachers, developers and researchers in this work we introduce a novel simulation framework, ND LIB . ND LIB is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. Upon the diffusion library, we designed a simulation server that allows remote execution of experiments and an online visualization tool that abstract the programmatic interface and makes available the simulation platform to non-technicians.1 aRossetti, Giulio1 aMilli, Letizia1 aRinzivillo, S1 aSirbu, Alina1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://ieeexplore.ieee.org/abstract/document/825977401912nas a2200169   4500008004100000245006500041210006400106300001400170490000700184520135600191100002301547700001701570700001801587700002001605700002101625856009601646       2017                        eng d00aNever drive alone: Boosting carpooling with network analysis0 aNever drive alone Boosting carpooling with network analysis  a237–2570 v643 aCarpooling, i.e., the act where two or more travelers share the same car for a common trip, is one of the possibilities brought forward to reduce traffic and its externalities, but experience shows that it is difficult to boost the adoption of carpooling to significant levels. In our study, we analyze the potential impact of carpooling as a collective phenomenon emerging from people׳s mobility, by network analytics. Based on big mobility data from travelers in a given territory, we construct the network of potential carpooling, where nodes correspond to the users and links to possible shared trips, and analyze the structural and topological properties of this network, such as network communities and node ranking, to the purpose of highlighting the subpopulations with higher chances to create a carpooling community, and the propensity of users to be either drivers or passengers in a shared car. Our study is anchored to reality thanks to a large mobility dataset, consisting of the complete one-month-long GPS trajectories of approx. 10% circulating cars in Tuscany. We also analyze the aggregated outcome of carpooling by means of empirical simulations, showing how an assignment policy exploiting the network analytic concepts of communities and node rankings minimizes the number of single occupancy vehicles observed after carpooling.1 aGuidotti, Riccardo1 aNanni, Mirco1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/never-drive-alone-boosting-carpooling-network-analysis01626nas a2200145   4500008004100000245006300041210006300104520117000167100002301337700002101360700002101381700002101402700002001423856003701443       2017                        eng d00aNext Basket Prediction using Recurring Sequential Patterns0 aNext Basket Prediction using Recurring Sequential Patterns3 aNowadays, a hot challenge for supermarket chains is to offer personalized services for their customers. Next basket prediction, i.e., supplying the customer a shopping list for the next purchase according to her current needs, is one of these services. Current approaches are not capable to capture at the same time the different factors influencing the customer's decision process: co-occurrency, sequentuality, periodicity and recurrency of the purchased items. To this aim, we define a pattern Temporal Annotated Recurring Sequence (TARS) able to capture simultaneously and adaptively all these factors. We define the method to extract TARS and develop a predictor for next basket named TBP (TARS Based Predictor) that, on top of TARS, is able to to understand the level of the customer's stocks and recommend the set of most necessary items. By adopting the TBP the supermarket chains could crop tailored suggestions for each individual customer which in turn could effectively speed up their shopping sessions. A deep experimentation shows that TARS are able to explain the customer purchase behavior, and that TBP outperforms the state-of-the-art competitors.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aPappalardo, Luca1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://arxiv.org/abs/1702.0715801685nas a2200145   4500008004100000245008500041210006900126300001200195490000600207520118800213100002101401700002001422700002101442856007601463       2017                        eng d00aNode-centric Community Discovery: From static to dynamic social network analysis0 aNodecentric Community Discovery From static to dynamic social ne  a32–480 v33 aNowadays, online social networks represent privileged playgrounds that enable researchers to study, characterize and understand complex human behaviors. Social Network Analysis, commonly known as SNA, is the multidisciplinary field of research under which researchers of different backgrounds perform their studies: one of the hottest topics in such diversified context is indeed Community Discovery. Clustering individuals, whose relations are described by a networked structure, into homogeneous communities is a complex task required by several analytical processes. Moreover, due to the user-centric and dynamic nature of online social services, during the last decades, particular emphasis was dedicated to the definition of node-centric, overlapping and evolutive Community Discovery methodologies.
In this paper we provide a comprehensive and concise review of the main results, both algorithmic and analytical, we obtained in this field. Moreover, to better underline the rationale behind our research activity on Community Discovery, in this work we provide a synthetic review of the relevant literature, discussing not only methodological results but also analytical ones.1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://www.sciencedirect.com/science/article/abs/pii/S246869641730105202813nas a2200157   4500008004100000245006200041210006000103260001300163300001400176520231700190100001702507700002102524700002402545700002002569856006602589       2017                        eng d00aOpinion dynamics: models, extensions and external effects0 aOpinion dynamics models extensions and external effects  bSpringer  a363–4013 aRecently, social phenomena have received a lot of attention not only from social scientists, but also from physicists, mathematicians and computer scientists, in the emerging interdisciplinary field of complex system science. Opinion dynamics is one of the processes studied, since opinions are the drivers of human behaviour, and play a crucial role in many global challenges that our complex world and societies are facing: global financial crises, global pandemics, growth of cities, urbanisation and migration patterns, and last but not least important, climate change and environmental sustainability and protection. Opinion formation is a complex process affected by the interplay of different elements, including the individual predisposition, the influence of positive and negative peer interaction (social networks playing a crucial role in this respect), the information each individual is exposed to, and many others. Several models inspired from those in use in physics have been developed to encompass many of these elements, and to allow for the identification of the mechanisms involved in the opinion formation process and the understanding of their role, with the practical aim of simulating opinion formation and spreading under various conditions. These modelling schemes range from binary simple models such as the voter model, to multi-dimensional continuous approaches. Here, we provide a review of recent methods, focusing on models employing both peer interaction and external information, and emphasising the role that less studied mechanisms, such as disagreement, has in driving the opinion dynamics. Due to the important role that external information (mainly in the form of mass media broadcast) can have in enhancing awareness of social issues, a special emphasis will be devoted to study different forms it can take, investigating their effectiveness in driving the opinion formation at the population level. The review shows that, although a large number of approaches exist, some mechanisms such as the effect of multiple external information sources could largely benefit from further studies. Additionally, model validation with real data, which are starting to become available, is still largely lacking and should in our opinion be the main ambition of future investigations.1 aSirbu, Alina1 aLoreto, Vittorio1 aServedio, Vito, D P1 aTria, Francesca  uhttp://link.springer.com/chapter/10.1007/978-3-319-25658-0_1701373nas a2200145   4500008004100000245005000041210005000091260001300141520092300154100002301077700001901100700002101119700002001140856006701160       2017                        eng d00aPrivacy Preserving Multidimensional Profiling0 aPrivacy Preserving Multidimensional Profiling  bSpringer3 aRecently, big data had become central in the analysis of human behavior and the development of innovative services. In particular, a new class of services is emerging, taking advantage of different sources of data, in order to consider the multiple aspects of human beings. Unfortunately, these data can lead to re-identification problems and other privacy leaks, as diffusely reported in both scientific literature and media. The risk is even more pressing if multiple sources of data are linked together since a potential adversary could know information related to each dataset. For this reason, it is necessary to evaluate accurately and mitigate the individual privacy risk before releasing personal data. In this paper, we propose a methodology for the first task, i.e., assessing privacy risk, in a multidimensional scenario, defining some possible privacy attacks and simulating them using real-world datasets.1 aPratesi, Francesca1 aMonreale, Anna1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_1501507nas a2200121   4500008004100000245007100041210006900112300001200181520108400193100002101277700001801298856006901316       2017                        eng d00aQuantifying the relation between performance and success in soccer0 aQuantifying the relation between performance and success in socc  a17500143 aThe availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team’s position in a competition’s final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover, we find that, while victory and defeats can be explained by the team’s performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data and exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking which is similar to the actual ranking, suggesting that a complex systems’ view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.1 aPappalardo, Luca1 aCintia, Paolo  uhttp://www.worldscientific.com/doi/abs/10.1142/S021952591750014X01370nas a2200121   4500008004100000245008000041210006900121260001300190520093200203100002301135700002301158856006701181       2017                        eng d00aRecognizing Residents and Tourists with Retail Data Using Shopping Profiles0 aRecognizing Residents and Tourists with Retail Data Using Shoppi  bSpringer3 aThe huge quantity of personal data stored by service providers registering customers daily life enables the analysis of individual fingerprints characterizing the customers’ behavioral profiles. We propose
a framework for recognizing residents, tourists and occasional shoppers
among the customers of a retail market chain. We employ our recognition framework on a real massive dataset containing the shopping transactions of more than one million of customers, and we identify representative temporal shopping profiles for residents, tourists and occasional
customers. Our experiments show that even though residents are about
33% of the customers they are responsible for more than 90% of the expenditure. We statistically validate the number of residents and tourists
with national official statistics enabling in this way the adoption of our
recognition framework for the development of novel services and analysis.1 aGuidotti, Riccardo1 aGabrielli, Lorenzo  uhttps://link.springer.com/chapter/10.1007/978-3-319-76111-4_3500594nas a2200181   4500008004100000245009200041210006900133300001400202490000600216100002200222700002300244700002000267700002300287700002200310700001700332700001700349856004600366       2017                        eng d00aScalable and flexible clustering solutions for mobile phone-based population indicators0 aScalable and flexible clustering solutions for mobile phonebased  a285–2990 v41 aLulli, Alessandro1 aGabrielli, Lorenzo1 aDazzi, Patrizio1 aDell'Amico, Matteo1 aMichiardi, Pietro1 aNanni, Mirco1 aRicci, Laura  uhttps://doi.org/10.1007/s41060-017-0065-y01350nas a2200133   4500008004100000022001400041245005900055210005900114260000800173520094200181100002301123700002401146856004601170       2017                        eng d  a1573-767500aSegregation discovery in a social network of companies0 aSegregation discovery in a social network of companies  cSep3 aWe introduce a framework for the data-driven analysis of social segregation of minority groups, and challenge it on a complex scenario. The framework builds on quantitative measures of segregation, called segregation indexes, proposed in the social science literature. The segregation discovery problem is introduced, which consists of searching sub-groups of population and minorities for which a segregation index is above a minimum threshold. A search algorithm is devised that solves the segregation problem by computing a multi-dimensional data cube that can be explored by the analyst. The machinery underlying the search algorithm relies on frequent itemset mining concepts and tools. The framework is challenged on a cases study in the context of company networks. We analyse segregation on the grounds of sex and age for directors in the boards of the Italian companies. The network includes 2.15M companies and 3.63M directors.1 aBaroni, Alessandro1 aRuggieri, Salvatore  uhttps://doi.org/10.1007/s10844-017-0485-001783nas a2200169   4500008004100000245009100041210006900132260001300201520120400214100002001418700001701438700002101455700002001476700002201496700002901518856006601547       2017                        eng d00aSentiment Spreading: An Epidemic Model for Lexicon-Based Sentiment Analysis on Twitter0 aSentiment Spreading An Epidemic Model for LexiconBased Sentiment  bSpringer3 aWhile sentiment analysis has received significant attention in the last years, problems still exist when tools need to be applied to microblogging content. This because, typically, the text to be analysed consists of very short messages lacking in structure and semantic context. At the same time, the amount of text produced by online platforms is enormous. So, one needs simple, fast and effective methods in order to be able to efficiently study sentiment in these data. Lexicon-based methods, which use a predefined dictionary of terms tagged with sentiment valences to evaluate sentiment in longer sentences, can be a valid approach. Here we present a method based on epidemic spreading to automatically extend the dictionary used in lexicon-based sentiment analysis, starting from a reduced dictionary and large amounts of Twitter data. The resulting dictionary is shown to contain valences that correlate well with human-annotated sentiment, and to produce tweet sentiment classifications comparable to the original dictionary, with the advantage of being able to tag more tweets than the original. The method is easily extensible to various languages and applicable to large amounts of data.1 aPollacci, Laura1 aSirbu, Alina1 aGiannotti, Fosca1 aPedreschi, Dino1 aLucchese, Claudio1 aMuntean, Cristina, Ioana  uhttps://link.springer.com/chapter/10.1007/978-3-319-70169-1_901187nas a2200145   4500008004100000245004700041210004700088300001400135490000700149520075000156100002000906700001800926700001900944856007800963       2017                        eng d00aSurvey on using constraints in data mining0 aSurvey on using constraints in data mining  a424–4640 v313 aThis paper provides an overview of the current state-of-the-art on using constraints in knowledge discovery and data mining. The use of constraints in a data mining task requires specific definition and satisfaction tools during knowledge extraction. This survey proposes three groups of studies based on classification, clustering and pattern mining, whether the constraints are on the data, the models or the measures, respectively. We consider the distinctions between hard and soft constraint satisfaction, and between the knowledge extraction phases where constraints are considered. In addition to discussing how constraints can be used in data mining, we show how constraint-based languages can be used throughout the data mining process.1 aGrossi, Valerio1 aRomei, Andrea1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/survey-using-constraints-data-mining00584nas a2200145   4500008004100000245009100041210006900132260001600201100002300217700002200240700001700262700002100279700002000300856011800320       2017                        eng d00aThere's A Path For Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas0 aTheres A Path For Everyone A DataDriven Personal Model Reproduci  aTokyobIEEE1 aGuidotti, Riccardo1 aTrasarti, Roberto1 aNanni, Mirco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/theres-path-everyone-data-driven-personal-model-reproducing-mobility-agendas01804nas a2200157   4500008004100000245008200041210006900123300001600192490000800208520128300216100002101499700002101520700002001541700002101561856006401582       2017                        eng d00aTiles: an online algorithm for community discovery in dynamic social networks0 aTiles an online algorithm for community discovery in dynamic soc  a1213–12410 v1063 aCommunity discovery has emerged during the last decade as one of the most challenging problems in social network analysis. Many algorithms have been proposed to find communities on static networks, i.e. networks which do not change in time. However, social networks are dynamic realities (e.g. call graphs, online social networks): in such scenarios static community discovery fails to identify a partition of the graph that is semantically consistent with the temporal information expressed by the data. In this work we propose Tiles, an algorithm that extracts overlapping communities and tracks their evolution in time following an online iterative procedure. Our algorithm operates following a domino effect strategy, dynamically recomputing nodes community memberships whenever a new interaction takes place. We compare Tiles with state-of-the-art community detection algorithms on both synthetic and real world networks having annotated community structure: our experiments show that the proposed approach is able to guarantee lower execution times and better correspondence with the ground truth communities than its competitors. Moreover, we illustrate the specifics of the proposed approach by discussing the properties of identified communities it is able to identify.1 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://link.springer.com/article/10.1007/s10994-016-5582-801069nas a2200133   4500008004100000245014000041210006900181520047400250100002100724700001900745700002200764700002000786856012900806       2016                        eng d00aAdvances in Network Science: 12th International Conference and School, NetSci-X 2016, Wroclaw, Poland, January 11-13, 2016, Proceedings0 aAdvances in Network Science 12th International Conference and Sc3 aThis book constitutes the refereed proceedings of the 12th International Conference and  School of Network Science, NetSci-X 2016, held in Wroclaw, Poland, in January 2016. The 12 full and 6 short papers were carefully reviewed and selected from 59 submissions. The papers deal with the study of network models in domains ranging from biology and physics to computer science, from financial markets to cultural integration, and from social media to infectious diseases.1 aWierzbicki, Adam1 aBrandes, Ulrik1 aSchweitzer, Frank1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/advances-network-science-12th-international-conference-and-school-netsci-x-2016-wroclaw01717nas a2200181   4500008004100000245007400041210006900115300001200184490000600196520110000202100002101302700002101323700002301344700002201367700002001389700002101409856010501430       2016                        eng d00aAn analytical framework to nowcast well-being using mobile phone data0 aanalytical framework to nowcast wellbeing using mobile phone dat  a75–920 v23 aAn intriguing open question is whether measurements derived from Big Data recording human activities can yield high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users’ trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly “nowcast” the well-being and the socio-economic development of a territory.1 aPappalardo, Luca1 aVanhoof, Maarten1 aGabrielli, Lorenzo1 aSmoreda, Zbigniew1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/analytical-framework-nowcast-well-being-using-mobile-phone-data01172nas a2200133   4500008004100000245009500041210006900136260002800205520061000233100002000843700002300863700002100886856013100907       2016                        eng d00a“Are we playing like Music-Stars?” Placing Emerging Artists on the Italian Music Scene0 aAre we playing like MusicStars Placing Emerging Artists on the I  aRiva del Gardac09/20163 aThe Italian emerging bands chase success on the footprint of popular artists by playing rhythmic danceable and happy songs. Our finding comes out from a deep study of the Italian music scene and how the new generation ofmusicians relate with the tradition of their country. By analyzing Spotify data we investigated the peculiarity of regional mu- sic and we placed emerging bands within the musical movements defined by already successful artists. The approach proposed and the results ob- tained are a first attempt to outline some rules suggesting how to reach the success in the musical Italian scene.1 aPollacci, Laura1 aGuidotti, Riccardo1 aRossetti, Giulio  uhttps://kdd.isti.cnr.it/publications/%E2%80%9Care-we-playing-music-stars%E2%80%9D-placing-emerging-artists-italian-music-scene01416nas a2200133   4500008004100000245001900041210001900060260001300079520107000092100002301162700002101185700002001206856005601226       2016                        eng d00aAudio Ergo Sum0 aAudio Ergo Sum  bSpringer3 aNobody can state “Rock is my favorite genre” or “David Bowie is my favorite artist”. We defined a Personal Listening Data Model able to capture musical preferences through indicators and patterns, and we discovered that we are all characterized by a limited set of musical preferences, but not by a unique predilection. The empowered capacity of mobile devices and their growing adoption in our everyday life is generating an enormous increment in the production of personal data such as calls, positioning, online purchases and even music listening. Musical listening is a type of data that has started receiving more attention from the scientific community as consequence of the increasing availability of rich and punctual online data sources. Starting from the listening of 30k Last.Fm users, we show how the employment of the Personal Listening Data Models can provide higher levels of self-awareness. In addition, the proposed model will enable the development of a wide range of analysis and musical services both at personal and at collective level.1 aGuidotti, Riccardo1 aRossetti, Giulio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/audio-ergo-sum01766nas a2200169   4500008004100000020001800041245007400059210006900133260005500202520117100257100002201428700001901450700002201469700001701491700002401508856006401532       2016                        eng d  a978889635488900aBig Data and Public Administration: a case study for Tuscany Airports0 aBig Data and Public Administration a case study for Tuscany Airp  aUgento, Lecce (Italy)bMatematicamente.itc06/20163 aIn the last decade, the fast development of Information and Communication Technologies led to the wide diffusion of sensors able to track various aspects of human activity, as well as the storage and computational capabilities needed to record and analyze them. The so-called Big Data promise to improve the effectiveness of businesses, the quality of urban life, as well as many other fields, including the functioning of public administrations. Yet, translating the wealth of potential information hidden in Big Data to consumable intelligence seems to be still a difficult task, with a limited basis of success stories. This paper reports a project activity centered on a public administration  - IRPET, the Regional Institute for Economic Planning of Tuscany (Italy). The paper deals, among other topics, with human mobility and public transportation at a regional scale, summarizing the open questions posed by the Public Administration (PA), the envisioned role that Big Data might have in answering them, the actual challenges that emerged in trying to implement them, and finally the results we obtained, the limitations that emerged and the lessons learned.1 aFurletti, Barbara1 aFadda, Daniele1 aPiccini, Leonardo1 aNanni, Mirco1 aLattarulo, Patrizia  uhttp://sebd2016.unisalento.it/grid/SEBD2016-proceedings.pdf01199nas a2200289   4500008004100000022004100041050001400082245004600096210004500142260001200187300000800199490000600207520037500213100002300588700002200611700002300633700002200656700002100678700002000699700002400719700001900743700002000762700002000782700002100802700002400823856006200847       2016                        eng d  aprint: 2095-8099 / online: 2096-0026  a10-1244/N00aBig Data Research in Italy: A Perspective0 aBig Data Research in Italy A Perspective  c06/2016  a1630 v23 aThe aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains.1 aBergamaschi, Sonia1 aCarlini, Emanuele1 aCeci, Michelangelo1 aFurletti, Barbara1 aGiannotti, Fosca1 aMalerba, Donato1 aMezzanzanica, Mario1 aMonreale, Anna1 aPasi, Gabriella1 aPedreschi, Dino1 aPerego, Raffaele1 aRuggieri, Salvatore  uhttp://engineering.org.cn/EN/abstract/article_12288.shtml01704nas a2200133   4500008004100000245007000041210006900111520127400180100001901454700002001473700001601493700002401509856003701533       2016                        eng d00aCausal Discrimination Discovery Through Propensity Score Analysis0 aCausal Discrimination Discovery Through Propensity Score Analysi3 aSocial discrimination is considered illegal and unethical in the modern world. Such discrimination is often implicit in observed decisions' datasets, and anti-discrimination organizations seek to discover cases of discrimination and to understand the reasons behind them. Previous work in this direction adopted simple observational data analysis; however, this can produce biased results due to the effect of confounding variables. In this paper, we propose a causal discrimination discovery and understanding approach based on propensity score analysis. The propensity score is an effective statistical tool for filtering out the effect of confounding variables. We employ propensity score weighting to balance the distribution of individuals from protected and unprotected groups w.r.t. the confounding variables. For each individual in the dataset, we quantify its causal discrimination or favoritism with a neighborhood-based measure calculated on the balanced distributions. Subsequently, the causal discrimination/favoritism patterns are understood by learning a regression tree. Our approach avoids common pitfalls in observational data analysis and make its results legally admissible. We demonstrate the results of our approach on two discrimination datasets.1 aQureshi, Bilal1 aKamiran, Faisal1 aKarim, Asim1 aRuggieri, Salvatore  uhttps://arxiv.org/abs/1608.0373501430nas a2200133   4500008004100000245008200041210006900123260000900192520091700201100002301118700002401141700001901165856011201184       2016                        eng d00aClassification Rule Mining Supported by Ontology for Discrimination Discovery0 aClassification Rule Mining Supported by Ontology for Discriminat  bIEEE3 aDiscrimination discovery from data consists of designing data mining methods for the actual discovery of discriminatory situations and practices hidden in a large amount of historical decision records. Approaches based on classification rule mining consider items at a flat concept level, with no exploitation of background knowledge on the hierarchical and inter-relational structure of domains. On the other hand, ontologies are a widespread and ever increasing means for expressing such a knowledge. In this paper, we propose a framework for discrimination discovery from ontologies, where contexts of prima-facie evidence of discrimination are summarized in the form of generalized classification rules at different levels of abstraction. Throughout the paper, we adopt a motivating and intriguing case study based on discriminatory tariffs applied by the U. S. Harmonized Tariff Schedules on imported goods.1 aLuong, Binh, Thanh1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/classification-rule-mining-supported-ontology-discrimination-discovery01549nas a2200157   4500008004100000245009100041210006900132520094400201100002401145700001801169700001901187700002301206700002201229700002001251856012001271       2016                        eng d00aData Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach.0 aData Mining and Constraint Programming Foundations of a CrossDis3 aA successful integration of constraint programming and data mining has the potential to lead to a new ICT paradigm with far reaching implications. It could change the face of data mining and machine learning, as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge.
This book reports on some key results obtained on this integrated and cross- disciplinary approach within the European FP7 FET Open project no. 284715 on “Inductive Constraint Programming” and a number of associated workshops and Dagstuhl seminars. The book is structured in five parts: background; learning to model; learning to solve; constraint programming for data mining; and showcases. 1 aBessiere, Christian1 aDe Raedt, Luc1 aKotthoff, Lars1 aNijssen, Siegfried1 aO'Sullivan, Barry1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/data-mining-and-constraint-programming-foundations-cross-disciplinary-approach00937nas a2200145   4500008004100000245004500041210004400086260003800130300001200168520047400180100002000654700002000674700001900694856007800713       2016                        eng d00aData Mining and Constraints: An Overview0 aData Mining and Constraints An Overview  bSpringer International Publishing  a25–483 aThis paper provides an overview of the current state-of-the-art on using constraints in knowledge discovery and data mining. The use of constraints requires mechanisms for defining and evaluating them during the knowledge extraction process. We give a structured account of three main groups of constraints based on the specific context in which they are defined and used. The aim is to provide a complete view on constraints as a building block of data mining methods.1 aGrossi, Valerio1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/data-mining-and-constraints-overview01899nas a2200169   4500008004100000245007000041210006900111300001700180490000600197520138900203100001701592700002201609700001901631700002001650700002001670856003901690       2016                        eng d00aDriving Profiles Computation and Monitoring for Car Insurance CRM0 aDriving Profiles Computation and Monitoring for Car Insurance CR  a14:1–14:260 v83 aCustomer segmentation is one of the most traditional and valued tasks in customer relationship management (CRM). In this article, we explore the problem in the context of the car insurance industry, where the mobility behavior of customers plays a key role: Different mobility needs, driving habits, and skills imply also different requirements (level of coverage provided by the insurance) and risks (of accidents). In the present work, we describe a methodology to extract several indicators describing the driving profile of customers, and we provide a clustering-oriented instantiation of the segmentation problem based on such indicators. Then, we consider the availability of a continuous flow of fresh mobility data sent by the circulating vehicles, aiming at keeping our segments constantly up to date. We tackle a major scalability issue that emerges in this context when the number of customers is large-namely, the communication bottleneck-by proposing and implementing a sophisticated distributed monitoring solution that reduces communications between vehicles and company servers to the essential. We validate the framework on a large database of real mobility data coming from GPS devices on private cars. Finally, we analyze the privacy risks that the proposed approach might involve for the users, providing and evaluating a countermeasure based on data perturbation.1 aNanni, Mirco1 aTrasarti, Roberto1 aMonreale, Anna1 aGrossi, Valerio1 aPedreschi, Dino  uhttp://doi.acm.org/10.1145/291214801694nas a2200157   4500008004100000245006800041210006700109260003800176300001200214520112100226100002301347700002001370700002001390700002401410856010201434       2016                        eng d00aGoing Beyond GDP to Nowcast Well-Being Using Retail Market Data0 aGoing Beyond GDP to Nowcast WellBeing Using Retail Market Data  bSpringer International Publishing  a29–423 aOne of the most used measures of the economic health of a nation is the Gross Domestic Product (GDP): the market value of all officially recognized final goods and services produced within a country in a given period of time. GDP, prosperity and well-being of the citizens of a country have been shown to be highly correlated. However, GDP is an imperfect measure in many respects. GDP usually takes a lot of time to be estimated and arguably the well-being of the people is not quantifiable simply by the market value of the products available to them. In this paper we use a quantification of the average sophistication of satisfied needs of a population as an alternative to GDP. We show that this quantification can be calculated more easily than GDP and it is a very promising predictor of the GDP value, anticipating its estimation by six months. The measure is arguably a more multifaceted evaluation of the well-being of the population, as it tells us more about how people are satisfying their needs. Our study is based on a large dataset of retail micro transactions happening across the Italian territory.1 aGuidotti, Riccardo1 aCoscia, Michele1 aPedreschi, Dino1 aPennacchioli, Diego  uhttps://kdd.isti.cnr.it/publications/going-beyond-gdp-nowcast-well-being-using-retail-market-data01559nas a2200181   4500008004100000245009300041210006900134300000800203490000600211520091800217100002101135700002101156700001701177700002001194700002101214700001801235856012401253       2016                        eng d00aHomophilic network decomposition: a community-centric analysis of online social services0 aHomophilic network decomposition a communitycentric analysis of   a1030 v63 aIn this paper we formulate the homophilic network decomposition problem: Is it possible to identify a network partition whose structure is able to characterize the degree of homophily of its nodes? The aim of our work is to understand the relations between the homophily of individuals and the topological features expressed by specific network substructures. We apply several community detection algorithms on three large-scale online social networks—Skype, LastFM and Google+—and advocate the need of identifying the right algorithm for each specific network in order to extract a homophilic network decomposition. Our results show clear relations between the topological features of communities and the degree of homophily of their nodes in three online social scenarios: product engagement in the Skype network, number of listened songs on LastFM and homogeneous level of education among users of Google+.1 aRossetti, Giulio1 aPappalardo, Luca1 aKikas, Riivo1 aPedreschi, Dino1 aGiannotti, Fosca1 aDumas, Marlon  uhttps://kdd.isti.cnr.it/publications/homophilic-network-decomposition-community-centric-analysis-online-social-services00753nas a2200121   4500008004100000245004700041210004500088260003800133520033900171100002400510700001900534856007800553       2016                        eng d00aA KDD process for discrimination discovery0 aKDD process for discrimination discovery  bSpringer International Publishing3 aThe acceptance of analytical methods for discrimination discovery by practitioners and legal scholars can be only achieved if the data mining and machine learning communities will be able to provide case studies, methodological refinements, and the consolidation of a KDD process. We summarize here an approach along these directions.1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/kdd-process-discrimination-discovery01443nas a2200133   4500008004100000245008000041210006900121260003500190520094100225100002101166700002101187700001801208856008301226       2016                        eng d00aA novel approach to evaluate community detection algorithms on ground truth0 anovel approach to evaluate community detection algorithms on gro  aDijon, FrancebSpringer-Verlag3 aEvaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating the adherence of a community partition to the ground truth: our methodology provides more information than the state-of-the-art ones and is fast to compute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.1 aRossetti, Giulio1 aPappalardo, Luca1 aRinzivillo, S  uhttp://www.giuliorossetti.net/about/wp-content/uploads/2015/12/Complenet16.pdf01341nas a2200169   4500008004100000245006100041210006000102260003800162300001400200520081200214100002001026700001501046700001901061700001701080700002301097856005101120       2016                        eng d00aPartition-Based Clustering Using Constraint Optimization0 aPartitionBased Clustering Using Constraint Optimization  bSpringer International Publishing  a282–2993 aPartition-based clustering is the task of partitioning a dataset in a number of groups of examples, such that examples in each group are similar to each other. Many criteria for what constitutes a good clustering have been identified in the literature; furthermore, the use of additional constraints to find more useful clusterings has been proposed. In this chapter, it will be shown that most of these clustering tasks can be formalized using optimization criteria and constraints. We demonstrate how a range of clustering tasks can be modelled in generic constraint programming languages with these constraints and optimization criteria. Using the constraint-based modeling approach we also relate the DBSCAN method for density-based clustering to the label propagation technique for community discovery.1 aGrossi, Valerio1 aGuns, Tias1 aMonreale, Anna1 aNanni, Mirco1 aNijssen, Siegfried  uhttp://dx.doi.org/10.1007/978-3-319-50137-6_1101568nas a2200133   4500008004100000245008400041210006900125260003600194490001400230520111700244100001701361700002001378856003601398       2016                        eng d00aPower Consumption Modeling and Prediction in a Hybrid CPU-GPU-MIC Supercomputer0 aPower Consumption Modeling and Prediction in a Hybrid CPUGPUMIC   aGrenoble, FrancebSpringer LNCS0 vLNCS 98333 aPower consumption is a major obstacle for High Performance Computing (HPC) systems in their quest towards the holy grail of ExaFLOP performance. Significant advances in power efficiency have to be made before this goal can be attained and accurate modeling is an essential step towards power efficiency by optimizing system operating parameters to match dynamic energy needs. In this paper we present a study of power consumption by jobs in Eurora, a hybrid CPU-GPU-MIC system installed at the largest Italian data center. Using data from a dedicated monitoring framework, we build a data-driven model of power consumption for each user in the system and use it to predict the power requirements of future jobs. We are able to achieve good prediction results for over 80 % of the users in the system. For the remaining users, we identify possible reasons why prediction performance is not as good. Possible applications for our predictive modeling results include scheduling optimization, power-aware billing and system-scale power modeling. All the scripts used for the study have been made available on GitHub.1 aSirbu, Alina1 aBabaoglu, Ozalp  uhttp://arxiv.org/abs/1601.0596101787nas a2200121   4500008004100000245006100041210006000102260003800162520137900200100001701579700002001596856004901616       2016                        eng d00aPredicting System-level Power for a Hybrid Supercomputer0 aPredicting Systemlevel Power for a Hybrid Supercomputer  aInnsbruck, AustriabIEEEc07/20163 aFor current High Performance Computing systems to scale towards the holy grail of ExaFLOP performance, their power consumption has to be reduced by at least one order of magnitude. This goal can be achieved only through a combination of hardware and software advances. Being able to model and accurately predict the power consumption of large computational systems is necessary for software-level innovations such as proactive and power-aware scheduling, resource allocation and fault tolerance techniques. In this paper we present a 2-layer model of power consumption for a hybrid supercomputer (which held the top spot of the Green500 list on July 2013) that combines CPU, GPU and MIC technologies to achieve higher energy efficiency. Our model takes as input workload information - the number and location of resources that are used by each job at a certain time - and calculates the resulting system-level power consumption. When jobs are submitted to the system, the workload configuration can be foreseen based on the scheduler policies, and our model can then be applied to predict the ensuing system-level power consumption. Additionally, alternative workload configurations can be evaluated from a power perspective and more efficient ones can be selected. Applications of the model include not only power-aware scheduling but also prediction of anomalous behavior.1 aSirbu, Alina1 aBabaoglu, Ozalp  uhttp://ieeexplore.ieee.org/document/7568420/01227nas a2200121   4500008004100000245005000041210004900091260004500140520083300185100001901018700002101037856004701058       2016                        eng d00aPrivacy-Preserving Outsourcing of Data Mining0 aPrivacyPreserving Outsourcing of Data Mining  a Atlanta, GA, USAbIEEE Computer Society3 aData mining is gaining momentum in society due to the ever increasing availability of large amounts of data, easily gathered by a variety of collection technologies and stored via computer systems. Due to the limited computational resources of data owners and the developments in cloud computing, there has been considerable recent interest in the paradigm of data mining-as-a-service (DMaaS). In this paradigm, a company (data owner) lacking in expertise or computational resources outsources its mining needs to a third party service provider (server). Given the fact that the server may not be fully trusted, one of the main concerns of the DMaaS paradigm is the protection of data privacy. In this paper, we provide an overview of a variety of techniques and approaches that address the privacy issues of the DMaaS paradigm.1 aMonreale, Anna1 aWang, Hui, Wendy  uhttp://dx.doi.org/10.1109/COMPSAC.2016.16901567nas a2200145   4500008004100000245010400041210006900145260000900214520098000223100002501203700001901228700002301247700002301270856012801293       2016                        eng d00aPrivacy-Preserving Outsourcing of Pattern Mining of Event-Log Data-A Use-Case from Process Industry0 aPrivacyPreserving Outsourcing of Pattern Mining of EventLog Data  bIEEE3 aWith the advent of cloud computing and its model for IT services based on the Internet and big data centers, the interest of industries into XaaS ("Anything as a Service") paradigm is increasing. Business intelligence and knowledge discovery services are typical services that companies tend to externalize on the cloud, due to their data intensive nature and the algorithms complexity. What is appealing for a company is to rely on external expertise and infrastructure to compute the analytical results and models which are required by the business analysts for understanding the business phenomena under observation. Although it is advantageous to achieve sophisticated analysis there exist several serious privacy issues in this paradigm. In this paper we investigate through an industrial use-case the application of a framework for privacypreserving outsourcing of pattern mining on event-log data. Moreover, we present and discuss some ideas about possible extensions.1 aMarrella, Alessandro1 aMonreale, Anna1 aKloepper, Benjamin1 aKrueger, Martin, W  uhttps://kdd.isti.cnr.it/publications/privacy-preserving-outsourcing-pattern-mining-event-log-data-use-case-process-industry01225nam a2200217   4500008004100000020002200041245004600063210004600109520061000155100001600765700002300781700001800804700002500822700001900847700001700866700002300883700002100906700002300927700002000950856003700970       2016                        eng d  a978-92-79-61762-100aRealising the European open science cloud0 aRealising the European open science cloud3 aThe European Open Science Cloud (EOSC) aims to accelerate and support the current transition to more effective Open Science and Open Innovation in the Digital Single Market. It should enable trusted access to services, systems and the re-use of shared scientific data across disciplinary, social and geographical borders. This report approaches the EOSC as a federated environment for scientific data sharing and re-use, based on existing and emerging elements in the Member States, with light-weight international guidance and governance, and a large degree of freedom regarding practical implementation.1 aAyris, Paul1 aBerthou, Jean-Yves1 aBruce, Rachel1 aLindstaedt, Stefanie1 aMonreale, Anna1 aMons, Barend1 aMurayama, Yasuhiro1 aSödergård, Caj1 aTochtermann, Klaus1 aWilkinson, Ross  uhttp://dx.doi.org/10.2777/94015400344nas a2200109   4500008004100000245003600041210003600077260000900113100001800122700002100140856007300161       2016                        eng d00aSPARQL Queries over Source Code0 aSPARQL Queries over Source Code  bIEEE1 aSetzu, Mattia1 aAtzori, Maurizio  uhttps://kdd.isti.cnr.it/publications/sparql-queries-over-source-code03432nas a2200169   4500008004100000245004600041210004600087300001000133490000700143520296100150100001703111700001903128700002203147700002003169700002203189856005103211       2016                        eng d00aSpecial Issue on Mobile Traffic Analytics0 aSpecial Issue on Mobile Traffic Analytics  a1–20 v953 aThis Special Issue of Computer Communications is dedicated to mobile traffic data analysis. This is an emerging field of research that stems from the increasing pervasiveness in our lives of always-connected mobile devices. These devices continuously collect, generate, receive or communicate data; in doing so, they leave trails of digital crumbs that can be followed, recorded and analysed in many and varied ways, and for a number of different purposes.
From a data collection perspective, applications running on smartphones allow tracking user activities with extreme accuracy, in terms of mobility, context, and service usage. Yet, having individuals informedly install and run software that monitors their actions is not obvious; finding adequate incentives is equivalently complex. The other option is gathering mobile traffic data in the mobile network. This is an increasingly common practice for telecommunication operators: the collection of minimum information required for billing is giving way to in-depth inspection and recording of mobile service usages in space and time, and of traffic flows at the network edge and core. In this case, data access remains the major impediment, due to privacy and industrial secrecy reasons.

Despite the issues inherent to the data collection, the richness of knowledge that can be extracted from the aforementioned sources is such that actors in both academia and industry are putting significant effort in gathering, analysing and possibly making available mobile traffic data. Indeed, mobile traffic data typically contain information on large populations of individuals (from thousands to millions users) with high spatio-temporal granularity. The combination of accuracy and coverage is unprecedented, and it has proven key in validating theories and scaling up experimental studies in a number of research fields across many disciplines, including physics, sociology, epidemiology, transportation systems, and, of course, mobile networking.

As a result, we witness today a rapid growth of the literature that proposes or exploits mobile traffic analytics. Included in this Special Issue are eight papers that cover a significant portion of the different research topics in this area, ranging from data collection to the characterization of land use and mobile service consumption, from the inference and prediction of user mobility to the detection of malicious traffic. These papers were selected from 30 high-quality submissions after at least two rounds of reviews by experts and guest editors. The original submissions were received from five continents and a variety of countries, including Austria, Argentina, Belgium, Brazil, Chile, China, France, Germany, Italy, South Korea, Luxembourg, Pakistan, Saudi Arabia, Spain, Sweden, Tunisia, Turkey, USA. The accepted papers reflect this geographical heterogeneity, and are authored by researchers based in Europe, North and South America.1 aFiore, Marco1 aShafiq, Zubair1 aSmoreda, Zbigniew1 aStanica, Razvan1 aTrasarti, Roberto  uhttp://dx.doi.org/10.1016/j.comcom.2016.10.00901970nas a2200193   4500008004100000022001400041245010300055210006900158260001200227300000700239490000600246520137200252100002101624700002301645700001901668700002001687700002101707856004801728       2016                        eng d  a1869-546900aA supervised approach for intra-/inter-community interaction prediction in dynamic social networks0 asupervised approach for intraintercommunity interaction predicti  c09/2016  a860 v63 aDue to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital footprints, that describe a complex, rapidly evolving, network structures. In such dynamic scenario, one of the most challenging tasks involves the prediction of future interactions between couples of actors (i.e., users in online social networks, researchers in collaboration networks). In this paper, we approach such problem by leveraging networks dynamics: to this extent, we propose a supervised learning approach which exploits features computed by time-aware forecasts of topological measures calculated between node pairs. Moreover, since real social networks are generally composed by weakly connected modules, we instantiate the interaction prediction problem in two disjoint applicative scenarios: intra-community and inter-community link prediction. Experimental results on real time-stamped networks show how our approach is able to reach high accuracy. Furthermore, we analyze the performances of our methodology when varying the typologies of features, community discovery algorithms and forecast methods.1 aRossetti, Giulio1 aGuidotti, Riccardo1 aMiliou, Ioanna1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1007/s13278-016-0397-y02543nas a2200133   4500008004100000245009300041210006900134260001200203300001100215520208300226100001702309700002002326856006302346       2016                        eng d00aTowards operator-less data centers through data-driven, predictive, proactive autonomics0 aTowards operatorless data centers through datadriven predictive   c04/2016  a1–143 aContinued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using live data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating predictive models for node failures. Our results support the practicality of a data-driven approach by showing the effectiveness of predictive models based on data found in typical data center logs. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing node state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if nodes will fail in a future 24-h window. Our evaluation reveals that if we limit false positive rates to 5 %, we can achieve true positive rates between 27 and 88 % with precision varying between 50 and 72 %. This level of performance allows us to recover large fraction of jobs’ executions (by redirecting them to other nodes when a failure of the present node is predicted) that would otherwise have been wasted due to failures. We discuss the feasibility of including our predictive model as the central component of a data-driven autonomic manager and operating it on-line with live data streams (rather than off-line on data logs). All of the scripts used for BigQuery and classification analyses are publicly available on GitHub.1 aSirbu, Alina1 aBabaoglu, Ozalp  uhttp://link.springer.com/article/10.1007/s10586-016-0564-y01050nas a2200157   4500008004100000245004700041210004700088260003800135300001400173520054400187100002100731700002300752700002000775700001800795856007900813       2016                        eng d00aUnderstanding human mobility with big data0 aUnderstanding human mobility with big data  bSpringer International Publishing  a208–2203 aThe paper illustrates basic methods of mobility data mining, designed to extract from the big mobility data the patterns of collective movement behavior, i.e., discover the subgroups of travelers characterized by a common purpose, profiles of individual movement activity, i.e., characterize the routine mobility of each traveler. We illustrate a number of concrete case studies where mobility data mining is put at work to create powerful analytical services for policy makers, businesses, public administrations, and individual citizens.1 aGiannotti, Fosca1 aGabrielli, Lorenzo1 aPedreschi, Dino1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/understanding-human-mobility-big-data01716nas a2200169   4500008004100000245006700041210006700108300000700175490000600182520115300188100002301341700001901364700001801383700002001401700002101421856010401442       2016                        eng d00aUnveiling mobility complexity through complex network analysis0 aUnveiling mobility complexity through complex network analysis  a590 v63 aThe availability of massive digital traces of individuals is offering a series of novel insights on the understanding of patterns characterizing human mobility. Many studies try to semantically enrich mobility data with annotations about human activities. However, these approaches either focus on places with high frequencies (e.g., home and work), or relay on background knowledge (e.g., public available points of interest). In this paper, we depart from the concept of frequency and we focus on a high level representation of mobility using network analytics. The visits of each driver to each systematic destination are modeled as links in a bipartite network where a set of nodes represents drivers and the other set represents places. We extract such network from two real datasets of human mobility based, respectively, on GPS and GSM data. We introduce the concept of mobility complexity of drivers and places as a ranking analysis over the nodes of these networks. In addition, by means of community discovery analysis, we differentiate subgroups of drivers and places according both to their homogeneity and to their mobility complexity.1 aGuidotti, Riccardo1 aMonreale, Anna1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/unveiling-mobility-complexity-through-complex-network-analysis01492nas a2200169   4500008004100000020002200041245006500063210006400128520092200192100002001114700002201134700001701156700002001173700002401193700002101217856008401238       2016                        eng d  a978-989-758-181-600aUnveiling Political Opinion Structures with a Web-experiment0 aUnveiling Political Opinion Structures with a Webexperiment3 aThe dynamics of political votes has been widely studied, both for its practical interest and as a paradigm of the dynamics of mass opinions and collective phenomena, where theoretical predictions can be easily tested. However, the vote outcome is often influenced by many factors beyond the bare opinion on the candidate, and in most cases it is bound to a single preference. The voter perception of the political space is still to be elucidated. We here propose a web experiment (laPENSOcos`ı) where we explicitly investigate participants’ opinions on political entities (parties, coalitions, individual candidates) of the Italian political scene. As a main result, we show that the political perception follows a Weber-Fechner-like law, i.e., when ranking political entities according to the user expressed preferences, the perceived distance of the user from a given entity scales as the logarithm of this rank.1 aGravino, Pietro1 aCaminiti, Saverio1 aSirbu, Alina1 aTria, Francesca1 aServedio, Vito, D P1 aLoreto, Vittorio  uhttp://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/000590630039004701665nas a2200133   4500008004100000245008800041210006900129260003800198300001200236520111700248100002301365700002501388856011801413       2016                        eng d00aWhere Is My Next Friend? Recommending Enjoyable Profiles in Location Based Services0 aWhere Is My Next Friend Recommending Enjoyable Profiles in Locat  bSpringer International Publishing  a65–783 aHow many of your friends, with whom you enjoy spending some time, live close by? How many people are at your reach, with whom you could have a nice conversation? We introduce a measure of enjoyability that may be the basis for a new class of location-based services aimed at maximizing the likelihood that two persons, or a group of people, would enjoy spending time together. Our enjoyability takes into account both topic similarity between two users and the users’ tendency to connect to people with similar or dissimilar interest. We computed the enjoyability on two datasets of geo-located tweets, and we reasoned on the applicability of the obtained results for producing friend recommendations. We aim at suggesting couples of users which are not friends yet, but which are frequently co-located and maximize our enjoyability measure. By taking into account the spatial dimension, we show how 50 % of users may find at least one enjoyable person within 10 km of their two most visited locations. Our results are encouraging, and open the way for a new class of recommender systems based on enjoyability.1 aGuidotti, Riccardo1 aBerlingerio, Michele  uhttps://kdd.isti.cnr.it/publications/where-my-next-friend-recommending-enjoyable-profiles-location-based-services01914nas a2200145   4500008004100000245005100041210005100092260001600143520143700159100002301596700002001619700002001639700002401659856008501683       2015                        eng d00aBehavioral Entropy and Profitability in Retail0 aBehavioral Entropy and Profitability in Retail  aParisbIEEE3 aHuman behavior is predictable in principle: people are systematic in their everyday choices. This predictability can be used to plan events and infrastructure, both for the public good and for private gains. In this paper we investigate the largely unexplored relationship between the systematic behavior of a customer and its profitability for a retail company. We estimate a customer’s behavioral entropy over two dimensions: the basket entropy is the variety of what customers buy, and the spatio-temporal entropy is the spatial and temporal variety of their shopping sessions. To estimate the basket and the spatiotemporal entropy we use data mining and information theoretic techniques. We find that predictable systematic customers are more profitable for a supermarket: their average per capita expenditures are higher than non systematic customers and they visit the shops more often. However, this higher individual profitability is masked by its overall level. The highly systematic customers are a minority of the customer set. As a consequence, the total amount of revenues they generate is small. We suggest that favoring a systematic behavior in their customers might be a good strategy for supermarkets to increase revenue. These results are based on data coming from a large Italian supermarket chain, including more than 50 thousand customers visiting 23 shops to purchase more than 80 thousand distinct products.1 aGuidotti, Riccardo1 aCoscia, Michele1 aPedreschi, Dino1 aPennacchioli, Diego  uhttps://kdd.isti.cnr.it/publications/behavioral-entropy-and-profitability-retail00424nas a2200133   4500008004100000245004500041210004300086100001900129700002100148700002000169700002100189700001700210856006300227       2015                        eng d00aA Big Data Analyzer for Large Trace Logs0 aBig Data Analyzer for Large Trace Logs1 aBalliu, Alkida1 aOlivetti, Dennis1 aBabaoglu, Ozalp1 aMarzolla, Moreno1 aSirbu, Alina  uhttp://link.springer.com/article/10.1007/s00607-015-0480-701157nas a2200157   4500008004100000245005600041210005300097260003600150520061100186100002300797700002200820700002200842700002100864700002000885856009400905       2015                        eng d00aCity users’ classification with mobile phone data0 aCity users classification with mobile phone data  aSanta Clara (CA) - USAc11/20153 aNowadays mobile phone data are an actual proxy for studying the users’ social life and urban dynamics. In this paper we present the Sociometer, and analytical framework aimed at classifying mobile phone users into behavioral categories by means of their call habits. The analytical process starts from spatio-temporal profiles, learns the different behaviors, and returns annotated profiles. After the description of the methodology and its evaluation, we present an application of the Sociometer for studying city users of one small and one big city, evaluating the impact of big events in these cities.1 aGabrielli, Lorenzo1 aFurletti, Barbara1 aTrasarti, Roberto1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/city-users%E2%80%99-classification-mobile-phone-data01521nas a2200157   4500008004100000245005700041210005700098260003100155520103200186100002001218700001901238700001701257700002001274700001901294856005001313       2015                        eng d00aClustering Formulation Using Constraint Optimization0 aClustering Formulation Using Constraint Optimization  bSpringer Berlin Heidelberg3 aThe problem of clustering a set of data is a textbook machine learning problem, but at the same time, at heart, a typical optimization problem. Given an objective function, such as minimizing the intra-cluster distances or maximizing the inter-cluster distances, the task is to find an assignment of data points to clusters that achieves this objective. In this paper, we present a constraint programming model for a centroid based clustering and one for a density based clustering. In particular, as a key contribution, we show how the expressivity introduced by the formulation of the problem by constraint programming makes the standard problem easy to be extended with other constraints that permit to generate interesting variants of the problem. We show this important aspect in two different ways: first, we show how the formulation of the density-based clustering by constraint programming makes it very similar to the label propagation problem and then, we propose a variant of the standard label propagation approach.1 aGrossi, Valerio1 aMonreale, Anna1 aNanni, Mirco1 aPedreschi, Dino1 aTurini, Franco  uhttp://dx.doi.org/10.1007/978-3-662-49224-6_901183nas a2200145   4500008004100000245005700041210005500098260007200153520067500225100002800900700002900928700001800957700001800975856004400993       2015                        eng d00aComeWithMe: An Activity-Oriented Carpooling Approach0 aComeWithMe An ActivityOriented Carpooling Approach  bInstitute of Electrical {&} Electronics Engineers ({IEEE})c09/20153 aThe interest in carpooling is increasing due to the need to reduce traffic and noise pollution. Most of the available approaches and systems are route oriented, where driver and passengers are matched when the destination location is the same. ComeWithMe offers a new perspective: the destination is the intended activity instead of a location. This novel matching method is aimed to boost the possibilities of rides if passenger reaches a different location maintaining the activity. We conducted experiments using a real data set of trajectories and our results showed that the proposed matching algorithm improved the traditional carpooling approach in more than 80%.1 aLira, Vinicius Monteiro1 aTimes, Valéria Cesário1 aRenso, Chiara1 aRinzivillo, S  uhttp://dx.doi.org/10.1109/itsc.2015.41400574nas a2200169   4500008004100000020002200041245007400063210006900137260002400206100002100230700002100251700001700272700002000289700002100309700001800330856005600348       2015                        eng d  a978-1-4503-3854-700aCommunity-centric analysis of user engagement in Skype social network0 aCommunitycentric analysis of user engagement in Skype social net  aParis, FrancebIEEE1 aRossetti, Giulio1 aPappalardo, Luca1 aKikas, Riivo1 aPedreschi, Dino1 aGiannotti, Fosca1 aDumas, Marlon  uhttp://dl.acm.org/citation.cfm?doid=2808797.280938400450nas a2200133   4500008004100000245008600041210006900127300001400196490000600210100001700216700001800233700002300251856004200274       2015                        eng d00aData Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks0 aData Integration for Microarrays Enhanced Inference for Gene Reg  a255–2690 v41 aSirbu, Alina1 aCrane, Martin1 aRuskin, Heather, J  uhttp://www.mdpi.com/2076-3905/4/2/25501393nas a2200169   4500008004100000245005700041210005700098260002000155520084000175100002201015700002301037700002201060700002201082700002101104700002201125856007601147       2015                        eng d00aDetecting and understanding big events in big cities0 aDetecting and understanding big events in big cities  aBostonc04/20153 aRecent studies have shown the great potential of big data such as mobile phone location data to model human behavior. Big data allow to analyze people presence in a territory in a fast and effective way with respect to the classical surveys (diaries or questionnaires). One of the drawbacks of these collection systems is incompleteness of the users' traces; people are localized only when they are using their phones. In this work we define a data mining method for identifying people presence and understanding the impact of big events in big cities. We exploit the ability of the Sociometer for classifying mobile phone users in mobility categories through their presence profile. The experiment in cooperation with Orange Telecom has been conduced in Paris during the event F^ete de la Musique using a
privacy preserving protocol.1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aTrasarti, Roberto1 aSmoreda, Zbigniew1 aVanhoof, Maarten1 aZiemlicki, Cezary  uhttp://www.netmob.org/assets/img/netmob15_book_of_abstracts_posters.pdf02380nas a2200169   4500008004100000245004700041210004500088300001600133490000700149520190300156100001702059700002602076700001902102700002002121700002102141856004802162       2015                        eng d00aDiscrimination- and privacy-aware patterns0 aDiscrimination and privacyaware patterns  a1733–17820 v293 aData mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. We are therefore faced with unprecedented opportunities and risks: a deeper understanding of human behavior and how our society works is darkened by a greater chance of privacy intrusion and unfair discrimination based on the extracted patterns and profiles. Consider the case when a set of patterns extracted from the personal data of a population of individual persons is released for a subsequent use into a decision making process, such as, e.g., granting or denying credit. First, the set of patterns may reveal sensitive information about individual persons in the training population and, second, decision rules based on such patterns may lead to unfair discrimination, depending on what is represented in the training cases. Although methods independently addressing privacy or discrimination in data mining have been proposed in the literature, in this context we argue that privacy and discrimination risks should be tackled together, and we present a methodology for doing so while publishing frequent pattern mining results. We describe a set of pattern sanitization methods, one for each discrimination measure used in the legal literature, to achieve a fair publishing of frequent patterns in combination with two possible privacy transformations: one based on k-anonymity and one based on differential privacy. Our proposed pattern sanitization methods based on k-anonymity yield both privacy- and discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion. Moreover, they obtain a better trade-off between protection and data quality than the sanitization methods based on differential privacy. Finally, the effectiveness of our proposals is assessed by extensive experiments. 1 aHajian, Sara1 aDomingo-Ferrer, Josep1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1007/s10618-014-0393-700493nas a2200133   4500008004100000245008200041210006900123300001100192100002400203700002200227700003000249700001700279856006300296       2015                        eng d00aEgalitarianism in the rank aggregation problem: a new dimension for democracy0 aEgalitarianism in the rank aggregation problem a new dimension f  a1–161 aContucci, Pierluigi1 aPanizzi, Emanuele1 aRicci-Tersenghi, Federico1 aSirbu, Alina  uhttp://link.springer.com/article/10.1007/s11135-015-0197-x02127nas a2200121   4500008004100000245007900041210006900120520171000189100002001899700002001919700001901939856004701958       2015                        eng d00aAn exploration of learning processes as process maps in FLOSS repositories0 aexploration of learning processes as process maps in FLOSS repos3 aEvidence suggests that Free/Libre Open Source Software (FLOSS) environ-ments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while mak-ing use of the tools available in these environments. A number of studies docu-ment the existence of learning processes in FLOSS through the analysis of sur-veys and questionnaires filled by FLOSS project participants. At the same time, the interest in understanding the dynamics of the FLOSS phenomenon, its popu-larity and success resulted in the development of tools and techniques for extract-ing and analyzing data from different FLOSS data sources. This new field is called Mining Software Repositories (MSR). In spite of these efforts, there is limited work aiming to provide empirical evidence of learning processes directly from FLOSS repositories. In this paper, we seek to trigger such an initiative by proposing an approach based on Process Mining to trace learning behaviors from FLOSS participants’ trails of activities, as recorded in FLOSS repositories, and visualize them as pro-cess maps. Process maps provide a pictorial representation of real behavior as it is recorded in FLOSS data. Our aim is to provide critical evidence that boosts the understanding of learning behavior in FLOSS communities by analyzing the rel-evant repositories. In order to accomplish this, we propose an effective approach that comprises first the mining of FLOSS repositories in order to generate Event logs, and then the generation of process maps, equipped with relevant statistical data interpreting and indicating the value of process discovery from these reposi-tories.1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttp://eprints.adm.unipi.it/id/eprint/234401284nas a2200145   4500008004100000245006500041210006400106260004400170520074700214100001900961700001700980700002300997700002201020856009601042       2015                        eng d00aFind Your Way Back: Mobility Profile Mining with Constraints0 aFind Your Way Back Mobility Profile Mining with Constraints  aCorkbSpringer International Publishing3 aMobility profile mining is a data mining task that can be formulated as clustering over movement trajectory data. The main challenge is to separate the signal from the noise, i.e. one-off trips. We show that standard data mining approaches suffer the important drawback that they cannot take the symmetry of non-noise trajectories into account. That is, if a trajectory has a symmetric equivalent that covers the same trip in the reverse direction, it should become more likely that neither of them is labelled as noise. We present a constraint model that takes this knowledge into account to produce better clusters. We show the efficacy of our approach on real-world data that was previously processed using standard data mining techniques.1 aKotthoff, Lars1 aNanni, Mirco1 aGuidotti, Riccardo1 aO'Sullivan, Barry  uhttps://kdd.isti.cnr.it/publications/find-your-way-back-mobility-profile-mining-constraints02029nas a2200145   4500008004100000245008700041210006900128520139000197100001801587700002101605700002001626700002101646700002001667856019601687       2015                        eng d00aThe harsh rule of the goals: data-driven performance indicators for football teams0 aharsh rule of the goals datadriven performance indicators for fo3 a—Sports analytics in general, and football (soccer in
USA) analytics in particular, have evolved in recent years in an
amazing way, thanks to automated or semi-automated sensing
technologies that provide high-fidelity data streams extracted
from every game. In this paper we propose a data-driven
approach and show that there is a large potential to boost the
understanding of football team performance. From observational
data of football games we extract a set of pass-based performance
indicators and summarize them in the H indicator. We observe a
strong correlation among the proposed indicator and the success
of a team, and therefore perform a simulation on the four major
European championships (78 teams, almost 1500 games). The
outcome of each game in the championship was replaced by a
synthetic outcome (win, loss or draw) based on the performance
indicators computed for each team. We found that the final
rankings in the simulated championships are very close to the
actual rankings in the real championships, and show that teams
with high ranking error show extreme values of a defense/attack
efficiency measure, the Pezzali score. Our results are surprising
given the simplicity of the proposed indicators, suggesting that
a complex systems’ view on football data has the potential of
revealing hidden patterns and behavior of superior quality.1 aCintia, Paolo1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aMalvaldi, Marco  uhttps://www.researchgate.net/profile/Luca_Pappalardo/publication/281318318_The_harsh_rule_of_the_goals_data-driven_performance_indicators_for_football_teams/links/561668e308ae37cfe4090a5d.pdf00450nas a2200109   4500008004100000245011200041210006900153260001300222100001700235700002000252856006800272       2015                        eng d00aA Holistic Approach to Log Data Analysis in High-Performance Computing Systems: The Case of IBM Blue Gene/Q0 aHolistic Approach to Log Data Analysis in HighPerformance Comput  bSpringer1 aSirbu, Alina1 aBabaoglu, Ozalp  uhttp://link.springer.com/chapter/10.1007%2F978-3-319-27308-2_5101436nas a2200169   4500008004100000020002200041245007800063210006900141260002400210520086700234100002101101700002301122700002401145700002001169700002101189856005601210       2015                        eng d  a978-1-4503-3854-700aInteraction Prediction in Dynamic Networks exploiting Community Discovery0 aInteraction Prediction in Dynamic Networks exploiting Community   aParis, FrancebIEEE3 aDue to the growing availability of online social services, interactions between people became more and more easy to establish and track. Online social human activities generate digital footprints, that describe complex, rapidly evolving, dynamic networks. In such scenario one of the most challenging task to address involves the prediction of future interactions between couples of actors. In this study, we want to leverage networks dynamics and community structure to predict which are the future interactions more likely to appear. To this extent, we propose a supervised learning approach which exploit features computed by time-aware forecasts of topological measures calculated between pair of nodes belonging to the same community. Our experiments on real dynamic networks show that the designed analytical process is able to achieve interesting results.1 aRossetti, Giulio1 aGuidotti, Riccardo1 aPennacchioli, Diego1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dl.acm.org/citation.cfm?doid=2808797.280940100463nas a2200109   4500008004100000245008900041210006900130300001200199490000600211100002400217856011200241       2015                        eng d00aIntroduction to the special issue on Artificial Intelligence for Society and Economy0 aIntroduction to the special issue on Artificial Intelligence for  a23–230 v91 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/introduction-special-issue-artificial-intelligence-society-and-economy01046nas a2200145   4500008004100000020002200041245007100063210006900134490000700203520053000210100001900740700002000759700002200779856009900801       2015                        eng d  a978-88-99200-62-600aItEM: A Vector Space Model to Bootstrap an Italian Emotive Lexicon0 aItEM A Vector Space Model to Bootstrap an Italian Emotive Lexico0 vII3 aIn  recent  years  computational  linguistics  has  seen  a  rising  interest  in subjectivity,  opinions,  feelings and  emotions.  Even  though  great attention  has been given to polarity recognition, the research in emotion detection has had to rely on small emotion resources. In this paper,  we  present  a  methodology  to  build emotive   lexicons   by  jointly   exploiting vector  space  models  and  human  annotation,  and  we  provide  the  first  results  of the  evaluation  with  a  crowdsourcing  experiment.1 aPassaro, Lucia1 aPollacci, Laura1 aLenci, Alessandro  uhttps://kdd.isti.cnr.it/publications/item-vector-space-model-bootstrap-italian-emotive-lexicon01378nas a2200133   4500008004100000245005200041210004800093260000900141520095100150100001801101700002401119700001901143856008201162       2015                        eng d00aThe layered structure of company share networks0 alayered structure of company share networks  bIEEE3 aWe present a framework for the analysis of corporate governance problems using network science and graph algorithms on ownership networks. In such networks, nodes model companies/shareholders and edges model shares owned. Inspired by the widespread pyramidal organization of corporate groups of companies, we model ownership networks as layered graphs, and exploit the layered structure to design feasible and efficient solutions to three key problems of corporate governance. The first one is the long-standing problem of computing direct and indirect ownership (integrated ownership problem). The other two problems are introduced here: computing direct and indirect dividends (dividend problem), and computing the group of companies controlled by a parent shareholder (corporate group problem). We conduct an extensive empirical analysis of the Italian ownership network, which, with its 3.9M nodes, is 30× the largest network studied so far.1 aRomei, Andrea1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/layered-structure-company-share-networks00796nas a2200157   4500008004100000245005100041210005000092260000900142520030900151100001500460700002100475700001600496700002300512700002300535856008000558       2015                        eng d00aManaging travels with PETRA: The Rome use case0 aManaging travels with PETRA The Rome use case  bIEEE3 aThe aim of the PETRA project is to provide the basis for a city-wide transportation system that supports policies catering for both individual preferences of users and city-wide travel patterns. The PETRA platform will be initially deployed in the partner city of Rome, and later in Venice, and Tel-Aviv.1 aBotea, Adi1 aBraghin, Stefano1 aLopes, Nuno1 aGuidotti, Riccardo1 aPratesi, Francesca  uhttps://kdd.isti.cnr.it/publications/managing-travels-petra-rome-use-case-001355nas a2200133   4500008004100000245005800041210005800099260001900157520089600176100002001072700002001092700001901112856009001131       2015                        eng d00aMining learning processes from FLOSS mailing archives0 aMining learning processes from FLOSS mailing archives  bSpringer, Cham3 aEvidence suggests that Free/Libre Open Source Software (FLOSS) environments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while making use of these environments. As FLOSS repositories store data about participants’ interaction and activities, we analyze participants’ interaction and knowledge exchange in emails to trace learning activities that occur in distinct phases of the learning process. We make use of semantic search in SQL to retrieve data and build corresponding event logs which are then fed to a process mining tool in order to produce visual workflow nets. We view these nets as representative of the traces of learning activities in FLOSS as well as their relevant flow of occurrence. Additional statistical details are provided to contextualize and describe these models.1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/mining-learning-processes-floss-mailing-archives00973nas a2200181   4500008004100000245004900041210004900090260003800139520039600177100002500573700001600598700001500614700002100629700001600650700002300666700002300689856007900712       2015                        eng d00aMobility Mining for Journey Planning in Rome0 aMobility Mining for Journey Planning in Rome  bSpringer International Publishing3 aWe present recent results on integrating private car GPS routines obtained by a Data Mining module. into the PETRA (PErsonal TRansport Advisor) platform. The routines are used as additional “bus lines”, available to provide a ride to travelers. We present the effects of querying the planner with and without the routines, which show how Data Mining may help Smarter Cities applications.1 aBerlingerio, Michele1 aBicer, Veli1 aBotea, Adi1 aBraghin, Stefano1 aLopes, Nuno1 aGuidotti, Riccardo1 aPratesi, Francesca  uhttps://kdd.isti.cnr.it/publications/mobility-mining-journey-planning-rome02544nas a2200373   4500008004100000022001400041245008200055210006900137260000900206300001300215490000700228520142000235100001701655700001901672700002201691700002201713700001501735700002001750700002001770700001901790700002101809700002101830700001901851700002101870700001601891700002601907700002001933700002401953700001701977700001701994700002002011700002702031856011202058       2015                        eng d  a1932-620300aParticipatory Patterns in an International Air Quality Monitoring Initiative.0 aParticipatory Patterns in an International Air Quality Monitorin  c2015  ae01367630 v103 a<p>The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.</p>1 aSirbu, Alina1 aBecker, Martin1 aCaminiti, Saverio1 aDe Baets, Bernard1 aElen, Bart1 aFrancis, Louise1 aGravino, Pietro1 aHotho, Andreas1 aIngarra, Stefano1 aLoreto, Vittorio1 aMolino, Andrea1 aMueller, Juergen1 aPeters, Jan1 aRicchiuti, Ferdinando1 aSaracino, Fabio1 aServedio, Vito, D P1 aStumme, Gerd1 aTheunis, Jan1 aTria, Francesca1 aVan den Bossche, Joris  uhttps://kdd.isti.cnr.it/publications/participatory-patterns-international-air-quality-monitoring-initiative02010nas a2200157   4500008004100000245004500041210004500086260001200131300001100143490000600154520154300160100002001703700002401723700002101747856008401768       2015                        eng d00aProduct assortment and customer mobility0 aProduct assortment and customer mobility  c10-2015  a1–180 v43 aCustomers mobility is dependent on the sophistication of their needs: sophisticated customers need to travel more to fulfill their needs. In this paper, we provide more detailed evidence of this phenomenon, providing an empirical validation of the Central Place Theory. For each customer, we detect what is her favorite shop, where she purchases most products. We can study the relationship between the favorite shop and the closest one, by recording the influence of the shop’s size and the customer’s sophistication in the discordance cases, i.e. the cases in which the favorite shop is not the closest one. We show that larger shops are able to retain most of their closest customers and they are able to catch large portions of customers from smaller shops around them. We connect this observation with the shop’s larger sophistication, and not with its other characteristics, as the phenomenon is especially noticeable when customers want to satisfy their sophisticated needs. This is a confirmation of the recent extensions of the Central Place Theory, where the original assumptions of homogeneity in customer purchase power and needs are challenged. Different types of shops have also different survival logics. The largest shops get closed if they are unable to catch customers from the smaller shops, while medium size shops get closed if they cannot retain their closest customers. All analysis are performed on a large real-world dataset recording all purchases from millions of customers across the west coast of Italy.1 aCoscia, Michele1 aPennacchioli, Diego1 aGiannotti, Fosca  uhttp://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0051-301921nas a2200169   4500008004100000245003800041210003800079260002400117520140400141100001901545700001901564700002101583700002001604700002101624700002501645856008101670       2015                        eng d00aQuantification in Social Networks0 aQuantification in Social Networks  aParis, FrancebIEEE3 aIn many real-world applications there is a need to monitor the distribution of a population across different classes, and to track changes in this distribution over time. As an example, an important task is to monitor the percentage of unemployed adults in a given region. When the membership of an individual in a class cannot be established deterministically, a typical solution is the classification task. However, in the above applications the final goal is not determining which class the individuals belong to, but estimating the prevalence of each class in the unlabeled data. This task is called quantification. Most of the work in the literature addressed the quantification problem considering data presented in conventional attribute format. Since the ever-growing availability of web and social media we have a flourish of network data representing a new important source of information and by using quantification network techniques we could quantify collective behavior, i.e., the number of users that are involved in certain type of activities, preferences, or behaviors. In this paper we exploit the homophily effect observed in many social networks in order to construct a quantifier for networked data. Our experiments show the effectiveness of the proposed approaches and the comparison with the existing state-of-the-art quantification methods shows that they are more accurate. 1 aMilli, Letizia1 aMonreale, Anna1 aRossetti, Giulio1 aPedreschi, Dino1 aGiannotti, Fosca1 aSebastiani, Fabrizio  uhttp://www.giuliorossetti.net/about/wp-content/uploads/2015/12/main_DSAA.pdf01571nas a2200181   4500008004100000245005600041210005600097260000700153490000600160520105400166100002101220700002001241700001801261700002001279700002101299700002801320856004101348       2015                        eng d00aReturners and explorers dichotomy in human mobility0 aReturners and explorers dichotomy in human mobility  c090 v63 aThe availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.1 aPappalardo, Luca1 aSimini, Filippo1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca1 aBarabasi, Albert-Laszlo  uhttp://dx.doi.org/10.1038/ncomms916601825nas a2200217   4500008004100000245004800041210004600089300000600135490000600141520119400147100001801341700001901359700002201378700002401400700002101424700002001445700002301465700001901488700002401507856007601531       2015                        eng d00aA risk model for privacy in trajectory data0 arisk model for privacy in trajectory data  a90 v23 aTime sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacy-preserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users with a high degree of certainty. These methods provide theoretical worst-case privacy risks as measures of the privacy protection that they offer. However, often with many real-world data the worst-case scenario is too pessimistic and does not provide a realistic view of the privacy risks: the real probability of re-identification is often much lower than the theoretical worst-case risk. In this paper, we propose a novel empirical risk model for privacy which, in relation to the cost of privacy attacks, demonstrates better the practical risks associated with a privacy preserving data release. We show detailed evaluation of the proposed risk model by using k-anonymised real-world mobility data and then, we show how the empirical evaluation of the privacy risk has a different trend in synthetic data describing random movements.1 aBasu, Anirban1 aMonreale, Anna1 aTrasarti, Roberto1 aCorena, Juan Camilo1 aGiannotti, Fosca1 aPedreschi, Dino1 aKiyomoto, Shinsaku1 aMiyake, Yutaka1 aYanagihara, Tadashi  uhttps://kdd.isti.cnr.it/publications/risk-model-privacy-trajectory-data01127nas a2200121   4500008004100000245005900041210005900100260001900159520069200178100002300870700002400893856008800917       2015                        eng d00aSegregation Discovery in a Social Network of Companies0 aSegregation Discovery in a Social Network of Companies  bSpringer, Cham3 aWe introduce a framework for a data-driven analysis of segregation of minority groups in social networks, and challenge it on a complex scenario. The framework builds on quantitative measures of segregation, called segregation indexes, proposed in the social science literature. The segregation discovery problem consists of searching sub-graphs and sub-groups for which a reference segregation index is above a minimum threshold. A search algorithm is devised that solves the segregation problem. The framework is challenged on the analysis of segregation of social groups in the boards of directors of the real and large network of Italian companies connected through shared directors.1 aBaroni, Alessandro1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/segregation-discovery-social-network-companies00674nas a2200205   4500008004100000245006100041210006000102300001400162490000700176100002300183700002100206700002000227700002000247700002100267700002000288700001800308700002100326700002300347856009800370       2015                        eng d00aSmall Area Model-Based Estimators Using Big Data Sources0 aSmall Area ModelBased Estimators Using Big Data Sources  a263–2810 v311 aMarchetti, Stefano1 aGiusti, Caterina1 aPratesi, Monica1 aSalvati, Nicola1 aGiannotti, Fosca1 aPedreschi, Dino1 aRinzivillo, S1 aPappalardo, Luca1 aGabrielli, Lorenzo  uhttps://kdd.isti.cnr.it/publications/small-area-model-based-estimators-using-big-data-sources00553nas a2200145   4500008004100000245007400041210006900115260000900184100002300193700001800216700002500234700002400259700002000283856010400303       2015                        eng d00aSocial or green? A data-driven approach for more enjoyable carpooling0 aSocial or green A datadriven approach for more enjoyable carpool  bIEEE1 aGuidotti, Riccardo1 aSassi, Andrea1 aBerlingerio, Michele1 aPascale, Alessandra1 aGhaddar, Bissan  uhttps://kdd.isti.cnr.it/publications/social-or-green-data-driven-approach-more-enjoyable-carpooling00406nas a2200109   4500008004100000245007700041210006900118100002300187700002200210700001700232856004700249       2015                        eng d00a{TOSCA:} two-steps clustering algorithm for personal locations detection0 aTOSCA twosteps clustering algorithm for personal locations detec1 aGuidotti, Riccardo1 aTrasarti, Roberto1 aNanni, Mirco  uhttp://doi.acm.org/10.1145/2820783.282081800491nas a2200121   4500008004100000245006900041210006900110260003100179300001400210100002300224700001800247856010400265       2015                        eng d00aTowards a Boosted Route Planner Using Individual Mobility Models0 aTowards a Boosted Route Planner Using Individual Mobility Models  bSpringer Berlin Heidelberg  a108–1231 aGuidotti, Riccardo1 aCintia, Paolo  uhttps://kdd.isti.cnr.it/publications/towards-boosted-route-planner-using-individual-mobility-models00409nas a2200109   4500008004100000245005100041210005000092260000900142100001700151700002000168856011100188       2015                        eng d00aTowards Data-Driven Autonomics in Data Centers0 aTowards DataDriven Autonomics in Data Centers  bIEEE1 aSirbu, Alina1 aBabaoglu, Ozalp  uhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7312140&filter%3DAND%28p_IS_Number%3A7312127%2900458nas a2200121   4500008004100000245009600041210006900137100002300206700002200229700001700251700002100268856004700289       2015                        eng d00aTowards user-centric data management: individual mobility analytics for collective services0 aTowards usercentric data management individual mobility analytic1 aGuidotti, Riccardo1 aTrasarti, Roberto1 aNanni, Mirco1 aGiannotti, Fosca  uhttp://doi.acm.org/10.1145/2834126.283413201297nas a2200193   4500008004100000022002200041245006500063210006500128260003800193300001200231490000900243520068200252100002300934700002200957700002100979700001701000700001801017856006801035       2015                        eng d  a978-3-319-15200-400aUse of Mobile Phone Data to Estimate Visitors Mobility Flows0 aUse of Mobile Phone Data to Estimate Visitors Mobility Flows  bSpringer International Publishing  a214-2260 v89383 aBig Data originating from the digital breadcrumbs of human activities, sensed as by-product of the technologies that we use for our daily activities, allows us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data “proxies”, such as the mobile calls data for mobility. In this paper we investigate to what extent data coming from mobile operators could be a support in producing reliable and timely estimates of intra-city mobility flows. The idea is to define an estimation method based on calling data to characterize the mobility habits of visitors at the level of a single municipality.1 aGabrielli, Lorenzo1 aFurletti, Barbara1 aGiannotti, Fosca1 aNanni, Mirco1 aRinzivillo, S  uhttp://link.springer.com/chapter/10.1007%2F978-3-319-15201-1_1401426nas a2200133   4500008004100000245009200041210006900133260001900202520089400221100002001115700002001135700001901155856011801174       2014                        eng d00aAn abstract state machine (ASM) representation of learning process in FLOSS communities0 aabstract state machine ASM representation of learning process in  bSpringer, Cham3 aFree/Libre Open Source Software (FLOSS) communities as collaborative environments enable the occurrence of learning between participants in these groups. With the increasing interest research on understanding the mechanisms and processes through which learning occurs in FLOSS, there is an imperative to describe these processes. One successful way of doing this is through specification methods. In this paper, we describe the adoption of Abstract States Machines (ASMs) as a specification methodology for the description of learning processes in FLOSS. The goal of this endeavor is to represent the many possible steps and/or activities FLOSS participants go through during interactions that can be categorized as learning processes. Through ASMs, we express learning phases as states while activities that take place before moving from one state to another are expressed as transitions.1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/abstract-state-machine-asm-representation-learning-process-floss-communities01995nas a2200157   4500008003900000245005100039210005100090300001400141490000700155520154700162100001901709700002001728700002201748700001901770856004801789       2014                          d00aAnonymity preserving sequential pattern mining0 aAnonymity preserving sequential pattern mining  a141–1730 v223 aThe increasing availability of personal data of a sequential nature, such as time-stamped transaction or location data, enables increasingly sophisticated sequential pattern mining techniques. However, privacy is at risk if it is possible to reconstruct the identity of individuals from sequential data. Therefore, it is important to develop privacy-preserving techniques that support publishing of really anonymous data, without altering the analysis results significantly. In this paper we propose to apply the Privacy-by-design paradigm for designing a technological framework to counter the threats of undesirable, unlawful effects of privacy violation on sequence data, without obstructing the knowledge discovery opportunities of data mining technologies. First, we introduce a k-anonymity framework for sequence data, by defining the sequence linking attack model and its associated countermeasure, a k-anonymity notion for sequence datasets, which provides a formal protection against the attack. Second, we instantiate this framework and provide a specific method for constructing the k-anonymous version of a sequence dataset, which preserves the results of sequential pattern mining, together with several basic statistics and other analytical properties of the original data, including the clustering structure. A comprehensive experimental study on realistic datasets of process-logs, web-logs and GPS tracks is carried out, which empirically shows how, in our proposed method, the protection of privacy meets analytical utility.1 aMonreale, Anna1 aPedreschi, Dino1 aPensa, Ruggero, G1 aPinelli, Fabio  uhttp://dx.doi.org/10.1007/s10506-014-9154-601456nas a2200145   4500008004100000245006500041210006400106260003300170520092200203100002401125700001701149700002001166700002201186856010201208       2014                        eng d00aAnti-discrimination analysis using privacy attack strategies0 aAntidiscrimination analysis using privacy attack strategies  bSpringer, Berlin, Heidelberg3 aSocial discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination discovery under hard assumptions which have rarely tackled in the literature: indirect discrimination discovery, privacy-aware discrimination discovery, and discrimination data recovery. The intuition comes from the intriguing parallel between the role of the anti-discrimination authority in the three scenarios above and the role of an attacker in private data publishing. We design strategies and algorithms inspired/based on Frèchet bounds attacks, attribute inference attacks, and minimality attacks to the purpose of unveiling hidden discriminatory practices. Experimental results show that they can be effective tools in the hands of anti-discrimination authorities.1 aRuggieri, Salvatore1 aHajian, Sara1 aKamiran, Faisal1 aZhang, Xiangliang  uhttps://kdd.isti.cnr.it/publications/anti-discrimination-analysis-using-privacy-attack-strategies00459nas a2200145   4500008004100000245004800041210004700089260004400136100001900180700002100199700002000220700002100240700001700261856003500278       2014                        eng d00aBiDAl: Big Data Analyzer for Cluster Traces0 aBiDAl Big Data Analyzer for Cluster Traces  bGI-Edition Lecture Notes in Informatics1 aBalliu, Alkida1 aOlivetti, Dennis1 aBabaoglu, Ozalp1 aMarzolla, Moreno1 aSirbu, Alina  uhttp://arxiv.org/abs/1410.130900498nas a2200157   4500008003900000024001300039245005600052210005500108260002800163100002200191700002200213700002300235700001700258700002000275856004500295       2014                          d  aVol-113300aBig data analytics for smart mobility: a case study0 aBig data analytics for smart mobility a case study  aAthens, Greecec03/20141 aFurletti, Barbara1 aTrasarti, Roberto1 aGabrielli, Lorenzo1 aNanni, Mirco1 aPedreschi, Dino  uhttp://ceur-ws.org/Vol-1133/paper-57.pdf01965nas a2200193   4500008004100000245007600041210006900117260000900186520136400195100001801559700002401577700001901601700002001620700002101640700002301661700002001684700001901704856004801723       2014                        eng d00aCF-inspired Privacy-Preserving Prediction of Next Location in the Cloud0 aCFinspired PrivacyPreserving Prediction of Next Location in the   bIEEE3 aMobility data gathered from location sensors such as Global Positioning System (GPS) enabled phones and vehicles is valuable for spatio-temporal data mining for various location-based services (LBS). Such data is often considered sensitive and there exist many a mechanism for privacy preserving analyses of the data. Through various anonymisation mechanisms, it can be ensured with a high probability that a particular individual cannot be identified when mobility data is outsourced to third parties for analysis. However, challenges remain with the privacy of the queries on outsourced analysis results, especially when the queries are sent directly to third parties by end-users. Drawing inspiration from our earlier work in privacy preserving collaborative filtering (CF) and next location prediction, in this exploratory work, we propose a novel representation of trajectory data in the CF domain and experiment with a privacy preserving Slope One CF predictor. We present evaluations for the accuracy and the computational performance of our proposal using anonymised data gathered from real traffic data in the Italian cities of Pisa and Milan. One use-case is a third-party location-prediction-as-a-service deployed on a public cloud, which can respond to privacy-preserving queries while enabling data owners to build a rich predictor on the cloud. 1 aBasu, Anirban1 aCorena, Juan Camilo1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aKiyomoto, Shinsaku1 aVaidya, Jaideep1 aMiyake, Yutaka  uhttp://dx.doi.org/10.1109/CloudCom.2014.11400546nas a2200145   4500008004100000245007400041210006900115490000700184100001900191700002400210700002000234700002400254700002200278856010000300       2014                        eng d00aThe CoLing Lab system for Sentiment Polarity Classification of tweets0 aCoLing Lab system for Sentiment Polarity Classification of tweet0 vII1 aPassaro, Lucia1 aLebani, Gianluca, E1 aPollacci, Laura1 aChersoni, Emmanuele1 aLenci, Alessandro  uhttps://kdd.isti.cnr.it/publications/coling-lab-system-sentiment-polarity-classification-tweets01216nas a2200157   4500008004100000245005100041210004400092300001400136490000800150520073400158100002400892700002200916700001700938700002500955856007800980       2014                        eng d00aOn the complexity of quantified linear systems0 acomplexity of quantified linear systems  a128–1340 v5183 aIn this paper, we explore the computational complexity of the conjunctive fragment of the first-order theory of linear arithmetic. Quantified propositional formulas of linear inequalities with (k−1) quantifier alternations are log-space complete in ΣkP or ΠkP depending on the initial quantifier. We show that when we restrict ourselves to quantified conjunctions of linear inequalities, i.e., quantified linear systems, the complexity classes collapse to polynomial time. In other words, the presence of universal quantifiers does not alter the complexity of the linear programming problem, which is known to be in P. Our result reinforces the importance of sentence formats from the perspective of computational complexity.1 aRuggieri, Salvatore1 aEirinakis, Pavlos1 aSubramani, K1 aWojciechowski, Piotr  uhttps://kdd.isti.cnr.it/publications/complexity-quantified-linear-systems01257nas a2200145   4500008004100000245005600041210005500097300001400152490000700166520078100173100002100954700002400975700002200999856009001021       2014                        eng d00aDecision tree building on multi-core using FastFlow0 aDecision tree building on multicore using FastFlow  a800–8200 v263 aThe whole computer hardware industry embraced the multi-core. The extreme optimisation of sequential algorithms is then no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an in-depth study of the parallelisation of an implementation of the C4.5 algorithm for multi-core architectures. We characterise elapsed time lower bounds for the forms of parallelisations adopted and achieve close to optimal performance. Our implementation is based on the FastFlow parallel programming environment, and it requires minimal changes to the original sequential code. Copyright © 2013 John Wiley & Sons, Ltd.1 aAldinucci, Marco1 aRuggieri, Salvatore1 aTorquati, Massimo  uhttps://kdd.isti.cnr.it/publications/decision-tree-building-multi-core-using-fastflow01871nas a2200217   4500008003900000022001400039245010000053210006900153300000600222520115600228653001901384100002201403700003101425700001701456700002201473700002201495700002101517700002201538700002201560856007101582       2014                          d  a0308-596100aDiscovering urban and country dynamics from mobile phone data with spatial correlation patterns0 aDiscovering urban and country dynamics from mobile phone data wi  a-3 aAbstract Mobile communication technologies pervade our society and existing wireless networks are able to sense the movement of people, generating large volumes of data related to human activities, such as mobile phone call records. At the present, this kind of data is collected and stored by telecom operators infrastructures mainly for billing reasons, yet it represents a major source of information in the study of human mobility. In this paper, we propose an analytical process aimed at extracting interconnections between different areas of the city that emerge from highly correlated temporal variations of population local densities. To accomplish this objective, we propose a process based on two analytical tools: (i) a method to estimate the presence of people in different geographical areas; and (ii) a method to extract time- and space-constrained sequential patterns capable to capture correlations among geographical areas in terms of significant co-variations of the estimated presence. The methods are presented and combined in order to deal with two real scenarios of different spatial scale: the Paris Region and the whole France.10aUrban dynamics1 aTrasarti, Roberto1 aOlteanu-Raimond, Ana-Maria1 aNanni, Mirco1 aCouronné, Thomas1 aFurletti, Barbara1 aGiannotti, Fosca1 aSmoreda, Zbigniew1 aZiemlicki, Cezary  uhttp://www.sciencedirect.com/science/article/pii/S030859611300201200512nas a2200133   4500008004100000245009000041210007100131260003800202300001400240100001700254700001800271700002300289856006600312       2014                        eng d00aEGIA–Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach0 aEGIA–Evolutionary Optimisation of Gene Regulatory Networks an In  bSpringer International Publishing  a217–2291 aSirbu, Alina1 aCrane, Martin1 aRuskin, Heather, J  uhttp://link.springer.com/chapter/10.1007/978-3-319-05401-8_2101701nas a2200157   4500008003900000245002700039210002700066300001400093520128600107100001701393700001901410700002001429700002601449700002101475856004701496       2014                          d00aFair pattern discovery0 aFair pattern discovery  a113–1203 aData mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. We are assisting to unprecedented opportunities of understanding human and society behavior that unfortunately is darkened by several risks for human rights: one of this is the unfair discrimination based on the extracted patterns and profiles. Consider the case when a set of patterns extracted from the personal data of a population of individual persons is released for subsequent use in a decision making process, such as, e.g., granting or denying credit. Decision rules based on such patterns may lead to unfair discrimination, depending on what is represented in the training cases. In this context, we address the discrimination risks resulting from publishing frequent patterns. We present a set of pattern sanitization methods, one for each discrimination measure used in the legal literature, for fair (discrimination-protected) publishing of frequent pattern mining results. Our proposed pattern sanitization methods yield discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion. Finally, the effectiveness of our proposals is assessed by extensive experiments.1 aHajian, Sara1 aMonreale, Anna1 aPedreschi, Dino1 aDomingo-Ferrer, Josep1 aGiannotti, Fosca  uhttp://doi.acm.org/10.1145/2554850.255504300570nas a2200133   4500008004100000245011900041210006900160300001400229490000700243100002100250700002000271700002400291856012100315       2014                        eng d00aIntroduction to special issue on computational methods for enforcing privacy and fairness in the knowledge society0 aIntroduction to special issue on computational methods for enfor  a109–1110 v221 aMascetti, Sergio1 aRicci, Annarita1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/introduction-special-issue-computational-methods-enforcing-privacy-and-fairness00542nas a2200157   4500008003900000245006600039210006600105260002500171300001400196100002800210700001800238700001800256700002900274700003400303856004700337       2014                          d00aInvestigating semantic regularity of human mobility lifestyle0 aInvestigating semantic regularity of human mobility lifestyle  aPorto, PortugalbACM  a314–3171 aLira, Vinicius Monteiro1 aRinzivillo, S1 aRenso, Chiara1 aTimes, Valéria Cesário1 aTedesco, Patr{\'ı}cia, C A R  uhttp://doi.acm.org/10.1145/2628194.262822600492nas a2200133   4500008003900000245009200039210006900131300001400200100002800214700001800242700002900260700001800289856005100307       2014                          d00a{MAPMOLTY:} {A} Web Tool for Discovering Place Loyalty Based on Mobile Crowdsource Data0 aMAPMOLTY A Web Tool for Discovering Place Loyalty Based on Mobil  a528–5311 aLira, Vinicius Monteiro1 aRinzivillo, S1 aTimes, Valéria Cesário1 aRenso, Chiara  uhttp://dx.doi.org/10.1007/978-3-319-08245-5_4300447nas a2200109   4500008004100000245006800041210006700109100001800176700002100194700002000215856010200235       2014                        eng d00aMining efficient training patterns of non-professional cyclists0 aMining efficient training patterns of nonprofessional cyclists1 aCintia, Paolo1 aPappalardo, Luca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/mining-efficient-training-patterns-non-professional-cyclists01818nas a2200205   4500008003900000245002300039210002300062260001500085300000900100520128200109100001701391700002201408700001801430700002201448700001801470700002301488700001801511700002101529856006201550       2014                          d00aMobility Profiling0 aMobility Profiling  bIGI Global  a1-293 aThe ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network. 1 aNanni, Mirco1 aTrasarti, Roberto1 aCintia, Paolo1 aFurletti, Barbara1 aRenso, Chiara1 aGabrielli, Lorenzo1 aRinzivillo, S1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mobility-profiling-001608nas a2200133   4500008004100000245005800041210005600099300001400155490000700169520116600176100001801342700002401360856009001384       2014                        eng d00aA multidisciplinary survey on discrimination analysis0 amultidisciplinary survey on discrimination analysis  a582–6380 v293 aThe collection and analysis of observational and experimental data represent the main tools for assessing the presence, the extent, the nature, and the trend of discrimination phenomena. Data analysis techniques have been proposed in the last 50 years in the economic, legal, statistical, and, recently, in the data mining literature. This is not surprising, since discrimination analysis is a multidisciplinary problem, involving sociological causes, legal argumentations, economic models, statistical techniques, and computational issues. The objective of this survey is to provide a guidance and a glue for researchers and anti-discrimination data analysts on concepts, problems, application areas, datasets, methods, and approaches from a multidisciplinary perspective. We organize the approaches according to their method of data collection as observational, quasi-experimental, and experimental studies. A fourth line of recently blooming research on knowledge discovery based methods is also covered. Observational methods are further categorized on the basis of their application context: labor economics, social profiling, consumer markets, and others.1 aRomei, Andrea1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/multidisciplinary-survey-discrimination-analysis01500nas a2200133   4500008004100000245007000041210006900111260001900180520100900199100002001208700002001228700001901248856009901267       2014                        eng d00aOntolifloss: Ontology for learning processes in FLOSS communities0 aOntolifloss Ontology for learning processes in FLOSS communities  bSpringer, Cham3 aFree/Libre Open Source Software (FLOSS) communities are considered an example of commons-based peer-production models where groups of participants work together to achieve projects of common purpose. In these settings, many occurring activities can be documented and have established them as learning environments. As knowledge exchange is proved to occur in FLOSS, the dynamic and free nature of participation poses a great challenge in understanding activities pertaining to Learning Processes.

In this paper we raise this question and propose an ontology (called OntoLiFLOSS) in order to define terms and concepts that can explain learning activities taking place in these communities. The objective of this endeavor is to define in the simplest possible way a common definition of concepts and activities that can guide the identification of learning processes taking place among FLOSS members in any of the standard repositories such as mailing list, SVN, bug trackers and even discussion forums.1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/ontolifloss-ontology-learning-processes-floss-communities01798nas a2200133   4500008004100000245011000041210006900151260000900220520130600229100002401535700002001559700002001579856006501599       2014                        eng d00aOverlap versus partition: marketing classification and customer profiling in complex networks of products0 aOverlap versus partition marketing classification and customer p  bIEEE3 aIn recent years we witnessed the explosion in the availability of data regarding human and customer behavior in the market. This data richness era has fostered the development of useful applications in understanding how markets and the minds of the customers work. In this paper we focus on the analysis of complex networks based on customer behavior. Complex network analysis has provided a new and wide toolbox for the classic data mining task of clustering. With community discovery, i.e. the detection of functional modules in complex networks, we are now able to group together customers and products using a variety of different criteria. The aim of this paper is to explore this new analytic degree of freedom. We are interested in providing a case study uncovering the meaning of different community discovery algorithms on a network of products connected together because co-purchased by the same customers. We focus our interest in the different interpretation of a partition approach, where each product belongs to a single community, against an overlapping approach, where each product can belong to multiple communities. We found that the former is useful to improve the marketing classification of products, while the latter is able to create a collection of different customer profiles.1 aPennacchioli, Diego1 aCoscia, Michele1 aPedreschi, Dino  uhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=681831200482nas a2200145   4500008004100000245006800041210006300109100002400172700002100196700002100217700002000238700002100258700002000279856003700299       2014                        eng d00aThe patterns of musical influence on the Last.Fm social network0 apatterns of musical influence on the LastFm social network1 aPennacchioli, Diego1 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aCoscia, Michele  uhttps://kdd.isti.cnr.it/node/62301645nas a2200205   4500008003900000245004500039210004300084300001400127520105800141100001801199700001901217700002401236700002101260700002001281700002301301700001901324700002401343700002201367856005001389       2014                          d00aA Privacy Risk Model for Trajectory Data0 aPrivacy Risk Model for Trajectory Data  a125–1403 aTime sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacy-preserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users with a high degree of certainty. These methods provide theoretical worst-case privacy risks as measures of the privacy protection that they offer. However, often with many real-world data the worst-case scenario is too pessimistic and does not provide a realistic view of the privacy risks: the real probability of re-identification is often much lower than the theoretical worst-case risk. In this paper we propose a novel empirical risk model for privacy which, in relation to the cost of privacy attacks, demonstrates better the practical risks associated with a privacy preserving data release. We show detailed evaluation of the proposed risk model by using k-anonymised real-world mobility data.1 aBasu, Anirban1 aMonreale, Anna1 aCorena, Juan Camilo1 aGiannotti, Fosca1 aPedreschi, Dino1 aKiyomoto, Shinsaku1 aMiyake, Yutaka1 aYanagihara, Tadashi1 aTrasarti, Roberto  uhttp://dx.doi.org/10.1007/978-3-662-43813-8_901574nas a2200157   4500008003900000245006200039210006000101490000700161520105400168100001901222700001801241700002301259700002101282700002001303856009301323       2014                          d00aPrivacy-by-Design in Big Data Analytics and Social Mining0 aPrivacybyDesign in Big Data Analytics and Social Mining0 v103 aPrivacy is ever-growing concern in our society and is becoming a fundamental aspect to take into account when one wants to use, publish and analyze data involving human personal sensitive information. Unfortunately, it is increasingly hard to transform the data in a way that it protects sensitive information: we live in the era of big data characterized by unprecedented opportunities to sense, store and analyze social data describing human activities in great detail and resolution. As a result, privacy preservation simply cannot be accomplished by de-identification alone. In this paper, we propose the privacy-by-design paradigm to develop technological frameworks for countering the threats of undesirable, unlawful effects of privacy violation, without obstructing the knowledge discovery opportunities of social mining and big data analytical technologies. Our main idea is to inscribe privacy protection into the knowledge discovery technology by design, so that the analysis incorporates the relevant privacy requirements from the start.1 aMonreale, Anna1 aRinzivillo, S1 aPratesi, Francesca1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/privacy-design-big-data-analytics-and-social-mining01411nas a2200133   4500008004100000245008100041210006900122260001900191520090300210100002001113700002001133700001901153856010501172       2014                        eng d00aProcess mining event logs from FLOSS data: state of the art and perspectives0 aProcess mining event logs from FLOSS data state of the art and p  bSpringer, Cham3 aFree/Libre Open Source Software (FLOSS) is a phenomenon that has undoubtedly triggered extensive research endeavors. At the heart of these initiatives is the ability to mine data from FLOSS repositories with the hope of revealing empirical evidence to answer existing questions on the FLOSS development process. In spite of the success produced with existing mining techniques, emerging questions about FLOSS data require alternative and more appropriate ways to explore and analyse such data.

In this paper, we explore a different perspective called process mining. Process mining has been proved to be successful in terms of tracing and reconstructing process models from data logs (event logs). The chief objective of our analysis is threefold. We aim to achieve: (1) conformance to predefined models; (2) discovery of new model patterns; and, finally, (3) extension to predefined models.

1 aMukala, Patrick1 aCerone, Antonio1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/process-mining-event-logs-floss-data-state-art-and-perspectives00505nas a2200145   4500008004100000245008100041210006900122100001800191700002300209700001700232700002100249700002000270700002100290856004800311       2014                        eng d00aThe purpose of motion: Learning activities from Individual Mobility Networks0 apurpose of motion Learning activities from Individual Mobility N1 aRinzivillo, S1 aGabrielli, Lorenzo1 aNanni, Mirco1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1109/DSAA.2014.705809001836nas a2200157   4500008004100000245003800041210003500079300001400114490000700128520138300135100002201518700002401540700001701564700002501581856007201606       2014                        eng d00aOn quantified linear implications0 aquantified linear implications  a301–3250 v713 aA Quantified Linear Implication (QLI) is an inclusion query over two polyhedral sets, with a quantifier string that specifies which variables are existentially quantified and which are universally quantified. Equivalently, it can be viewed as a quantified implication of two systems of linear inequalities. In this paper, we provide a 2-person game semantics for the QLI problem, which allows us to explore the computational complexities of several of its classes. More specifically, we prove that the decision problem for QLIs with an arbitrary number of quantifier alternations is PSPACE-hard. Furthermore, we explore the computational complexities of several classes of 0, 1, and 2-quantifier alternation QLIs. We observed that some classes are decidable in polynomial time, some are NP-complete, some are coNP-hard and some are  ΠP2Π2P -hard. We also establish the hardness of QLIs with 2 or more quantifier alternations with respect to the first quantifier in the quantifier string and the number of quantifier alternations. All the proofs that we provide for polynomially solvable problems are constructive, i.e., polynomial-time decision algorithms are devised that utilize well-known procedures. QLIs can be utilized as powerful modelling tools for real-life applications. Such applications include reactive systems, real-time schedulers, and static program analyzers.1 aEirinakis, Pavlos1 aRuggieri, Salvatore1 aSubramani, K1 aWojciechowski, Piotr  uhttps://kdd.isti.cnr.it/publications/quantified-linear-implications02340nas a2200169   4500008004100000245004200041210003800083300001100121490000600132520186000138100002401998700002002022700001802042700002102060700002002081856006902101       2014                        eng d00aThe retail market as a complex system0 aretail market as a complex system  a1–270 v33 aAim of this paper is to introduce the complex system perspective into retail market analysis. Currently, to understand the retail market means to search for local patterns at the micro level, involving the segmentation, separation and profiling of diverse groups of consumers. In other contexts, however, markets are modelled as complex systems. Such strategy is able to uncover emerging regularities and patterns that make markets more predictable, e.g. enabling to predict how much a country’s GDP will grow. Rather than isolate actors in homogeneous groups, this strategy requires to consider the system as a whole, as the emerging pattern can be detected only as a result of the interaction between its self-organizing parts. This assumption holds also in the retail market: each customer can be seen as an independent unit maximizing its own utility function. As a consequence, the global behaviour of the retail market naturally emerges, enabling a novel description of its properties, complementary to the local pattern approach. Such task demands for a data-driven empirical framework. In this paper, we analyse a unique transaction database, recording the micro-purchases of a million customers observed for several years in the stores of a national supermarket chain. We show the emergence of the fundamental pattern of this complex system, connecting the products’ volumes of sales with the customers’ volumes of purchases. This pattern has a number of applications. We provide three of them. By enabling us to evaluate the sophistication of needs that a customer has and a product satisfies, this pattern has been applied to the task of uncovering the hierarchy of needs of the customers, providing a hint about what is the next product a customer could be interested in buying and predicting in which shop she is likely to go to buy it.1 aPennacchioli, Diego1 aCoscia, Michele1 aRinzivillo, S1 aGiannotti, Fosca1 aPedreschi, Dino  uhttp://link.springer.com/article/10.1140/epjds/s13688-014-0033-x01299nas a2200169   4500008004100000245006600041210006600107260003800173300001400211520076600225100002300991700001901014700001801033700002001051700002101071856003701092       2014                        eng d00aRetrieving Points of Interest from Human Systematic Movements0 aRetrieving Points of Interest from Human Systematic Movements  bSpringer International Publishing  a294–3083 aHuman mobility analysis is emerging as a more and more fundamental task to deeply understand human behavior. In the last decade these kind of studies have become feasible thanks to the massive increase in availability of mobility data. A crucial point, for many mobility applications and analysis, is to extract interesting locations for people. In this paper, we propose a novel methodology to retrieve efficiently significant places of interest from movement data. Using car drivers’ systematic movements we mine everyday interesting locations, that is, places around which people life gravitates. The outcomes show the empirical evidence that these places capture nearly the whole mobility even though generated only from systematic movements abstractions.1 aGuidotti, Riccardo1 aMonreale, Anna1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/node/63602438nas a2200157   4500008004100000245008400041210006900125300000600194490000600200520195300206100002002159700002102179700002102200700002002221856003902241       2014                        eng d00aUncovering Hierarchical and Overlapping Communities with a Local-First Approach0 aUncovering Hierarchical and Overlapping Communities with a Local  a60 v93 aCommunity discovery in complex networks is the task of organizing a network’s structure by grouping together nodes related to each other. Traditional approaches are based on the assumption that there is a global-level organization in the network. However, in many scenarios, each node is the bearer of complex information and cannot be classified in disjoint clusters. The top-down global view of the partition approach is not designed for this. Here, we represent this complex information as multiple latent labels, and we postulate that edges in the networks are created among nodes carrying similar labels. The latent labels are the communities a node belongs to and we discover them with a simple local-first approach to community discovery. This is achieved by democratically letting each node vote for the communities it sees surrounding it in its limited view of the global system, its ego neighborhood, using a label propagation algorithm, assuming that each node is aware of the label it shares with each of its connections. The local communities are merged hierarchically, unveiling the modular organization of the network at the global level and identifying overlapping groups and groups of groups. We tested this intuition against the state-of-the-art overlapping community discovery and found that our new method advances in the chosen scenarios in the quality of the obtained communities. We perform a test on benchmark and on real-world networks, evaluating the quality of the community coverage by using the extracted communities to predict the metadata attached to the nodes, which we consider external information about the latent labels. We also provide an explanation about why real-world networks contain overlapping communities and how our logic is able to capture them. Finally, we show how our method is deterministic, is incremental, and has a limited time complexity, so that it can be used on real-world scale networks.1 aCoscia, Michele1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttp://doi.acm.org/10.1145/262951102456nas a2200205   4500008003900000020002200039245014900061210006900210260002300279520172700302100002202029700002302051700002102074700001902095700001702114700002002131700001902151700002302170856005702193       2014                          d  a978-88-8467-874-400aUse of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach0 aUse of mobile phone data to estimate mobility flows Measuring ur  aCagliari c06/20143 aThe Big Data, originating from the digital breadcrumbs of human activi-
ties, sensed as a by-product of the technologies that we use for our daily activities, let
us to observe the individual and collective behavior of people at an unprecedented
detail. Many dimensions of our social life have big data “proxies”, as the mobile
calls data for mobility. In this paper we investigate to what extent such ”big data”,
in integration with administrative ones, could be a support in producing reliable and
timely estimates of inter-city mobility. The study has been jointly developed by Is-
tat, CNR, University of Pisa in the range of interest of the “Commssione di studio
avente il compito di orientare le scelte dellIstat sul tema dei Big Data ”. In an on-
going project at ISTAT, called “Persons and Places” – based on an integration of
administrative data sources, it has been produced a first release of Origin Destina-
tion matrix – at municipality level – assuming that the places of residence and that
of work (or study) be the terminal points of usual individual mobility for work or
study. The coincidence between the city of residence and that of work (or study) –
is considered as a proxy of the absence of intercity mobility for a person (we define
him a static resident). The opposite case is considered as a proxy of presence of mo-
bility (the person is a dynamic resident: commuter or embedded). As administrative
data do not contain information on frequency of the mobility, the idea is to specify
an estimate method, using calling data as support, to define for each municipality the
stock of standing residents, embedded city users and daily city users (commuters)1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aGiannotti, Fosca1 aMilli, Letizia1 aNanni, Mirco1 aPedreschi, Dino1 aVivio, Roberta1 aGarofalo, Giuseppe  uhttp://www.sis2014.it/proceedings/allpapers/3026.pdf01202nas a2200145   4500008003900000245006500039210006500104520069100169100002300860700002200883700002100905700001700926700001800943856009500961       2014                          d00aUse of mobile phone data to estimate visitors mobility flows0 aUse of mobile phone data to estimate visitors mobility flows3 aBig Data originating from the digital breadcrumbs of human activities,
sensed as by-product of the technologies that we use for our daily activities, allows
us to observe the individual and collective behavior of people at an unprecedented
detail. Many dimensions of our social life have big data “proxies”, such as the mo-
bile calls data for mobility. In this paper we investigate to what extent data coming
from mobile operators could be a support in producing reliable and timely estimates
of intra-city mobility flows. The idea is to define an estimation method based on
calling data to characterize the mobility habits of visitors at the level of a single
municipality1 aGabrielli, Lorenzo1 aFurletti, Barbara1 aGiannotti, Fosca1 aNanni, Mirco1 aRinzivillo, S  uhttp://www.di.unipi.it/mokmasd/symposium-2014/preproceedings/GabrielliEtAl-mokmasd2014.pdf01114nas a2200121   4500008004100000245006700041210006400108300001300172490000600185520073100191100002400922856004600946       2014                        eng d00aUsing t-closeness anonymity to control for non-discrimination.0 aUsing tcloseness anonymity to control for nondiscrimination  a99–1290 v73 aWe investigate the relation between t-closeness, a well-known model of data anonymization
against attribute disclosure, and α-protection, a model of the social discrimination hidden in
data. We show that t-closeness implies bdf (t)-protection, for a bound function bdf () depending on
the discrimination measure f() at hand. This allows us to adapt inference control methods, such
as the Mondrian multidimensional generalization technique and the Sabre bucketization and redistribution
framework, to the purpose of non-discrimination data protection. The parallel between
the two analytical models raises intriguing issues on the interplay between data anonymization and
non-discrimination research in data protection.1 aRuggieri, Salvatore  uhttp://dl.acm.org/citation.cfm?id=287062300525nas a2200133   4500008003900000245007200039210006900111260002800180100002200208700002300230700001800253700001800271856010200289       2013                          d00aAnalysis of GSM Calls Data for Understanding User Mobility Behavior0 aAnalysis of GSM Calls Data for Understanding User Mobility Behav  aSanta Clara, California1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aRenso, Chiara1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/analysis-gsm-calls-data-understanding-user-mobility-behavior00465nas a2200133   4500008003900000245006300039210006200102260000900164490000600173100002800179700001900207700001800226856008700244       2013                          d00aAssessing the Attractiveness of Places with Movement Data 0 aAssessing the Attractiveness of Places with Movement Data  c20130 v41 aFurtado, André Salvaro1 aFileto, Renato1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/assessing-attractiveness-places-movement-data00515nas a2200121   4500008003900000245007700039210006900116100002200185700002300207700001800230700003800248856010700286       2013                          d00aAverage Speed Estimation For Road Networks Based On GPS Raw Trajectories0 aAverage Speed Estimation For Road Networks Based On GPS Raw Traj1 aBarbosa, Ivanildo1 aCasanova, Marco, A1 aRenso, Chiara1 aMacêdo, José Antônio Fernandes  uhttps://kdd.isti.cnr.it/publications/average-speed-estimation-road-networks-based-gps-raw-trajectories02195nas a2200289   4500008004100000022001400041245005900055210005800114260000900172300001100181490000600192520133700198100001901535700002201554700002101576700002001597700002001617700002801637700001901665700002101684700002101705700002601726700002401752700001701776700002001793856009201813       2013                        eng d  a1932-620300aAwareness and learning in participatory noise sensing.0 aAwareness and learning in participatory noise sensing  c2013  ae816380 v83 a<p>The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.</p>1 aBecker, Martin1 aCaminiti, Saverio1 aFiorella, Donato1 aFrancis, Louise1 aGravino, Pietro1 aHaklay, Mordechai, Muki1 aHotho, Andreas1 aLoreto, Vittorio1 aMueller, Juergen1 aRicchiuti, Ferdinando1 aServedio, Vito, D P1 aSirbu, Alina1 aTria, Francesca  uhttps://kdd.isti.cnr.it/publications/awareness-and-learning-participatory-noise-sensing00562nas a2200145   4500008003900000245008500039210006900124260001400193100001900207700001900226700001900245700002400264700001800288856011000306       2013                          d00aBaquara: A Holistic Ontological Framework for Movement Analysis with Linked Data0 aBaquara A Holistic Ontological Framework for Movement Analysis w  aHong Kong1 aFileto, Renato1 aKrger, Marcelo1 aPelekis, Nikos1 aTheodoridis, Yannis1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/baquara-holistic-ontological-framework-movement-analysis-linked-data00492nas a2200145   4500008004100000245006800041210006700109300001200176490000700188100001700195700002100212700002400233700002000257856006900277       2013                        eng d00aCohesion, consensus and extreme information in opinion dynamics0 aCohesion consensus and extreme information in opinion dynamics  a13500350 v161 aSirbu, Alina1 aLoreto, Vittorio1 aServedio, Vito, D P1 aTria, Francesca  uhttp://www.worldscientific.com/doi/abs/10.1142/S021952591350035500483nas a2200145   4500008004100000245005100041210005100092260000900143100002100152700002000173700001800193700002000211700002100231856008500252       2013                        eng d00aComparing General Mobility and Mobility by Car0 aComparing General Mobility and Mobility by Car  cSept1 aPappalardo, Luca1 aSimini, Filippo1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/comparing-general-mobility-and-mobility-car00552nas a2200133   4500008003900000245008400039210006900123100001900192700001800211700002600229700002900255700002500284856010900309       2013                          d00aCONSTAnT - A Conceptual Data Model for Semantic Trajectories of Moving Objects 0 aCONSTAnT A Conceptual Data Model for Semantic Trajectories of Mo1 aBogorny, Vania1 aRenso, Chiara1 aAquino, Artur Ribeiro1 aSiqueira, Fernando Lucca1 aAlvares, Luis Otavio  uhttps://kdd.isti.cnr.it/publications/constant-conceptual-data-model-semantic-trajectories-moving-objects00296nas a2200097   4500008004100000245004400041210004300085260000900128100002400137856003700161       2013                        eng d00aData Anonymity Meets Non-discrimination0 aData Anonymity Meets Nondiscrimination  bIEEE1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/node/63100369nas a2200133   4500008004100000245003600041210003200077260001300109300001300122100002000135700002400155700001900179856003700198       2013                        eng d00aThe discovery of discrimination0 adiscovery of discrimination  bSpringer  a91–1081 aPedreschi, Dino1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://kdd.isti.cnr.it/node/63400441nas a2200133   4500008004100000245007600041210006900117300001600186490000700202100001800209700002400227700001900251856003700270       2013                        eng d00aDiscrimination discovery in scientific project evaluation: A case study0 aDiscrimination discovery in scientific project evaluation A case  a6064–60790 v401 aRomei, Andrea1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://kdd.isti.cnr.it/node/63200394nas a2200121   4500008003900000245004400039210004300083260000900126100002100135700001500156700002000171856008100191       2013                          d00aEfficient GPU-based skyline computation0 aEfficient GPUbased skyline computation  c20131 aBoeg, Kenneth, S1 aIra Assent1 aMagnani, Matteo  uhttps://kdd.isti.cnr.it/publications/efficient-gpu-based-skyline-computation00410nas a2200109   4500008004100000245008700041210006900128100001800197700002100215700002000236856004400256       2013                        eng d00a"Engine Matters": {A} First Large Scale Data Driven Study on Cyclists' Performance0 aEngine Matters A First Large Scale Data Driven Study on Cyclists1 aCintia, Paolo1 aPappalardo, Luca1 aPedreschi, Dino  uhttp://dx.doi.org/10.1109/ICDMW.2013.4101933nas a2200169   4500008003900000245004700039210004600086300001200132490000700144520146600151100002501617700002001642700002101662700001901683700002001702856004101722       2013                          d00aEvolving networks: Eras and turning points0 aEvolving networks Eras and turning points  a27–480 v173 aWithin the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure (derived from the Jaccard coefficient) between two temporal snapshots of the network, able to detect the turning points at the beginning of the eras. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks and null models, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset, a collaboration graph extracted from a cinema database, and a network extracted from a database of terrorist attacks; we illustrate how the discovered temporal clustering highlights the crucial moments when the networks witnessed profound changes in their structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttp://dx.doi.org/10.3233/IDA-12056600477nas a2200133   4500008003900000245005700039210005700096100002400153700002000177700001800197700002000215700002100235856008700256       2013                          d00aExplaining the PRoduct Range Effect in Purchase Data0 aExplaining the PRoduct Range Effect in Purchase Data1 aPennacchioli, Diego1 aCoscia, Michele1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/explaining-product-range-effect-purchase-data00445nas a2200133   4500008004100000245005900041210005700100100001800157700002200175700003400197700001800231700002000249856004200269       2013                        eng d00aA Gravity Model for Speed Estimation over Road Network0 aGravity Model for Speed Estimation over Road Network1 aCintia, Paolo1 aTrasarti, Roberto1 ade Macêdo, José, Antônio F1 aAlmada, Livia1 aFereira, Camila  uhttp://dx.doi.org/10.1109/MDM.2013.8300550nas a2200157   4500008004100000245009600041210006900137300001400206490000700220100001800227700002100245700003400266700002200300700002200322856004800344       2013                        eng d00aHow you move reveals who you are: understanding human behavior by analyzing trajectory data0 aHow you move reveals who you are understanding human behavior by  a331–3620 v371 aRenso, Chiara1 aBaglioni, Miriam1 ade Macêdo, José, Antônio F1 aTrasarti, Roberto1 aWachowicz, Monica  uhttp://dx.doi.org/10.1007/s10115-012-0511-z00432nas a2200121   4500008003900000245005500039210005500094260001600149100001800165700002200183700001800205856008700223       2013                          d00aInferring human activities from GPS tracks UrbComp0 aInferring human activities from GPS tracks UrbComp  aChicago USA1 aCintia, Paolo1 aFurletti, Barbara1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/inferring-human-activities-gps-tracks-urbcomp00283nas a2200097   4500008004100000245003400041210003400075260001500109100002400124856003700148       2013                        eng d00aLearning from polyhedral sets0 aLearning from polyhedral sets  bAAAI Press1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/node/63300434nas a2200121   4500008003900000245005600039210005600095260000900151100002100160700002100181700002000202856009000222       2013                          d00aMeasuring tie strength in multidimensional networks0 aMeasuring tie strength in multidimensional networks  c20131 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/measuring-tie-strength-multidimensional-networks00325nas a2200085   4500008004100000245007000041210006800111100002300179856003700202       2013                        eng d00aMobility Ranking - Human Mobility Analysis Using Ranking Measures0 aMobility Ranking Human Mobility Analysis Using Ranking Measures1 aGuidotti, Riccardo  uhttps://kdd.isti.cnr.it/node/64300642nas a2200157   4500008003900000245009600039210006900135260001400204100002000218700003800238700002600276700001800302700002400320700002200344856011800366       2013                          d00aMob-Warehouse: A semantic approach for mobility analysis with a Trajectory Data Ware- house0 aMobWarehouse A semantic approach for mobility analysis with a Tr  aHong Kong1 aWagner, Ricardo1 aMacêdo, José Antônio Fernandes1 aRaffaetà, Alessandra1 aRenso, Chiara1 aRoncato, Alessandro1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/publications/mob-warehouse-semantic-approach-mobility-analysis-trajectory-data-ware-house02277nas a2200193   4500008004100000245004800041210004600089260001900135520169200154100001701846700002201863700002201885700002301907700002401930700002101954700001901975700001801994856007102012       2013                        eng d00aMP4-A Project: Mobility Planning For Africa0 aMP4A Project Mobility Planning For Africa  aCambridge, USA3 aThis project aims to create a tool that uses mobile phone transaction (trajectory) data that will be able to address transportation related challenges, thus allowing promotion and facilitation of sustainable urban mobility planning in Third World countries. The proposed tool is a transport demand model for Ivory Coast, with emphasis on its major urbanization Abidjan. The consortium will bring together available data from the internet, and integrate these with the mobility data obtained from the mobile phones in order to build the best possible transport model. A transport model allows an understanding of current and future infrastructure requirements in Ivory Coast. As such, this project will provide the first proof of concept. In this context, long-term analysis of individual call traces will be performed to reconstruct systematic movements, and to infer an origin-destination matrix. A similar process will be performed using the locations of caller and recipient of phone calls, enabling the comparison of socio-economic ties vs. mobility. The emerging links between different areas will be used to build an effective map to optimize regional border definitions and road infrastructure from a mobility perspective. Finally, we will try to build specialized origin-destination matrices for specific categories of population. Such categories will be inferred from data through analysis of calling behaviours, and will also be used to characterize the population of different cities. The project also includes a study of data compliance with distributions of standard measures observed in literature, including distribution of calls, call durations and call network features.1 aNanni, Mirco1 aTrasarti, Roberto1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aVan Der Mede, Peter1 aDe Bruijn, Joost1 ade Romph, Erik1 aBruil, Gerard  uhttp://perso.uclouvain.be/vincent.blondel/netmob/2013/D4D-book.pdf01161nas a2200145   4500008003900000245004100039210003800080260000900118520071500127100002000842700001900862700002100881700002100902856009200923       2013                          d00aOn multidimensional network measures0 amultidimensional network measures  c20133 aNetworks, i.e., sets of interconnected entities, are ubiquitous,
spanning disciplines as diverse as sociology, biology and computer science.
The recent availability of large amounts of network data has thus
provided a unique opportunity to develop models and analysis tools applicable
to a wide range of scenarios. However, real-world phenomena are
often more complex than existing graph data models. One relevant example
concerns the numerous types of social relationships (or edges) that
can be present between individuals in a social network. In this short paper
we present a unified model and a set of measures recently developed
to represent and analyze network data with multiple types of edges.1 aMagnani, Matteo1 aMonreale, Anna1 aRossetti, Giulio1 aGiannotti, Fosca  uhttps://www.researchgate.net/publication/256194479_On_multidimensional_network_measures00461nas a2200133   4500008004100000245006500041210006500106300001100171100001700182700002100199700002400220700002000244856006300264       2013                        eng d00aOpinion dynamics with disagreement and modulated information0 aOpinion dynamics with disagreement and modulated information  a1–201 aSirbu, Alina1 aLoreto, Vittorio1 aServedio, Vito, D P1 aTria, Francesca  uhttp://link.springer.com/article/10.1007/s10955-013-0724-x00516nas a2200121   4500008003900000245008700039210006900126100002200195700002300217700001800240700001800258856011800276       2013                          d00aPisa Tourism fluxes Observatory: deriving mobility indicators from GSM call habits0 aPisa Tourism fluxes Observatory deriving mobility indicators fro1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aRenso, Chiara1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/pisa-tourism-fluxes-observatory-deriving-mobility-indicators-gsm-call-habits01416nas a2200181   4500008004100000245005400041210005300095260002000148520088200168100002301050700001901073700002101092700001801113700002001131700002301151700002301174856003701197       2013                        eng d00aPrivacy-Aware Distributed Mobility Data Analytics0 aPrivacyAware Distributed Mobility Data Analytics  aRoccella Jonica3 aWe propose an approach to preserve privacy in an analytical processing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by individual vehicles and shipped to a central server. Movement data are sensitive because they may describe typical movement behaviors and therefore be used for re-identification of individuals in a database. We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential privacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the effectiveness of our approach also in terms of data utility preserved by the data transformation.
1 aPratesi, Francesca1 aMonreale, Anna1 aWang, Hui, Wendy1 aRinzivillo, S1 aPedreschi, Dino1 aAndrienko, Gennady1 aAndrienko, Natalia  uhttps://kdd.isti.cnr.it/node/61501685nas a2200241   4500008003900000020002200039245006100061210006000122260003800182300001200220520094300232100001901175700002101194700002301215700001801238700002001256700002301276700002301299700002501322700002401347700002101371856005101392       2013                          d  a978-3-319-00614-700aPrivacy-Preserving Distributed Movement Data Aggregation0 aPrivacyPreserving Distributed Movement Data Aggregation  bSpringer International Publishing  a225-2453 aWe propose a novel approach to privacy-preserving analytical processing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by individual vehicles and shipped to a central server. Movement data are sensitive because people’s whereabouts have the potential to reveal intimate personal traits, such as religious or sexual preferences, and may allow re-identification of individuals in a database. We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential privacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the effectiveness of our approach also in terms of data utility preserved by the data transformation.1 aMonreale, Anna1 aWang, Hui, Wendy1 aPratesi, Francesca1 aRinzivillo, S1 aPedreschi, Dino1 aAndrienko, Gennady1 aAndrienko, Natalia1 aVandenbroucke, Danny1 aBucher, Bénédicte1 aCrompvoets, Joep  uhttp://dx.doi.org/10.1007/978-3-319-00615-4_1301777nas a2200145   4500008003900000245008900039210006900128520121300197100002101410700002201431700001901453700002001472700002101492856011801513       2013                          d00aPrivacy-Preserving Mining of Association Rules From Outsourced Transaction Databases0 aPrivacyPreserving Mining of Association Rules From Outsourced Tr3 aSpurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-a-service. A company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the items and the association rules of the outsourced database are considered private property of the corporation (data owner). To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing the association rule mining task within a corporate privacy-preserving framework. We propose an attack model based on background knowledge and devise a scheme for privacy preserving outsourced mining. Our scheme ensures that each transformed item is indistinguishable with respect to the attacker's background knowledge, from at least k-1 other transformed items. Our comprehensive experiments on a very large and real transaction database demonstrate that our techniques are effective, scalable, and protect privacy.1 aGiannotti, Fosca1 aLakshmanan, L V S1 aMonreale, Anna1 aPedreschi, Dino1 aWang, Hui, Wendy  uhttps://kdd.isti.cnr.it/publications/privacy-preserving-mining-association-rules-outsourced-transaction-databases00505nas a2200133   4500008003900000245005400039210005100093100003200144700002300176700003100199700003800230700001800268856008500286       2013                          d00aA Proactive Ap- plication to Monitor Truck Fleets0 aProactive Ap plication to Monitor Truck Fleets1 aAlbuquerque, Fabio Da Costa1 aCasanova, Marco, A1 aCarvalho, Marcelo Tilio, M1 aMacêdo, José Antônio Fernandes1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/proactive-ap-plication-monitor-truck-fleets02131nas a2200169   4500008003900000245002500039210002500064300001400089520168900103100001901792700001901811700002101830700002101851700002001872700002501892856004401917       2013                          d00aQuantification Trees0 aQuantification Trees  a528–5363 aIn many applications there is a need to monitor how a population is distributed across different classes, and to track the changes in this distribution that derive from varying circumstances, an example such application is monitoring the percentage (or "prevalence") of unemployed people in a given region, or in a given age range, or at different time periods. When the membership of an individual in a class cannot be established deterministically, this monitoring activity requires classification. However, in the above applications the final goal is not determining which class each individual belongs to, but simply estimating the prevalence of each class in the unlabeled data. This task is called quantification. In a supervised learning framework we may estimate the distribution across the classes in a test set from a training set of labeled individuals. However, this may be sub optimal, since the distribution in the test set may be substantially different from that in the training set (a phenomenon called distribution drift). So far, quantification has mostly been addressed by learning a classifier optimized for individual classification and later adjusting the distribution it computes to compensate for its tendency to either under-or over-estimate the prevalence of the class. In this paper we propose instead to use a type of decision trees (quantification trees) optimized not for individual classification, but directly for quantification. Our experiments show that quantification trees are more accurate than existing state-of-the-art quantification methods, while retaining at the same time the simplicity and understandability of the decision tree framework.1 aMilli, Letizia1 aMonreale, Anna1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino1 aSebastiani, Fabrizio  uhttp://dx.doi.org/10.1109/ICDM.2013.12200562nas a2200145   4500008003900000245008700039210006900126490000700195100002300202700002300225700001400248700001800262700001900280856011700299       2013                          d00aScalable Analysis of Movement Data for Extracting and Exploring Significant Places0 aScalable Analysis of Movement Data for Extracting and Exploring 0 v191 aAndrienko, Gennady1 aAndrienko, Natalia1 aHunter, C1 aRinzivillo, S1 aWrobel, Stefan  uhttps://kdd.isti.cnr.it/publications/scalable-analysis-movement-data-extracting-and-exploring-significant-places00689nas a2200217   4500008003900000245004800039210004800087260001600135490000700151100002200158700002600180700001800206700002300224700002300247700001900270700001600289700002400305700003800329700001900367856008500386       2013                          d00aSemantic Trajectories Modeling and Analysis0 aSemantic Trajectories Modeling and Analysis  cAugust 20130 v451 aParent, Christine1 aSpaccapietra, Stefano1 aRenso, Chiara1 aAndrienko, Gennady1 aAndrienko, Natalia1 aBogorny, Vania1 aDamiani M L1 aGkoulalas-Divanis A1 aMacêdo, José Antônio Fernandes1 aPelekis, Nikos  uhttps://kdd.isti.cnr.it/publications/semantic-trajectories-modeling-and-analysis00526nas a2200133   4500008003900000245008000039210006900119490001400188100002000202700001800222700002100240700002000261856011100281       2013                          d00aSpatial and Temporal Evaluation of Network-based Analysis of Human Mobility0 aSpatial and Temporal Evaluation of Networkbased Analysis of Huma0 vto appear1 aCoscia, Michele1 aRinzivillo, S1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/spatial-and-temporal-evaluation-network-based-analysis-human-mobility00442nas a2200109   4500008003900000245006800039210006700107250001300174100002200187700001800209856010500227       2013                          d00aSpatio and Spatio-temporal Reasoning and Decision Support Tools0 aSpatio and Spatiotemporal Reasoning and Decision Support Tools  aspringer1 aWachowicz, Monica1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/spatio-and-spatio-temporal-reasoning-and-decision-support-tools00467nas a2200133   4500008003900000245005400039210005300093100002000146700002100166700002000187700001800207700002000225856008800245       2013                          d00aSpatio temporal keyword-queries in Social Networs0 aSpatio temporal keywordqueries in Social Networs1 aCozza, Vittoria1 aMessina, Antonio1 aMontesi, Danilo1 aArietta, Luca1 aMagnani, Matteo  uhttps://kdd.isti.cnr.it/publications/spatio-temporal-keyword-queries-social-networs00373nas a2200133   4500008004100000245002500041210002400066300000700090100001700097700002600114700001800140700001900158856006200177       2013                        eng d00aSpatio-Temporal Data0 aSpatioTemporal Data  a751 aNanni, Mirco1 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/spatio-temporal-data00487nas a2200133   4500008003900000245006600039210006300105100001700168700001700185700001800202700002200220700002000242856009100262       2013                          d00aA Study on Parameter Estimation for a Mining Flock Algorithm 0 aStudy on Parameter Estimation for a Mining Flock Algorithm1 aOng, Rebecca1 aNanni, Mirco1 aRenso, Chiara1 aWachowicz, Monica1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/study-parameter-estimation-mining-flock-algorithm00404nas a2200121   4500008003900000245004200039210004200081260002600123100002200149700001700171700001800188856007600206       2013                          d00aTailoring Moving Patterns to Contexts0 aTailoring Moving Patterns to Contexts  aLeuven, Belgium, 20131 aWachowicz, Monica1 aOng, Rebecca1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/tailoring-moving-patterns-contexts00453nas a2200145   4500008004100000245004600041210004200087100002400129700002100153700002100174700002000195700002100215700002000236856005100256       2013                        eng d00aThe Three Dimensions of Social Prominence0 aThree Dimensions of Social Prominence1 aPennacchioli, Diego1 aRossetti, Giulio1 aPappalardo, Luca1 aPedreschi, Dino1 aGiannotti, Fosca1 aCoscia, Michele  uhttp://dx.doi.org/10.1007/978-3-319-03260-3_2800549nas a2200169   4500008004100000245006800041210006600109300001200175490000700187100001800194700002100212700002700233700002000260700003000280700002200310856004700332       2013                        eng d00aTowards mega-modeling: a walk through data analysis experiences0 aTowards megamodeling a walk through data analysis experiences  a19–270 v421 aCeri, Stefano1 aPalpanas, Themis1 aValle, Emanuele, Della1 aPedreschi, Dino1 aFreytag, Johann-Christoph1 aTrasarti, Roberto  uhttp://doi.acm.org/10.1145/2536669.253667300577nas a2200169   4500008004100000245008100041210006900122100001700191700002200208700002200230700002300252700002400275700002100299700001900320700001800339856005000357       2013                        eng d00aTransportation Planning Based on {GSM} Traces: {A} Case Study on Ivory Coast0 aTransportation Planning Based on GSM Traces A Case Study on Ivor1 aNanni, Mirco1 aTrasarti, Roberto1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aVan Der Mede, Peter1 aDe Bruijn, Joost1 ade Romph, Erik1 aBruil, Gerard  uhttp://dx.doi.org/10.1007/978-3-319-04178-0_201029nas a2200169   4500008004100000245004700041210004500088300001200133490000800145520055800153100002100711700001800732700001400750700002000764700002100784856005400805       2013                        eng d00a{Understanding the patterns of car travel}0 aUnderstanding the patterns of car travel  a61–730 v2153 a{Are the patterns of car travel different from those of general human mobility? Based on a unique dataset consisting of the GPS trajectories of 10 million travels accomplished by 150,000 cars in Italy, we investigate how known mobility models apply to car travels, and illustrate novel analytical findings. We also assess to what extent the sample in our dataset is representative of the overall car mobility, and discover how to build an extremely accurate model that, given our GPS data, estimates the real traffic values as measured by road sensors.}1 aPappalardo, Luca1 aRinzivillo, S1 aQu, Zehui1 aPedreschi, Dino1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1140/epjst%252fe2013-01715-500535nas a2200145   4500008003900000245006700039210006600106300001200172100001800184700002900202700002300231700001800254700001900272856009800291       2013                          d00aWhere Have You Been Today? Annotating Trajectories with DayTag0 aWhere Have You Been Today Annotating Trajectories with DayTag  a467-4711 aRinzivillo, S1 aSiqueira, Fernando Lucca1 aGabrielli, Lorenzo1 aRenso, Chiara1 aBogorny, Vania  uhttps://kdd.isti.cnr.it/publications/where-have-you-been-today-annotating-trajectories-daytag00572nas a2200145   4500008003900000245007100039210006900110260002300179100001900202700002600221700002100247700001800268700003800286856010200324       2013                          d00aWhere Shall We Go Today? Planning Touristic Tours with TripBuilder0 aWhere Shall We Go Today Planning Touristic Tours with TripBuilde  aSan Francisco, USA1 aBrilhante, Igo1 aNardini, Franco Maria1 aPerego, Raffaele1 aRenso, Chiara1 aMacêdo, José Antônio Fernandes  uhttps://kdd.isti.cnr.it/publications/where-shall-we-go-today-planning-touristic-tours-tripbuilder00611nas a2200169   4500008004100000245005200041210005000093260000900143100002200152700002000174700002000194700002100214700002400235700001700259700002000276856014500296       2013                        eng d00aXTribe: a web-based social computation platform0 aXTribe a webbased social computation platform  bIEEE1 aCaminiti, Saverio1 aCicali, Claudio1 aGravino, Pietro1 aLoreto, Vittorio1 aServedio, Vito, D P1 aSirbu, Alina1 aTria, Francesca  uhttp://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6686061&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D668606100579nas a2200133   4500008003900000245009500039210006900134100002000203700002100223700002400244700002400268700002100292856013200313       2013                          d00aYou Know Because I Know”: a Multidimensional Network Approach to Human Resources Problem0 aYou Know Because I Know a Multidimensional Network Approach to H1 aCoscia, Michele1 aRossetti, Giulio1 aPennacchioli, Diego1 aCeccarelli, Damiano1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/you-know-because-i-know%E2%80%9D-multidimensional-network-approach-human-resources-problem00754nas a2200241   4500008004100000245007800041210006900119100001900188700002100207700001700228700002100245700001900266700001600285700002300301700001700324700001700341700002000358700002000378700002200398700002600420700001600446856005000462       2012                        eng d00aAn Agent-Based Model to Evaluate Carpooling at Large Manufacturing Plants0 aAgentBased Model to Evaluate Carpooling at Large Manufacturing P1 aBellemans, Tom1 aBothe, Sebastian1 aCho, Sungjin1 aGiannotti, Fosca1 aJanssens, Davy1 aKnapen, Luk1 aKörner, Christine1 aMay, Michael1 aNanni, Mirco1 aPedreschi, Dino1 aStange, Hendrik1 aTrasarti, Roberto1 aYasar, Ansar-Ul-Haque1 aWets, Geert  uhttp://dx.doi.org/10.1016/j.procs.2012.08.00100515nas a2200157   4500008003900000245004500039210004400084260002100128100002200149700002200171700002300193700001800216700002100234700002100255856008100276       2012                          d00aAnalisi di Mobilita' con dati eterogenei0 aAnalisi di Mobilita con dati eterogenei  aPisabISTI - CNR1 aFurletti, Barbara1 aTrasarti, Roberto1 aGabrielli, Lorenzo1 aRinzivillo, S1 aPappalardo, Luca1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/analisi-di-mobilita-con-dati-eterogenei01580nas a2200145   4500008003900000245008100039210006900120260000900189520106400198100001601262700001901278700001301297700001401310856011001324       2012                          d00aAnonymity: a Comparison between the Legal and Computer Science Perspectives.0 aAnonymity a Comparison between the Legal and Computer Science Pe  c20123 aPrivacy preservation has emerged as a major challenge in ICT. One possible solution for enforcing privacy is to guarantee anonymity. Indeed, according to international regulations, no restriction is applied to the handling of anonymous data. Consequently, in the past years the notion of anonymity has been extensively studied by two different communities: Law researchers and professionals that propose definitions of privacy regulations, and Computer Scientists attempting to provide technical solutions for enforcing the legal requirements.

In this contribution we address the problem with an interdisciplinary approach, in the aim to encourage the reciprocal understanding and collaboration between researchers in the two areas. To achieve this, we compare the different notions of anonymity provided in the European data protection Law with the formal models proposed in Computer Science. This analysis allows us to identify the main similarities and differences between the two points of view, hence highlighting the need for a joint research effort.1 aMascetti, S1 aMonreale, Anna1 aRicci, A1 aGerino, A  uhttps://kdd.isti.cnr.it/publications/anonymity-comparison-between-legal-and-computer-science-perspectives01748nas a2200169   4500008003900000245008300039210006900122260000900191520116900200100001001369700002101379700001901400700002001419700002101439700001101460856010701471       2012                          d00aAUDIO: An Integrity Auditing Framework of Outlier-Mining-as-a-Service Systems.0 aAUDIO An Integrity Auditing Framework of OutlierMiningasaService  c20123 aSpurred by developments such as cloud computing, there has been considerable recent interest in the data-mining-as-a-service paradigm. Users lacking in expertise or computational resources can outsource their data and mining needs to a third-party service provider (server). Outsourcing, however, raises issues about result integrity: how can the data owner verify that the mining results returned by the server are correct? In this paper, we present AUDIO, an integrity auditing framework for the specific task of distance-based outlier mining outsourcing. It provides efficient and practical verification approaches to check both completeness and correctness of the mining results. The key idea of our approach is to insert a small amount of artificial tuples into the outsourced data; the artificial tuples will produce artificial outliers and non-outliers that do not exist in the original dataset. The server’s answer is verified by analyzing the presence of artificial outliers/non-outliers, obtaining a probabilistic guarantee of correctness and completeness of the mining result. Our empirical results show the effectiveness and efficiency of our method.1 aR.Liu1 aWang, Hui, Wendy1 aMonreale, Anna1 aPedreschi, Dino1 aGiannotti, Fosca1 aGuo, W  uhttps://kdd.isti.cnr.it/publications/audio-integrity-auditing-framework-outlier-mining-service-systems01793nas a2200145   4500008003900000245007100039210006900110300001400179520131900193100001901512700002001531700001901551700002101570856005601591       2012                          d00aClassifying Trust/Distrust Relationships in Online Social Networks0 aClassifying TrustDistrust Relationships in Online Social Network  a552–5573 aOnline social networks are increasingly being used as places where communities gather to exchange information, form opinions, collaborate in response to events. An aspect of this information exchange is how to determine if a source of social information can be trusted or not. Data mining literature addresses this problem. However, if usually employs social balance theories, by looking at small structures in complex networks known as triangles. This has proven effective in some cases, but it under performs in the lack of context information about the relation and in more complex interactive structures. In this paper we address the problem of creating a framework for the trust inference, able to infer the trust/distrust relationships in those relational environments that cannot be described by using the classical social balance theory. We do so by decomposing a trust network in its ego network components and mining on this ego network set the trust relationships, extending a well known graph mining algorithm. We test our framework on three public datasets describing trust relationships in the real world (from the social media Epinions, Slash dot and Wikipedia) and confronting our results with the trust inference state of the art, showing better performances where the social balance theory fails.1 aBachi, Giacomo1 aCoscia, Michele1 aMonreale, Anna1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1109/SocialCom-PASSAT.2012.11500612nas a2200169   4500008003900000245006900039210006800108260000900176300001300185100001900198700002500217700002200242700001800264700003800282700002300320856009900343       2012                          d00aComeTogether: Discovering Communities of Places in Mobility Data0 aComeTogether Discovering Communities of Places in Mobility Data  c2012  a 268-2731 aBrilhante, Igo1 aBerlingerio, Michele1 aTrasarti, Roberto1 aRenso, Chiara1 aMacêdo, José Antônio Fernandes1 aCasanova, Marco, A  uhttps://kdd.isti.cnr.it/publications/cometogether-discovering-communities-places-mobility-data00477nas a2200133   4500008003900000245006100039210006100100100001900161700002100180700001700201700002000218700001800238856008700256       2012                          d00aData Science for Simulating the Era of Electric Vehicles0 aData Science for Simulating the Era of Electric Vehicles1 aJanssens, Davy1 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/data-science-simulating-era-electric-vehicles00502nas a2200133   4500008003900000245007000039210006800109260000900177100002000186700002100206700002100227700002000248856010000268       2012                          d00aDEMON: a Local-First Discovery Method for Overlapping Communities0 aDEMON a LocalFirst Discovery Method for Overlapping Communities  c20121 aCoscia, Michele1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/demon-local-first-discovery-method-overlapping-communities00430nas a2200121   4500008004100000245007000041210006800111100002000179700002100199700002100220700002000241856004700261       2012                        eng d00aDEMON: a local-first discovery method for overlapping communities0 aDEMON a localfirst discovery method for overlapping communities1 aCoscia, Michele1 aRossetti, Giulio1 aGiannotti, Fosca1 aPedreschi, Dino  uhttp://doi.acm.org/10.1145/2339530.233963001549nas a2200169   4500008003900000022001400039245005900053210005900112520102300171100001801194700002101212700001901233700002001252700002101272700002001293856006601313       2012                          d  a0933-187500aDiscovering the Geographical Borders of Human Mobility0 aDiscovering the Geographical Borders of Human Mobility3 aThe availability of massive network and mobility data from diverse domains has fostered the analysis of human behavior and interactions. Broad, extensive, and multidisciplinary research has been devoted to the extraction of non-trivial knowledge from this novel form of data. We propose a general method to determine the influence of social and mobility behavior over a specific geographical area in order to evaluate to what extent the current administrative borders represent the real basin of human movement. We build a network representation of human movement starting with vehicle GPS tracks and extract relevant clusters, which are then mapped back onto the territory, finding a good match with the existing administrative borders. The novelty of our approach is the focus on a detailed spatial resolution, we map emerging borders in terms of individual municipalities, rather than macro regional or national areas. We present a series of experiments to illustrate and evaluate the effectiveness of our approach.1 aRinzivillo, S1 aMainardi, Simone1 aPezzoni, Fabio1 aCoscia, Michele1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://link.springer.com/article/10.1007%2Fs13218-012-0181-800442nas a2200109   4500008004100000245009700041210006900138100002100207700002100228700002000249856006300269       2012                        eng d00a"How Well Do We Know Each Other?" Detecting Tie Strength in Multidimensional Social Networks0 aHow Well Do We Know Each Other Detecting Tie Strength in Multidi1 aPappalardo, Luca1 aRossetti, Giulio1 aPedreschi, Dino  uhttp://doi.ieeecomputersociety.org/10.1109/ASONAM.2012.18001756nas a2200157   4500008003900000020002200039245005600061210005600117260004900173520110400222100002201326700002301348700001801371700001801389856019101407       2012                          d  a978-1-4503-1542-500aIdentifying users profiles from mobile calls habits0 aIdentifying users profiles from mobile calls habits  aBeijing, ChinabACM New York, NY, USA ©20123 aThe huge quantity of positioning data registered by our mobile phones stimulates several research questions, mainly originating from the combination of this huge quantity of data with the extreme heterogeneity of the tracked user and the low granularity of the data. We propose a methodology to partition the users tracked by GSM phone calls into profiles like resident, commuters, in transit and tourists. The methodology analyses the phone calls with a combination of top-down and bottom up techniques where the top-down phase is based on a sequence of queries that identify some behaviors. The bottom-up is a machine learning phase to find groups of similar call behavior, thus refining the previous step. The integration of the two steps results in the partitioning of mobile traces into these four user categories that can be deeper analyzed, for example to understand the tourist movements in city or the traffic effects of commuters. An experiment on the identification of user profiles on a real dataset collecting call records from one month in the city of Pisa illustrates the methodology.1 aFurletti, Barbara1 aGabrielli, Lorenzo1 aRenso, Chiara1 aRinzivillo, S  uhttp://delivery.acm.org/10.1145/2350000/2346500/p17-furletti.pdf?ip=146.48.83.121&acc=ACTIVE%20SERVICE&CFID=166768290&CFTOKEN=58719386&__acm__=1357648050_e23771c2f6bd8feb96bd66b39294175d00475nas a2200121   4500008004100000245007700041210006900118100002200187700001900209700001700228700002100245856008700266       2012                        eng d00aIndividual Mobility Profiles: Methods and Application on Vehicle Sharing0 aIndividual Mobility Profiles Methods and Application on Vehicle 1 aTrasarti, Roberto1 aPinelli, Fabio1 aNanni, Mirco1 aGiannotti, Fosca  uhttp://sebd2012.dei.unipd.it/documents/188475/32d00b8a-8ead-4d97-923f-bd2f2cf6ddcb01411nas a2200157   4500008003900000245007400039210006900113300001400182520091000196100001701106700001901123700002001142700002601162700002101188856004401209       2012                          d00aInjecting Discrimination and Privacy Awareness Into Pattern Discovery0 aInjecting Discrimination and Privacy Awareness Into Pattern Disc  a360–3693 aData mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. Data mining comes with unprecedented opportunities and risks: a deeper understanding of human behavior and how our society works is darkened by a greater chance of privacy intrusion and unfair discrimination based on the extracted patterns and profiles. Although methods independently addressing privacy or discrimination in data mining have been proposed in the literature, in this context we argue that privacy and discrimination risks should be tackled together, and we present a methodology for doing so while publishing frequent pattern mining results. We describe a combined pattern sanitization framework that yields both privacy and discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion.1 aHajian, Sara1 aMonreale, Anna1 aPedreschi, Dino1 aDomingo-Ferrer, Josep1 aGiannotti, Fosca  uhttp://dx.doi.org/10.1109/ICDMW.2012.5101654nas a2200169   4500008004100000022001400041245009000055210006900145260001300214300001100227490000800238520105800246100001701304700002301321700001801344856012201362       2012                        eng d  a1611-753000aIntegrating heterogeneous gene expression data for gene regulatory network modelling.0 aIntegrating heterogeneous gene expression data for gene regulato  c2012 Jun  a95-1020 v1313 a<p>Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors' knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets.</p>1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin  uhttps://kdd.isti.cnr.it/publications/integrating-heterogeneous-gene-expression-data-gene-regulatory-network-modelling00371nas a2200121   4500008003900000022001400039245003800053210003800091490000700129100002200136700001900158856007200177       2012                          d  a1571-412800aKnowledge Discovery in Ontologies0 aKnowledge Discovery in Ontologies0 v161 aFurletti, Barbara1 aTurini, Franco  uhttp://iospress.metapress.com/content/765h53w41286p578/fulltext.pdf00515nas a2200121   4500008003900000245008900039210006900128260000900197100002800206700001900234700001800253856012200271       2012                          d00aM-Attract: Assessing Places Attractiveness by using Moving Objects Trajectories Data0 aMAttract Assessing Places Attractiveness by using Moving Objects  c20121 aFurtado, André Salvaro1 aFileto, Renato1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/m-attract-assessing-places-attractiveness-using-moving-objects-trajectories-data00381nas a2200121   4500008004100000245004100041210004000082100001800122700002700140700002000167700002200187856005000209       2012                        eng d00aMega-modeling for Big Data Analytics0 aMegamodeling for Big Data Analytics1 aCeri, Stefano1 aValle, Emanuele, Della1 aPedreschi, Dino1 aTrasarti, Roberto  uhttp://dx.doi.org/10.1007/978-3-642-34002-4_102055nas a2200169   4500008003900000245006600039210006500105260001200170490002200182520150900204100002501713700002001738700002101758700001901779700002001798856006701818       2012                          d00aMultidimensional networks: foundations of structural analysis0 aMultidimensional networks foundations of structural analysis  c10/20120 v Volume 15 / 20123 aComplex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. So far, network analysis has focused on the characterization and measurement of local and global properties of graphs, such as diameter, degree distribution, centrality, and so on. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens in monodimensional networks, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we present a solid repertoire of basic concepts and analytical measures, which take into account the general structure of multidimensional networks. We tested our framework on different real world multidimensional networks, showing the validity and the meaningfulness of the measures introduced, that are able to extract important and non-random information about complex phenomena in such networks. 1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttp://www.springerlink.com/content/f774289854430410/abstract/00498nas a2200133   4500008003900000245006600039210006600105260002200171100002000193700001800213700002000231700002100251856009200272       2012                          d00aOptimal Spatial Resolution for the Analysis of Human Mobility0 aOptimal Spatial Resolution for the Analysis of Human Mobility  aInstanbul, Turkey1 aCoscia, Michele1 aRinzivillo, S1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/optimal-spatial-resolution-analysis-human-mobility02529nas a2200181   4500008004100000022001400041245012200055210006900177260000900246300001100255490000600266520187200272100001702144700001902161700001802180700002302198856012602221       2012                        eng d  a1932-620300aRNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.0 aRNASeq vs dual and singlechannel microarray data sensitivity ana  c2012  ae509860 v73 a<p>With the fast development of high-throughput sequencing technologies, a new generation of genome-wide gene expression measurements is under way. This is based on mRNA sequencing (RNA-seq), which complements the already mature technology of microarrays, and is expected to overcome some of the latter's disadvantages. These RNA-seq data pose new challenges, however, as strengths and weaknesses have yet to be fully identified. Ideally, Next (or Second) Generation Sequencing measures can be integrated for more comprehensive gene expression investigation to facilitate analysis of whole regulatory networks. At present, however, the nature of these data is not very well understood. In this paper we study three alternative gene expression time series datasets for the Drosophila melanogaster embryo development, in order to compare three measurement techniques: RNA-seq, single-channel and dual-channel microarrays. The aim is to study the state of the art for the three technologies, with a view of assessing overlapping features, data compatibility and integration potential, in the context of time series measurements. This involves using established tools for each of the three different technologies, and technical and biological replicates (for RNA-seq and microarrays, respectively), due to the limited availability of biological RNA-seq replicates for time series data. The approach consists of a sensitivity analysis for differential expression and clustering. In general, the RNA-seq dataset displayed highest sensitivity to differential expression. The single-channel data performed similarly for the differentially expressed genes common to gene sets considered. Cluster analysis was used to identify different features of the gene space for the three datasets, with higher similarities found for the RNA-seq and single-channel microarray dataset.</p>1 aSirbu, Alina1 aKerr, Gráinne1 aCrane, Martin1 aRuskin, Heather, J  uhttps://kdd.isti.cnr.it/publications/rna-seq-vs-dual-and-single-channel-microarray-data-sensitivity-analysis-differential02994nas a2200205   4500008004100000245003100041210003100072300000800103490000800111520246000119100001902579700002102598700002102619700002402640700002102664700002202685700002302707700002102730856003702751       2012                        eng d00aSmart cities of the future0 aSmart cities of the future  a4810 v2143 aHere we sketch the rudiments of what constitutes a smart city which we define as a city in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies. We first sketch our vision defining seven goals which concern: developing a new understanding of urban problems; effective and feasible ways to coordinate urban technologies; models and methods for using urban data across spatial and temporal scales; developing new technologies for communication and dissemination; developing new forms of urban governance and organisation; defining critical problems relating to cities, transport, and energy; and identifying risk, uncertainty, and hazards in the smart city. To this, we add six research challenges: to relate the infrastructure of smart cities to their operational functioning and planning through management, control and optimisation; to explore the notion of the city as a laboratory for innovation; to provide portfolios of urban simulation which inform future designs; to develop technologies that ensure equity, fairness and realise a better quality of city life; to develop technologies that ensure informed participation and create shared knowledge for democratic city governance; and to ensure greater and more effective mobility and access to opportunities for urban populations. We begin by defining the state of the art, explaining the science of smart cities. We define six scenarios based on new cities badging themselves as smart, older cities regenerating themselves as smart, the development of science parks, tech cities, and technopoles focused on high technologies, the development of urban services using contemporary ICT, the use of ICT to develop new urban intelligence functions, and the development of online and mobile forms of participation. Seven project areas are then proposed: Integrated Databases for the Smart City, Sensing, Networking and the Impact of New Social Media, Modelling Network Performance, Mobility and Travel Behaviour, Modelling Urban Land Use, Transport and Economic Interactions, Modelling Urban Transactional Activities in Labour and Housing Markets, Decision Support as Urban Intelligence, Participatory Governance and Planning Structures for the Smart City. Finally we anticipate the paradigm shifts that will occur in this research and define a series of key demonstrators which we believe are important to progressing a science of smart cities.1 aBatty, Michael1 aAxhausen, Kay, W1 aGiannotti, Fosca1 aPozdnoukhov, Alexei1 aBazzani, Armando1 aWachowicz, Monica1 aOuzounis, Georgios1 aPortugali, Yuval  uhttps://kdd.isti.cnr.it/node/60100425nas a2200109   4500008003900000245006400039210006300103260001300166100001900179700002200198856009500220       2012                          d00aWhat else can be extracted from ontologies? Influence Rules0 aWhat else can be extracted from ontologies Influence Rules  bSpringer1 aTurini, Franco1 aFurletti, Barbara  uhttps://kdd.isti.cnr.it/publications/what-else-can-be-extracted-ontologies-influence-rules00382nas a2200097   4500008004100000245005200041210005200093100002900145700002100174856008900195       2012                        eng d00aWine and Food Tourism First European Conference0 aWine and Food Tourism First European Conference1 aRomano, Maria, Francesca1 aNatilli, Michela  uhttps://kdd.isti.cnr.it/publications/wine-and-food-tourism-first-european-conference01936nas a2200265   4500008004100000022001400041245005600055210005500111260001300166300001200179490000600191520117600197100002001373700001901393700002101412700001501433700001601448700001801464700001801482700002001500700001801520700002501538710002201563856008501585       2012                        eng d  a1548-710500aWisdom of crowds for robust gene network inference.0 aWisdom of crowds for robust gene network inference  c2012 Aug  a796-8040 v93 a<p>Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.</p>1 aMarbach, Daniel1 aCostello, J.C.1 aKüffner, Robert1 aVega, N.M.1 aPrill, R.J.1 aCamacho, D.M.1 aAllison, K.R.1 aKellis, Manolis1 aCollins, J.J.1 aStolovitzky, Gustavo1 aDREAM5 Consortium  uhttps://kdd.isti.cnr.it/publications/wisdom-crowds-robust-gene-network-inference02085nas a2200805   4500008004100000245005500041210005500096300001200151490000600163100002000169700001900189700002100208700001500229700001600244700001800260700001800278700002000296700001800316700001700334700002500351700001600376700001300392700001600405700001800421700002000439700001400459700001700473700001500490700002100505700001800526700001500544700001900559700002000578700001700598700001500615700001600630700001700646700002000663700001600683700001800699700001400717700001500731700001400746700001300760700001700773700001500790700001500805700001500820700001400835700001400849700001900863700001400882700002300896700001400919700001500933700001700948700001300965700001600978700001800994700001501012700002001027700001601047700001701063700001801080700001701098700001801115700001401133700001501147856011701162       2012                        eng d00aWisdom of crowds for robust gene network inference0 aWisdom of crowds for robust gene network inference  a796-8040 v91 aMarbach, Daniel1 aCostello, J.C.1 aKüffner, Robert1 aVega, N.M.1 aPrill, R.J.1 aCamacho, D.M.1 aAllison, K.R.1 aKellis, Manolis1 aCollins, J.J.1 aAderhold, A.1 aStolovitzky, Gustavo1 aBonneau, R.1 aChen, Y.1 aCordero, F.1 aCrane, Martin1 aDondelinger, F.1 aDrton, M.1 aEsposito, R.1 aFoygel, R.1 aDe La Fuente, A.1 aGertheiss, J.1 aGeurts, P.1 aGreenfield, A.1 aGrzegorczyk, M.1 aHaury, A.-C.1 aHolmes, B.1 aHothorn, T.1 aHusmeier, D.1 aHuynh-Thu, V.A.1 aIrrthum, A.1 aKarlebach, G.1 aLebre, S.1 aDe Leo, V.1 aMadar, A.1 aMani, S.1 aMordelet, F.1 aOstrer, H.1 aOuyang, Z.1 aPandya, R.1 aPetri, T.1 aPinna, A.1 aPoultney, C.S.1 aRezny, S.1 aRuskin, Heather, J1 aSaeys, Y.1 aShamir, R.1 aSirbu, Alina1 aSong, M.1 aSoranzo, N.1 aStatnikov, A.1 aVega, N.M.1 aVera-Licona, P.1 aVert, J.-P.1 aVisconti, A.1 aWang, Haizhou1 aWehenkel, L.1 aWindhager, L.1 aZhang, Y.1 aZimmer, R.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84870305264&partnerID=40&md5=04a686572bdefff60157bf68c95df7ea00495nas a2200133   4500008003900000245007300039210006900112300001200181490000600193100002000199700002100219700002000240856010100260       2011                          d00aA classification for community discovery methods in complex networks0 aclassification for community discovery methods in complex networ  a512-5460 v41 aCoscia, Michele1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/classification-community-discovery-methods-complex-networks01842nas a2200169   4500008003900000245007300039210006900112300001100181490000600192520129800198100001901496700002201515700002001537700001801557700001901575856007801594       2011                          d00aC-safety: a framework for the anonymization of semantic trajectories0 aCsafety a framework for the anonymization of semantic trajectori  a73-1010 v43 aThe increasing abundance of data about the trajectories of personal movement is opening
new opportunities for analyzing and mining human mobility. However, new risks emerge since it
opens new ways of intruding into personal privacy. Representing the personal movements as sequences
of places visited by a person during her/his movements - semantic trajectory - poses great
privacy threats. In this paper we propose a privacy model defining the attack model of semantic trajectory
linking and a privacy notion, called c-safety based on a generalization of visited places based
on a taxonomy. This method provides an upper bound to the probability of inferring that a given
person, observed in a sequence of non-sensitive places, has also visited any sensitive location. Coherently
with the privacy model, we propose an algorithm for transforming any dataset of semantic
trajectories into a c-safe one. We report a study on two real-life GPS trajectory datasets to show how
our algorithm preserves interesting quality/utility measures of the original trajectories, when mining
semantic trajectories sequential pattern mining results. We also empirically measure how the
probability that the attacker’s inference succeeds is much lower than the theoretical upper bound
established.1 aMonreale, Anna1 aTrasarti, Roberto1 aPedreschi, Dino1 aRenso, Chiara1 aBogorny, Vania  uhttp://dl.acm.org/citation.cfm?id=2019319&CFID=803961971&CFTOKEN=3599403900491nam a2200109   4500008004100000245008900041210006900130260002000199100002000219700002100239856012100260       2011                        eng d00aDinamiche di impoverimento. Meccanismi, traiettorie ed effetti in un contesto locale0 aDinamiche di impoverimento Meccanismi traiettorie ed effetti in   bCarocci Editore1 aTomei, Gabriele1 aNatilli, Michela  uhttps://kdd.isti.cnr.it/publications/dinamiche-di-impoverimento-meccanismi-traiettorie-ed-effetti-un-contesto-locale00486nas a2200121   4500008003900000245007200039210006900111300001200180100002500192700002000217700002100237856010600258       2011                          d00aFinding and Characterizing Communities in Multidimensional Networks0 aFinding and Characterizing Communities in Multidimensional Netwo  a490-4941 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/finding-and-characterizing-communities-multidimensional-networks00506nas a2200121   4500008003900000245008100039210006900120300001400189100002500203700002000228700002100248856011500269       2011                          d00aFinding redundant and complementary communities in multidimensional networks0 aFinding redundant and complementary communities in multidimensio  a2181-21841 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/finding-redundant-and-complementary-communities-multidimensional-networks01804nas a2200157   4500008003900000245005300039210005300092300001200145520129700157100002501454700002001479700002101499700001901520700002001539856008701559       2011                          d00aFoundations of Multidimensional Network Analysis0 aFoundations of Multidimensional Network Analysis  a485-4893 aComplex networks have been receiving increasing attention by the scientific community, thanks also to the increasing availability of real-world network data. In the last years, the multidimensional nature of many real world networks has been pointed out, i.e. many networks containing multiple connections between any pair of nodes have been analyzed. Despite the importance of analyzing this kind of networks was recognized by previous works, a complete framework for multidimensional network analysis is still missing. Such a framework would enable the analysts to study different phenomena, that can be either the generalization to the multidimensional setting of what happens inmonodimensional network, or a new class of phenomena induced by the additional degree of complexity that multidimensionality provides in real networks. The aim of this paper is then to give the basis for multidimensional network analysis: we develop a solid repertoire of basic concepts and analytical measures, which takes into account the general structure of multidimensional networks. We tested our framework on a real world multidimensional network, showing the validity and the meaningfulness of the measures introduced, that are able to extract important, nonrandom, information about complex phenomena.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/foundations-multidimensional-network-analysis00594nas a2200133   4500008003900000245011900039210006900158100002300227700002300250700002200273700001800295700001900313856012800332       2011                          d00aFrom Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data0 aFrom Movement Tracks through Events to Places Extracting and Cha1 aAndrienko, Gennady1 aAndrienko, Natalia1 aHurter, Cristophe1 aRinzivillo, S1 aWrobel, Stefan  uhttps://kdd.isti.cnr.it/publications/movement-tracks-through-events-places-extracting-and-characterizing-significant-places00493nas a2200097   4500008004100000245010900041210006900150100002100219700002900240856012600269       2011                        eng d00aThe impact of wine and food tourism in Italy: an analysis of official statistical data at province level0 aimpact of wine and food tourism in Italy an analysis of official1 aNatilli, Michela1 aRomano, Maria, Francesca  uhttps://kdd.isti.cnr.it/publications/impact-wine-and-food-tourism-italy-analysis-official-statistical-data-province-level00402nas a2200109   4500008004100000245005300041210004900094100002100143700002100164700002900185856007800214       2011                        eng d00aThe language of tourists in a wine and food blog0 alanguage of tourists in a wine and food blog1 aPavone, Pasquale1 aNatilli, Michela1 aRomano, Maria, Francesca  uhttps://kdd.isti.cnr.it/publications/language-tourists-wine-and-food-blog00347nas a2200109   4500008004100000245004600041210004600087100002100133700002500154700002100179856003700200       2011                        eng d00aLink Prediction su Reti Multidimensionali0 aLink Prediction su Reti Multidimensionali1 aRossetti, Giulio1 aBerlingerio, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/node/62700583nas a2200145   4500008004100000245009200041210006900133300001400202490000800216100001700224700002400241700002100265700002900286856012200315       2011                        eng d00aMeasuring the effectiveness of homeopathic care through objective and shared indicators0 aMeasuring the effectiveness of homeopathic care through objectiv  a212–2190 v1001 aLeone, Laura1 aMarchitiello, Maria1 aNatilli, Michela1 aRomano, Maria, Francesca  uhttps://kdd.isti.cnr.it/publications/measuring-effectiveness-homeopathic-care-through-objective-and-shared-indicators00386nas a2200109   4500008003900000245004500039210004500084260002700129100002200156700001900178856007900197       2011                          d00aMining Influence Rules out of Ontologies0 aMining Influence Rules out of Ontologies  aSiviglia, Spagnac20111 aFurletti, Barbara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/mining-influence-rules-out-ontologies00449nas a2200133   4500008003900000245005000039210005000089300001400139100002200153700001900175700001700194700002100211856008300232       2011                          d00aMining mobility user profiles for car pooling0 aMining mobility user profiles for car pooling  a1190-11981 aTrasarti, Roberto1 aPinelli, Fabio1 aNanni, Mirco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-mobility-user-profiles-car-pooling01493nas a2200157   4500008003900000245006200039210006000101260000900161520096900170100002101139700002201160700001901182700002001201700002101221856009301242       2011                          d00aPrivacy-preserving data mining from outsourced databases.0 aPrivacypreserving data mining from outsourced databases  c20113 aSpurred by developments such as cloud computing, there has been considerable recent interest in the paradigm of data mining-as-service: a company (data owner) lacking in expertise or computational resources can outsource its mining needs to a third party service provider (server). However, both the outsourced database and the knowledge extract from it by data mining are considered private property of the data owner. To protect corporate privacy, the data owner transforms its data and ships it to the server, sends mining queries to the server, and recovers the true patterns from the extracted patterns received from the server. In this paper, we study the problem of outsourcing a data mining task within a corporate privacy-preserving framework. We propose a scheme for privacy-preserving outsourced mining which offers a formal protection against information disclosure, and show that the data owner can recover the correct data mining results efficiently.1 aGiannotti, Fosca1 aLakshmanan, L V S1 aMonreale, Anna1 aPedreschi, Dino1 aWang, Hui, Wendy  uhttps://kdd.isti.cnr.it/publications/privacy-preserving-data-mining-outsourced-databases01999nas a2200169   4500008003900000245008200039210006900121300001200190490000600202520141100208100002501619700002001644700002101664700001901685700002001704856010501724       2011                          d00aThe pursuit of hubbiness: Analysis of hubs in large multidimensional networks0 apursuit of hubbiness Analysis of hubs in large multidimensional   a223-2370 v23 aHubs are highly connected nodes within a network. In complex network analysis, hubs have been widely studied, and are at the basis of many tasks, such as web search and epidemic outbreak detection. In reality, networks are often multidimensional, i.e., there can exist multiple connections between any pair of nodes. In this setting, the concept of hub depends on the multiple dimensions of the network, whose interplay becomes crucial for the connectedness of a node. In this paper, we characterize multidimensional hubs. We consider the multidimensional generalization of the degree and introduce a new class of measures, that we call Dimension Relevance, aimed at analyzing the importance of different dimensions for the hubbiness of a node. We assess the meaningfulness of our measures by comparing them on real networks and null models, then we study the interplay among dimensions and their effect on node connectivity. Our findings show that: (i) multidimensional hubs do exist and their characterization yields interesting insights and (ii) it is possible to detect the most influential dimensions that cause the different hub behaviors. We demonstrate the usefulness of multidimensional analysis in three real world domains: detection of ambiguous query terms in a word–word query log network, outlier detection in a social network, and temporal analysis of behaviors in a co-authorship network.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/pursuit-hubbiness-analysis-hubs-large-multidimensional-networks00478nas a2200157   4500008003900000245004600039210004400085300001000129490000600139100002200145700002100167700001700188700002000205700001800225856007700243       2011                          d00aA Query Language for Mobility Data Mining0 aQuery Language for Mobility Data Mining  a24-450 v71 aTrasarti, Roberto1 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/query-language-mobility-data-mining00474nas a2200133   4500008003900000245005800039210005800097260001400155300001200169100002100181700002500202700002100227856009200248       2011                          d00aScalable Link Prediction on Multidimensional Networks0 aScalable Link Prediction on Multidimensional Networks  aVancouver  a979-9861 aRossetti, Giulio1 aBerlingerio, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/scalable-link-prediction-multidimensional-networks00508nas a2200121   4500008004100000245008100041210006900122260001100191100001700202700002300219700001800242856012600260       2011                        eng d00aStages of Gene Regulatory Network Inference: the Evolutionary Algorithm Role0 aStages of Gene Regulatory Network Inference the Evolutionary Alg  bInTech1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin  uhttp://www.intechopen.com/articles/show/title/stages-of-gene-regulatory-network-inference-the-evolutionary-algorithm-role00557nas a2200109   4500008004100000245015600041210006900197100002000266700001500286700002100301856012500322       2011                        eng d00aStiramenti identitari. Strategie di integrazione degli strannieri nella provincia di Massa Carrara tra appartenenza etnica ed esperienza transnazionale0 aStiramenti identitari Strategie di integrazione degli strannieri1 aTomei, Gabriele1 aPaletti, F1 aNatilli, Michela  uhttps://kdd.isti.cnr.it/publications/stiramenti-identitari-strategie-di-integrazione-degli-strannieri-nella-provincia-di00528nas a2200169   4500008003900000245004600039210004600085300001200131100001700143700001900160700002200179700001700201700001800218700001800236700002100254856008300275       2011                          d00aTraffic Jams Detection Using Flock Mining0 aTraffic Jams Detection Using Flock Mining  a650-6531 aOng, Rebecca1 aPinelli, Fabio1 aTrasarti, Roberto1 aNanni, Mirco1 aRenso, Chiara1 aRinzivillo, S1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/traffic-jams-detection-using-flock-mining00659nas a2200181   4500008003900000245009400039210006900133300001200202490000700214100002100221700001700242700002000259700001900279700001800298700001800316700002200334856012100356       2011                          d00aUnveiling the complexity of human mobility by querying and mining massive trajectory data0 aUnveiling the complexity of human mobility by querying and minin  a695-7190 v201 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aPinelli, Fabio1 aRenso, Chiara1 aRinzivillo, S1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/publications/unveiling-complexity-human-mobility-querying-and-mining-massive-trajectory-data00344nas a2200109   4500008003900000245003600039210003400075300001200109100001800121700002400139856007100163       2011                          d00aWho/Where Are My New Customers?0 aWhoWhere Are My New Customers  a307-3171 aRinzivillo, S1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/publications/whowhere-are-my-new-customers00537nas a2200145   4500008003900000245007400039210006900113300001200182100001700194700002200211700001800233700002100251700002000272856009900292       2010                          d00aAdvanced knowledge discovery on movement data with the GeoPKDD system0 aAdvanced knowledge discovery on movement data with the GeoPKDD s  a693-6961 aNanni, Mirco1 aTrasarti, Roberto1 aRenso, Chiara1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/advanced-knowledge-discovery-movement-data-geopkdd-system00539nas a2200145   4500008003900000245007400039210006900113300001200182100001700194700002200211700001800233700002100251700002000272856010100292       2010                          d00aAdvanced knowledge discovery on movement data with the GeoPKDD system0 aAdvanced knowledge discovery on movement data with the GeoPKDD s  a693-6961 aNanni, Mirco1 aTrasarti, Roberto1 aRenso, Chiara1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/advanced-knowledge-discovery-movement-data-geopkdd-system-001803nas a2200157   4500008003900000245006600039210006500105300001000170520126700180100002501447700002001472700002101492700001901513700002001532856009301552       2010                          d00aAs Time Goes by: Discovering Eras in Evolving Social Networks0 aAs Time Goes by Discovering Eras in Evolving Social Networks  a81-903 aWithin the large body of research in complex network analysis, an important topic is the temporal evolution of networks. Existing approaches aim at analyzing the evolution on the global and the local scale, extracting properties of either the entire network or local patterns. In this paper, we focus instead on detecting clusters of temporal snapshots of a network, to be interpreted as eras of evolution. To this aim, we introduce a novel hierarchical clustering methodology, based on a dissimilarity measure (derived from the Jaccard coefficient) between two temporal snapshots of the network. We devise a framework to discover and browse the eras, either in top-down or a bottom-up fashion, supporting the exploration of the evolution at any level of temporal resolution. We show how our approach applies to real networks, by detecting eras in an evolving co-authorship graph extracted from a bibliographic dataset; we illustrate how the discovered temporal clustering highlights the crucial moments when the network had profound changes in its structure. Our approach is finally boosted by introducing a meaningful labeling of the obtained clusters, such as the characterizing topics of each discovered era, thus adding a semantic dimension to our analysis.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/time-goes-discovering-eras-evolving-social-networks01924nas a2200169   4500008004100000022001400041245008600055210006900141260000900210300000700219490000700226520134700233100001701580700002301597700001801620856011601638       2010                        eng d  a1471-210500aComparison of evolutionary algorithms in gene regulatory network model inference.0 aComparison of evolutionary algorithms in gene regulatory network  c2010  a590 v113 a<p><b>BACKGROUND: </b>The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.</p><p><b>RESULTS: </b>This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared.</p><p><b>CONCLUSIONS: </b>Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.</p>1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin  uhttps://kdd.isti.cnr.it/publications/comparison-evolutionary-algorithms-gene-regulatory-network-model-inference01557nas a2200169   4500008004100000022001400041245008300055210006900138260000900207300001100216490000600227520098100233100001701214700002301231700001801254856011501272       2010                        eng d  a1932-620300aCross-platform microarray data normalisation for regulatory network inference.0 aCrossplatform microarray data normalisation for regulatory netwo  c2010  ae138220 v53 a<p><b>BACKGROUND: </b>Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences.</p><p><b>METHODS: </b>We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets.</p><p><b>CONCLUSIONS: </b>Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.</p>1 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin  uhttps://kdd.isti.cnr.it/publications/cross-platform-microarray-data-normalisation-regulatory-network-inference00537nas a2200145   4500008003900000245006900039210006700108300001000175100002500185700002000210700002100230700001900251700002000270856010100290       2010                          d00aDiscovering Eras in Evolving Social Networks (Extended Abstract)0 aDiscovering Eras in Evolving Social Networks Extended Abstract  a78-851 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/discovering-eras-evolving-social-networks-extended-abstract01089nas a2200193   4500008003900000245004600039210004500085300001200130520052100142100002200663700001800685700001900703700001700722700001900739700001800758700002000776700002100796856007800817       2010                          d00aExploring Real Mobility Data with M-Atlas0 aExploring Real Mobility Data with MAtlas  a624-6273 aResearch on moving-object data analysis has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing location aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks. These have made available massive repositories of spatio-temporal data recording human mobile activities, that call for suitable analytical methods, capable of enabling the development of innovative, location-aware applications.1 aTrasarti, Roberto1 aRinzivillo, S1 aPinelli, Fabio1 aNanni, Mirco1 aMonreale, Anna1 aRenso, Chiara1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/exploring-real-mobility-data-m-atlas01250nas a2200169   4500008003900000022002200039245006500061210006200126520069900188100002300887700002300910700002100933700001900954700002000973700001800993856006901011       2010                          d  a978-989-20-1953-600aA Generalisation-based Approach to Anonymising Movement Data0 aGeneralisationbased Approach to Anonymising Movement Data3 aThe possibility to collect, store, disseminate, and analyze data about movements of people raises
very serious privacy concerns, given the sensitivity of the information about personal positions. In
particular, sensitive information about individuals can be uncovered with the use of data mining and
visual analytics methods. In this paper we present a method for the generalization of trajectory data
that can be adopted as the first step of a process to obtain k-anonymity in spatio-temporal datasets.
We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this
method of generalization of trajectories preserves the clustering analysis results. 1 aAndrienko, Gennady1 aAndrienko, Natalia1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino1 aRinzivillo, S  uhttp://agile2010.dsi.uminho.pt/pen/ShortPapers_PDF%5C122_DOC.pdf00562nas a2200169   4500008003900000022001400039245007600053210006900129260000900198490000600207100002200213700001900235700002100254700002100275700002000296856007600316       2010                          d  a1684-370300aImproving the Business Plan Evaluation Process: the Role of Intangibles0 aImproving the Business Plan Evaluation Process the Role of Intan  c20100 v71 aFurletti, Barbara1 aTurini, Franco1 aBellandi, Andrea1 aBaglioni, Miriam1 aPratesi, Chiara  uhttp://web.it.nctu.edu.tw/~qtqm/upcomingpapers/2010V7N1/2010V7N1_F3.pdf00526nas a2200133   4500008003900000245007800039210006900117300001200186100001900198700001900217700002200236700002100258856011300279       2010                          d00aLocation Prediction through Trajectory Pattern Mining (Extended Abstract)0 aLocation Prediction through Trajectory Pattern Mining Extended A  a134-1411 aMonreale, Anna1 aPinelli, Fabio1 aTrasarti, Roberto1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/location-prediction-through-trajectory-pattern-mining-extended-abstract00607nas a2200169   4500008003900000245007700039210006900116300000900185100002100194700001700215700002000232700001900252700001800271700001800289700002200307856010800329       2010                          d00aMobility data mining: discovering movement patterns from trajectory data0 aMobility data mining discovering movement patterns from trajecto  a7-101 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aPinelli, Fabio1 aRenso, Chiara1 aRinzivillo, S1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/publications/mobility-data-mining-discovering-movement-patterns-trajectory-data02124nas a2200193   4500008003900000245005100039210005100090300001300141490000600154520158100160100001901741700002301760700002301783700002101806700002001827700001801847700001901865856004601884       2010                          d00aMovement Data Anonymity through Generalization0 aMovement Data Anonymity through Generalization  a91–1210 v33 aWireless networks and mobile devices, such as mobile phones and GPS receivers, sense
and track the movements of people and vehicles, producing society-wide mobility databases. This is
a challenging scenario for data analysis and mining. On the one hand, exciting opportunities arise out
of discovering new knowledge about human mobile behavior, and thus fuel intelligent info-mobility
applications. On other hand, new privacy concerns arise when mobility data are published. The
risk is particularly high for GPS trajectories, which represent movement of a very high precision and
spatio-temporal resolution: the de-identification of such trajectories (i.e., forgetting the ID of their
associated owners) is only a weak protection, as generally it is possible to re-identify a person by observing
her routine movements. In this paper we propose a method for achieving true anonymity in
a dataset of published trajectories, by defining a transformation of the original GPS trajectories based
on spatial generalization and k-anonymity. The proposed method offers a formal data protection
safeguard, quantified as a theoretical upper bound to the probability of re-identification. We conduct
a thorough study on a real-life GPS trajectory dataset, and provide strong empirical evidence that
the proposed anonymity techniques achieve the conflicting goals of data utility and data privacy. In
practice, the achieved anonymity protection is much stronger than the theoretical worst case, while
the quality of the cluster analysis on the trajectory data is preserved.1 aMonreale, Anna1 aAndrienko, Gennady1 aAndrienko, Natalia1 aGiannotti, Fosca1 aPedreschi, Dino1 aRinzivillo, S1 aWrobel, Stefan  uhttp://www.tdp.cat/issues/abs.a045a10.php01643nas a2200157   4500008003900000245007100039210006900110300001000179520109600189100001901285700002201304700001801326700002001344700001901364856010201383       2010                          d00aPreserving privacy in semantic-rich trajectories of human mobility0 aPreserving privacy in semanticrich trajectories of human mobilit  a47-543 aThe increasing abundance of data about the trajectories of personal movement is opening up new opportunities for analyzing and mining human mobility, but new risks emerge since it opens new ways of intruding into personal privacy. Representing the personal movements as sequences of places visited by a person during her/his movements - semantic trajectory - poses even greater privacy threats w.r.t. raw geometric location data. In this paper we propose a privacy model defining the attack model of semantic trajectory linking, together with a privacy notion, called c-safety. This method provides an upper bound to the probability of inferring that a given person, observed in a sequence of nonsensitive places, has also stopped in any sensitive location. Coherently with the privacy model, we propose an algorithm for transforming any dataset of semantic trajectories into a c-safe one. We report a study on a real-life GPS trajectory dataset to show how our algorithm preserves interesting quality/utility measures of the original trajectories, such as sequential pattern mining results.1 aMonreale, Anna1 aTrasarti, Roberto1 aRenso, Chiara1 aPedreschi, Dino1 aBogorny, Vania  uhttps://kdd.isti.cnr.it/publications/preserving-privacy-semantic-rich-trajectories-human-mobility00497nas a2200109   4500008003900000245010500039210006900144300001200213100001700225700002200242856012300264       2010                          d00aQuerying and mining trajectories with gaps: a multi-path reconstruction approach (Extended Abstract)0 aQuerying and mining trajectories with gaps a multipath reconstru  a126-1331 aNanni, Mirco1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/publications/querying-and-mining-trajectories-gaps-multi-path-reconstruction-approach-extended00450nas a2200121   4500008004100000245008700041210006900128300001600197100001700213700002300230700001800253856005700271       2010                        eng d00aRegulatory network modelling: Correlation for structure and parameter optimisation0 aRegulatory network modelling Correlation for structure and param  a3473–34811 aSirbu, Alina1 aRuskin, Heather, J1 aCrane, Martin  uhttp://www.actapress.com/Abstract.aspx?paperId=4157300393nas a2200133   4500008003900000245003100039210003000070300001200100100002200112700002200134700001700156700001800173856006800191       2010                          d00aSpatio-temporal clustering0 aSpatiotemporal clustering  a855-8741 aKisilevich, Slava1 aMansmann, Florian1 aNanni, Mirco1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/publications/spatio-temporal-clustering01363nas a2200145   4500008003900000245004900039210004900088520089500137100002501032700002001057700002101077700001901098700002001117856008001137       2010                          d00aTowards Discovery of Eras in Social Networks0 aTowards Discovery of Eras in Social Networks3 aIn the last decades, much research has been devoted in topics related to Social Network Analysis. One important direction in this area is to analyze the temporal evolution of a network. So far, previous approaches analyzed this setting at both the global and the local level. In this paper, we focus on finding a way to detect temporal eras in an evolving network. We pose the basis for a general framework that aims at helping the analyst in browsing the temporal clusters both in a top-down and bottom-up way, exploring the network at any level of temporal details. We show the effectiveness of our approach on real data, by applying our proposed methodology to a co-authorship network extracted from a bibliographic dataset. Our first results are encouraging, and open the way for the definition and implementation of a general framework for discovering eras in evolving social networks.1 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/towards-discovery-eras-social-networks00453nas a2200145   4500008003900000245004500039210004500084250000700129260001900136100002200155700001900177700001900196700002000215856007200235       2009                          d00aAnonymous Sequences from Trajectory Data0 aAnonymous Sequences from Trajectory Data  a17  aCamogli, Italy1 aPensa, Ruggero, G1 aMonreale, Anna1 aPinelli, Fabio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/anonymous-sequences-trajectory-data00597nas a2200169   4500008003900000245007300039210006900112300000900181490000700190100002200197700002100219700002200240700002300262700002100285700002200306856009900328       2009                          d00aA constraint-based querying system for exploratory pattern discovery0 aconstraintbased querying system for exploratory pattern discover  a3-270 v341 aBonchi, Francesco1 aGiannotti, Fosca1 aLucchese, Claudio1 aOrlando, Salvatore1 aPerego, Raffaele1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/content/constraint-based-querying-system-exploratory-pattern-discovery00602nas a2200169   4500008003900000245007300039210006900112300000900181490000700190100002200197700002100219700002200240700002300262700002100285700002200306856010400328       2009                          d00aA constraint-based querying system for exploratory pattern discovery0 aconstraintbased querying system for exploratory pattern discover  a3-270 v341 aBonchi, Francesco1 aGiannotti, Fosca1 aLucchese, Claudio1 aOrlando, Salvatore1 aPerego, Raffaele1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/publications/constraint-based-querying-system-exploratory-pattern-discovery00484nas a2200121   4500008003900000245007700039210006900116300001200185100002200197700002100219700001800240856010400258       2009                          d00aDAMSEL: A System for Progressive Querying and Reasoning on Movement Data0 aDAMSEL A System for Progressive Querying and Reasoning on Moveme  a452-4561 aTrasarti, Roberto1 aBaglioni, Miriam1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/publications/damsel-system-progressive-querying-and-reasoning-movement-data00413nas a2200085   4500008004100000245007800041210006900119100003500188856010400223       2009                        eng d00aDeveloping a Spatial Knowledge Representation for Pedestrian Interactions0 aDeveloping a Spatial Knowledge Representation for Pedestrian Int1 aDaniel Ornellana, Chiara Renso  uhttps://kdd.isti.cnr.it/content/developing-spatial-knowledge-representation-pedestrian-interactions00457nas a2200121   4500008003900000245006200039210006100101300001400162100002100176700002000197700002400217856009400241       2009                          d00aGeographic privacy-aware knowledge discovery and delivery0 aGeographic privacyaware knowledge discovery and delivery  a1157-11581 aGiannotti, Fosca1 aPedreschi, Dino1 aTheodoridis, Yannis  uhttps://kdd.isti.cnr.it/content/geographic-privacy-aware-knowledge-discovery-and-delivery00517nas a2200145   4500008003900000245006100039210005600100100002100156700001700177700002000194700001800214700001800232700002200250856009900272       2009                          d00aGeoPKDD – Geographic Privacy-aware Knowledge Discovery0 aGeoPKDD Geographic Privacyaware Knowledge Discovery1 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara1 aRinzivillo, S1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/content/geopkdd-%E2%80%93-geographic-privacy-aware-knowledge-discovery00488nas a2200121   4500008003900000245007900039210006900118300001200187100002000199700002400219700001900243856010400262       2009                          d00aIntegrating induction and deduction for finding evidence of discrimination0 aIntegrating induction and deduction for finding evidence of disc  a157-1661 aPedreschi, Dino1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/integrating-induction-and-deduction-finding-evidence-discrimination00430nas a2200109   4500008003900000245006500039210006400104300001200168100001700180700002200197856010100219       2009                          d00aK-BestMatch Reconstruction and Comparison of Trajectory Data0 aKBestMatch Reconstruction and Comparison of Trajectory Data  a610-6151 aNanni, Mirco1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/publications/k-bestmatch-reconstruction-and-comparison-trajectory-data-000428nas a2200109   4500008003900000245006500039210006400104300001200168100001700180700002200197856009900219       2009                          d00aK-BestMatch Reconstruction and Comparison of Trajectory Data0 aKBestMatch Reconstruction and Comparison of Trajectory Data  a610-6151 aNanni, Mirco1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/publications/k-bestmatch-reconstruction-and-comparison-trajectory-data00468nas a2200121   4500008003900000245006800039210006700107300001200174100002000186700002400206700001900230856009700249       2009                          d00aMeasuring Discrimination in Socially-Sensitive Decision Records0 aMeasuring Discrimination in SociallySensitive Decision Records  a581-5921 aPedreschi, Dino1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/measuring-discrimination-socially-sensitive-decision-records00573nas a2200145   4500008003900000245008400039210006900123300001200192100002500204700002200229700002000251700002100271700001900292856011600311       2009                          d00aMining Clinical, Immunological, and Genetic Data of Solid Organ Transplantation0 aMining Clinical Immunological and Genetic Data of Solid Organ Tr  a211-2361 aBerlingerio, Michele1 aBonchi, Francesco1 aCurcio, Michele1 aGiannotti, Fosca1 aTurini, Franco  uhttps://kdd.isti.cnr.it/publications/mining-clinical-immunological-and-genetic-data-solid-organ-transplantation00443nas a2200145   4500008003900000245003300039210003300072260001900105300001200124100002500136700002200161700002200183700002200205856007000227       2009                          d00aMining Graph Evolution Rules0 aMining Graph Evolution Rules  aBled, Slovenia  a115-1301 aBerlingerio, Michele1 aBonchi, Francesco1 aBringmann, Björn1 aGionis, Aristides  uhttps://kdd.isti.cnr.it/publications/mining-graph-evolution-rules00477nas a2200145   4500008003900000245005000039210005000089300001200139100002100151700001700172700002000189700001800209700002200227856008200249       2009                          d00aMining Mobility Behavior from Trajectory Data0 aMining Mobility Behavior from Trajectory Data  a948-9511 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/publications/mining-mobility-behavior-trajectory-data00425nas a2200121   4500008003900000245005200039210005200091300001200143100002500155700002000180700002100200856008200221       2009                          d00aMining the Information Propagation in a Network0 aMining the Information Propagation in a Network  a333-3401 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-information-propagation-network-000423nas a2200121   4500008003900000245005200039210005200091300001200143100002500155700002000180700002100200856008000221       2009                          d00aMining the Information Propagation in a Network0 aMining the Information Propagation in a Network  a333-3401 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-information-propagation-network00460nas a2200121   4500008003900000245006500039210006500104300001200169100002500181700002000206700002100226856009100247       2009                          d00aMining the Temporal Dimension of the Information Propagation0 aMining the Temporal Dimension of the Information Propagation  a237-2481 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/publications/mining-temporal-dimension-information-propagation00455nas a2200121   4500008003900000245006500039210006500104300001200169100002500181700002000206700002100226856008600247       2009                          d00aMining the Temporal Dimension of the Information Propagation0 aMining the Temporal Dimension of the Information Propagation  a237-2481 aBerlingerio, Michele1 aCoscia, Michele1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/mining-temporal-dimension-information-propagation01604nas a2200157   4500008004100000245005100041210005100092260000800143520109900151100002301250700002301273700002101296700001901317700002001336856009001356       2009                        eng d00aMovement data anonymity through generalization0 aMovement data anonymity through generalization  bACM3 aIn recent years, spatio-temporal and moving objects databases have gained considerable interest, due to the diffusion of mobile devices (e.g., mobile phones, RFID devices and GPS devices) and of new applications, where the discovery of consumable, concise, and applicable knowledge is the key step. Clearly, in these applications privacy is a concern, since models extracted from this kind of data can reveal the behavior of group of individuals, thus compromising their privacy. Movement data present a new challenge for the privacy-preserving data mining community because of their spatial and temporal characteristics.

In this position paper we briefly present an approach for the generalization of movement data that can be adopted for obtaining k-anonymity in spatio-temporal datasets; specifically, it can be used to realize a framework for publishing of spatio-temporal data while preserving privacy. We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this method of generalization of trajectories preserves the clustering analysis results.1 aAndrienko, Gennady1 aAndrienko, Natalia1 aGiannotti, Fosca1 aMonreale, Anna1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/publications/movement-data-anonymity-through-generalization-000488nas a2200121   4500008003900000245007600039210006900115300001200184100002200196700002200218700001900240856010700259       2009                          d00aA new technique for sequential pattern mining under regular expressions0 anew technique for sequential pattern mining under regular expres  a325-3321 aTrasarti, Roberto1 aBonchi, Francesco1 aGoethals, Bart  uhttps://kdd.isti.cnr.it/publications/new-technique-sequential-pattern-mining-under-regular-expressions00347nas a2200097   4500008003900000245004000039210004000079260003100119100002200150856007700172       2009                          d00aOntology Driven Knowledge Discovery0 aOntology Driven Knowledge Discovery  aLucca - ItalybIMT - Lucca1 aFurletti, Barbara  uhttps://kdd.isti.cnr.it/publications/ontology-driven-knowledge-discovery00460nas a2200109   4500008004100000245008300041210006900124260001200193100002000205700002100225856010400246       2009                        eng d00aPoverty as a Social Condition: a Case Study on a Small Municipality in Tuscany0 aPoverty as a Social Condition a Case Study on a Small Municipali  bSEAFORD1 aTomei, Gabriele1 aNatilli, Michela  uhttps://kdd.isti.cnr.it/publications/poverty-social-condition-case-study-small-municipality-tuscany00579nas a2200121   4500008004100000245012200041210006900163100002200232700003800254700001800292700002200310856012500332       2009                        eng d00a{The Role of a Multi-tier Ontological Framework in Reasoning to Discover Meaningful Patterns of Sustainable Mobility}0 aRole of a Multitier Ontological Framework in Reasoning to Discov1 aWachowicz, Monica1 aMacêdo, José Antônio Fernandes1 aRenso, Chiara1 aLigtenberg, Arend  uhttps://kdd.isti.cnr.it/content/role-multi-tier-ontological-framework-reasoning-discover-meaningful-patterns-sustainable00529nas a2200121   4500008003900000245009700039210006900136300001200205100002000217700002100237700002200258856012700280       2009                          d00aSocial Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography0 aSocial Network Analysis as Knowledge Discovery Process A Case St  a279-2831 aCoscia, Michele1 aGiannotti, Fosca1 aPensa, Ruggero, G  uhttps://kdd.isti.cnr.it/publications/social-network-analysis-knowledge-discovery-process-case-study-digital-bibliography-000527nas a2200121   4500008003900000245009700039210006900136300001200205100002000217700002100237700002200258856012500280       2009                          d00aSocial Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography0 aSocial Network Analysis as Knowledge Discovery Process A Case St  a279-2831 aCoscia, Michele1 aGiannotti, Fosca1 aPensa, Ruggero, G  uhttps://kdd.isti.cnr.it/publications/social-network-analysis-knowledge-discovery-process-case-study-digital-bibliography00472nas a2200133   4500008003900000245005900039210005900098300001200157100002500169700001900194700001700213700002100230856008700251       2009                          d00aTemporal mining for interactive workflow data analysis0 aTemporal mining for interactive workflow data analysis  a109-1181 aBerlingerio, Michele1 aPinelli, Fabio1 aNanni, Mirco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/temporal-mining-interactive-workflow-data-analysis00518nas a2200145   4500008003900000245005700039210005700096300001200153100002100165700003800186700001800224700002200242700002200264856008600286       2009                          d00aTowards Semantic Interpretation of Movement Behavior0 aTowards Semantic Interpretation of Movement Behavior  a271-2881 aBaglioni, Miriam1 aMacêdo, José Antônio Fernandes1 aRenso, Chiara1 aTrasarti, Roberto1 aWachowicz, Monica  uhttps://kdd.isti.cnr.it/content/towards-semantic-interpretation-movement-behavior00523nas a2200145   4500008003900000245005700039210005700096300001200153100002100165700003800186700001800224700002200242700002200264856009100286       2009                          d00aTowards Semantic Interpretation of Movement Behavior0 aTowards Semantic Interpretation of Movement Behavior  a271-2881 aBaglioni, Miriam1 aMacêdo, José Antônio Fernandes1 aRenso, Chiara1 aTrasarti, Roberto1 aWachowicz, Monica  uhttps://kdd.isti.cnr.it/publications/towards-semantic-interpretation-movement-behavior00486nas a2200145   4500008003900000245005000039210005000089260003100139300001000170100002100180700001700201700002000218700001900238856008300257       2009                          d00aTrajectory pattern analysis for urban traffic0 aTrajectory pattern analysis for urban traffic  aSEATTLE, USAbACMc11/2009  a43-471 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aPinelli, Fabio  uhttps://kdd.isti.cnr.it/publications/trajectory-pattern-analysis-urban-traffic00552nas a2200145   4500008003900000245008100039210006900120300001200189100002300201700002300224700001800247700001700265700002000282856010400302       2009                          d00aA Visual Analytics Toolkit for Cluster-Based Classification of Mobility Data0 aVisual Analytics Toolkit for ClusterBased Classification of Mobi  a432-4351 aAndrienko, Gennady1 aAndrienko, Natalia1 aRinzivillo, S1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/visual-analytics-toolkit-cluster-based-classification-mobility-data00572nas a2200157   4500008003900000245006500039210006500104260003200169100002300201700002300224700001800247700001700265700002000282700002100302856009100323       2009                          d00aVisual Cluster Analysis of Large Collections of Trajectories0 aVisual Cluster Analysis of Large Collections of Trajectories  bIEEE Computer Society Press1 aAndrienko, Gennady1 aAndrienko, Natalia1 aRinzivillo, S1 aNanni, Mirco1 aPedreschi, Dino1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/visual-cluster-analysis-large-collections-trajectories02208nas a2200133   4500008003900000245006500039210006400104520173400168100001901902700001901921700002201940700002101962856009101983       2009                          d00aWhereNext: a Location Predictor on Trajectory Pattern Mining0 aWhereNext a Location Predictor on Trajectory Pattern Mining3 aThe pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data.This side eect provides the opportunity for innovative methods that analyse the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. The tree is learned from the Trajectory Patterns that hold a certain area and it may be used as a predictor of the next location of a new trajectory finding the best matching path in the tree. Three dierent best matching methods to classify a new moving object are proposed and their impact on the quality of prediction is studied extensively. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends on the movement of all available objects in a certain area instead of on the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties that have emerged from the data and this allows us to define matching methods that striclty depend on the properties of such movements. In addition, we propose a set of other measures, that evaluate a priori the predictive power of a set of Trajectory Patterns. This measures were tuned on a real life case study. Finally, an exhaustive set of experiments and results on the real dataset are presented.1 aMonreale, Anna1 aPinelli, Fabio1 aTrasarti, Roberto1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/wherenext-location-predictor-trajectory-pattern-mining00488nas a2200121   4500008004100000245008000041210006900121300001400190490000700204100002900211700002100240856010500261       2009                        eng d00aWine tourism in Italy: New profiles, styles of consumption, ways of touring0 aWine tourism in Italy New profiles styles of consumption ways of  a463–4750 v571 aRomano, Maria, Francesca1 aNatilli, Michela  uhttps://kdd.isti.cnr.it/publications/wine-tourism-italy-new-profiles-styles-consumption-ways-touring00451nas a2200145   4500008004100000245004300041210004300084300001200127490000700139100002100146700002200167700002100189700002000210856007500230       2008                        eng d00aAnonymity preserving pattern discovery0 aAnonymity preserving pattern discovery  a703-7270 v171 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/anonymity-preserving-pattern-discovery00659nas a2200181   4500008004100000245008100041210006900122100001800191700001500209700002100224700001500245700002200260700002600282700001800308700002600326700001900352856010600371       2008                        eng d00aAn Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology0 aApplication of Advanced SpatioTemporal Formalisms to Behavioural1 aCeccarelli, T1 aCenteno, D1 aGiannotti, Fosca1 aMassolo, A1 aParent, Christine1 aRaffaetà, Alessandra1 aRenso, Chiara1 aSpaccapietra, Stefano1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/application-advanced-spatio-temporal-formalisms-behavioural-ecology-000698nas a2200205   4500008004100000245008100041210006900122300001000191490000700201100002600208700001800234700001500252700002100267700001500288700002200303700001800325700002600343700001900369856010400388       2008                        eng d00aAn Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology0 aApplication of Advanced SpatioTemporal Formalisms to Behavioural  a37-720 v121 aRaffaetà, Alessandra1 aCeccarelli, T1 aCenteno, D1 aGiannotti, Fosca1 aMassolo, A1 aParent, Christine1 aRenso, Chiara1 aSpaccapietra, Stefano1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/application-advanced-spatio-temporal-formalisms-behavioural-ecology00465nas a2200121   4500008003900000245007000039210006700109300001200176100002000188700001800208700002400226856009300250       2008                          d00aA Case Study in Sequential Pattern Mining for IT-Operational Risk0 aCase Study in Sequential Pattern Mining for ITOperational Risk  a424-4391 aGrossi, Valerio1 aRomei, Andrea1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/case-study-sequential-pattern-mining-it-operational-risk00578nas a2200145   4500008004100000245012500041210006900166260006700235300001000302100002200312700002200334700001800356700002100374856003700395       2008                        eng d00aCharacterising the Next Generation of Mobile Applications Through a Privacy-Aware Geographic Knowledge Discovery Process0 aCharacterising the Next Generation of Mobile Applications Throug  aMobility, Privacy, and Geographyba Knowledge Discovery vision  a39-721 aWachowicz, Monica1 aLigtenberg, Arend1 aRenso, Chiara1 aGürses, Seda, F  uhttps://kdd.isti.cnr.it/node/63000574nas a2200145   4500008003900000245009800039210006900137300000700206100001800213700002300231700002100254700001900275700001700294856011700311       2008                          d00aClustering of German municipalities based on mobility characteristics: an overview of results0 aClustering of German municipalities based on mobility characteri  a691 aZanda, Andrea1 aKörner, Christine1 aGiannotti, Fosca1 aSchulz, Daniel1 aMay, Michael  uhttps://kdd.isti.cnr.it/content/clustering-german-municipalities-based-mobility-characteristics-overview-results00662nas a2200193   4500008004100000245007300041210006900114300001200183100002100195700001500216700001600231700002200247700001800269700002100287700002000308700001800328700002400346856009800370       2008                        eng d00aDAEDALUS: A knowledge discovery analysis framework for movement data0 aDAEDALUS A knowledge discovery analysis framework for movement d  a191-1981 aOrtale, Riccardo1 aRitacco, E1 aPelekisy, N1 aTrasarti, Roberto1 aCosta, Gianni1 aGiannotti, Fosca1 aManco, Giuseppe1 aRenso, Chiara1 aTheodoridis, Yannis  uhttps://kdd.isti.cnr.it/content/daedalus-knowledge-discovery-analysis-framework-movement-data00665nas a2200193   4500008003900000245007700039210006900116300000700185100002100192700001500213700001900228700002200247700001800269700002100287700002000308700001800328700002400346856010100370       2008                          d00aThe DAEDALUS framework: progressive querying and mining of movement data0 aDAEDALUS framework progressive querying and mining of movement d  a521 aOrtale, Riccardo1 aRitacco, E1 aPelekis, Nikos1 aTrasarti, Roberto1 aCosta, Gianni1 aGiannotti, Fosca1 aManco, Giuseppe1 aRenso, Chiara1 aTheodoridis, Yannis  uhttps://kdd.isti.cnr.it/content/daedalus-framework-progressive-querying-and-mining-movement-data00627nas a2200145   4500008003900000020002200039245008800061210006900149100001900218700002100237700002200258700002000280700001800300856016300318       2008                          d  a978-953-7619-16-900aDiscovering Strategic Behaviour in Multi- Agent Scenarios by Ontology-Driven Mining0 aDiscovering Strategic Behaviour in Multi Agent Scenarios by Onto1 aBacciu, Davide1 aBellandi, Andrea1 aFurletti, Barbara1 aGrossi, Valerio1 aRomei, Andrea  uhttp://www.intechopen.com/books/advances_in_robotics_automation_and_control/discovering_strategic_behaviors_in_multi-agent_scenarios_by_ontology-driven_mining00378nas a2200121   4500008003900000245003700039210003600076300001200112100002000124700002400144700001900168856006900187       2008                          d00aDiscrimination-aware data mining0 aDiscriminationaware data mining  a560-5681 aPedreschi, Dino1 aRuggieri, Salvatore1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/discrimination-aware-data-mining00568nas a2200133   4500008003900000020002200039245008600061210006900147260000900216100002200225700002500247700002300272856013900295       2008                          d  a978-972-8924-56-000aAN EXTENSIBLE AND INTERACTIVE SOFTWARE AGENT FOR MOBILE DEVICES BASED ON GPS DATA0 aEXTENSIBLE AND INTERACTIVE SOFTWARE AGENT FOR MOBILE DEVICES BAS  c20081 aFurletti, Barbara1 aFornasari, Francesco1 aMontanari, Claudio  uhttp://www.iadisportal.org/digital-library/mdownload/an-extensible-and-interactive-software-agent-for-mobile-devices-based-on-gps-data00494nas a2200157   4500008003900000245004700039210004700086300001200133100001800145700001900163700001900182700002300201700001900224700001700243856007600260       2008                          d00aKnowledge Discovery from Geographical Data0 aKnowledge Discovery from Geographical Data  a243-2651 aRinzivillo, S1 aTurini, Franco1 aBogorny, Vania1 aKörner, Christine1 aKuijpers, Bart1 aMay, Michael  uhttps://kdd.isti.cnr.it/content/knowledge-discovery-geographical-data-001916nas a2200145   4500008003900000245007900039210006900118260002000187520137500207100001901582700001901601700002201620700002101642856010701663       2008                          d00aLocation prediction within the mobility data analysis environment Daedalus0 aLocation prediction within the mobility data analysis environmen  aDublin, Ireland3 aIn this paper we propose a method to predict the next location of a moving object based on two recent results in GeoPKDD project: DAEDALUS, a mobility data analysis environment and Trajectory Pattern, a sequential pattern mining algorithm with temporal annotation integrated in DAEDALUS. The first one is a DMQL environment for moving objects, where both data and patterns can be represented. The second one extracts movement patterns as sequences of movements between locations with typical travel times.

This paper proposes a prediction method which uses the local models extracted by Trajectory Pattern to build a global model called Prediction Tree. The future location of a moving object is predicted visiting the tree and calculating the best matching function.

The integration within DAEDALUS system supports an interactive construction of the predictor on the top of a set of spatio-temporal patterns.

Others proposals in literature base the definition of prediction methods for future location of a moving object on previously extracted frequent patterns. They use the recent history of movements of the object itself and often use time only to order the events. Our work uses the movements of all moving objects in a certain area to learn a classifier built on the mined trajectory patterns, which are intrinsically equipped with temporal information.1 aPinelli, Fabio1 aMonreale, Anna1 aTrasarti, Roberto1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/location-prediction-within-mobility-data-analysis-environment-daedalus00411nas a2200109   4500008003900000245006300039210006100102300000900163100002100172700002000193856008800213       2008                          d00aMobility, Data Mining and Privacy: A Vision of Convergence0 aMobility Data Mining and Privacy A Vision of Convergence  a1-111 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/mobility-data-mining-and-privacy-vision-convergence00541nam a2200145   4500008003900000020002200039245007100061210006800132260001300200100002100213700002000234700002100254700002000275856010000295       2008                          d  a978-3-540-75176-200aMobility, Data Mining and Privacy - Geographic Knowledge Discovery0 aMobility Data Mining and Privacy Geographic Knowledge Discovery  bSpringer1 aGiannotti, Fosca1 aPedreschi, Dino1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/mobility-data-mining-and-privacy-geographic-knowledge-discovery00472nas a2200121   4500008003900000245007600039210006900115300001000184100002100194700002000215700001900235856009600254       2008                          d00aMobility, Data Mining and Privacy the Experience of the GeoPKDD Project0 aMobility Data Mining and Privacy the Experience of the GeoPKDD P  a25-321 aGiannotti, Fosca1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/mobility-data-mining-and-privacy-experience-geopkdd-project00467nas a2200133   4500008003900000245005200039210005200091260002400143100002200167700002100189700002000210700001800230856008500248       2008                          d00aOntological Support for Association Rule Mining0 aOntological Support for Association Rule Mining  aInnsbruck, Austria 1 aFurletti, Barbara1 aBellandi, Andrea1 aGrossi, Valerio1 aRomei, Andrea  uhttps://kdd.isti.cnr.it/publications/ontological-support-association-rule-mining00547nas a2200133   4500008003900000245008800039210006900127300001200196100002100208700003800229700001800267700002200285856010600307       2008                          d00aAn Ontology-Based Approach for the Semantic Modelling and Reasoning on Trajectories0 aOntologyBased Approach for the Semantic Modelling and Reasoning   a344-3531 aBaglioni, Miriam1 aMacêdo, José Antônio Fernandes1 aRenso, Chiara1 aWachowicz, Monica  uhttps://kdd.isti.cnr.it/content/ontology-based-approach-semantic-modelling-and-reasoning-trajectories00495nas a2200157   4500008003900000020002200039245004800061210004700109260000900156100001900165700002200184700002100206700002100227700002100248856006800269       2008                          d  a978-0-7695-3373-500aOntology-Based Business Plan Classification0 aOntologyBased Business Plan Classification  c20081 aTurini, Franco1 aFurletti, Barbara1 aBaglioni, Miriam1 aSpinsanti, Laura1 aBellandi, Andrea  uhttp://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=463478900476nas a2200145   4500008003900000245004800039210004700087300001200134100002100146700002100167700002200188700002100210700001900231856008000250       2008                          d00aOntology-Based Business Plan Classification0 aOntologyBased Business Plan Classification  a365-3711 aBaglioni, Miriam1 aBellandi, Andrea1 aFurletti, Barbara1 aSpinsanti, Laura1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/ontology-based-business-plan-classification00497nas a2200145   4500008004100000245005500041210005400096300001200150100002100162700002000183700002500203700001800228700002100246856008400267       2008                        eng d00aOntology-driven Querying of Geographical Databases0 aOntologydriven Querying of Geographical Databases  a31–441 aBaglioni, Miriam1 aGiovannetti, E.1 aMasserotti, Maria, V1 aRenso, Chiara1 aSpinsanti, Laura  uhttps://kdd.isti.cnr.it/content/ontology-driven-querying-geographical-databases01351nas a2200133   4500008003900000245009600039210006900135520084300204100002201047700001901069700001901088700002001107856009001127       2008                          d00aPattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining0 aPatternPreserving kAnonymization of Sequences and its Applicatio3 aSequential pattern mining is a major research field in knowledge
discovery and data mining. Thanks to the increasing availability of
transaction data, it is now possible to provide new and improved services
based on users’ and customers’ behavior. However, this puts the citizen’s
privacy at risk. Thus, it is important to develop new privacy-preserving
data mining techniques that do not alter the analysis results significantly.
In this paper we propose a new approach for anonymizing sequential
data by hiding infrequent, and thus potentially sensible, subsequences.
Our approach guarantees that the disclosed data are k-anonymous and
preserve the quality of extracted patterns. An application to a real-world
moving object database is presented, which shows the effectiveness of our
approach also in complex contexts.1 aPensa, Ruggero, G1 aMonreale, Anna1 aPinelli, Fabio1 aPedreschi, Dino  uhttps://air.unimi.it/retrieve/handle/2434/52786/106397/ProceedingsPiLBA08.pdf#page=4400657nas a2200181   4500008003900000245008000039210006900119300001200188100002000200700002200220700001900242700002700261700002100288700001900309700001800328700001900346856011000365       2008                          d00aPrivacy Protection: Regulations and Technologies, Opportunities and Threats0 aPrivacy Protection Regulations and Technologies Opportunities an  a101-1191 aPedreschi, Dino1 aBonchi, Francesco1 aTurini, Franco1 aVerykios, Vassilios, S1 aAtzori, Maurizio1 aMalin, Bradley1 aMoelans, Bart1 aSaygin, Yücel  uhttps://kdd.isti.cnr.it/content/privacy-protection-regulations-and-technologies-opportunities-and-threats00536nas a2200157   4500008003900000245005800039210005800097300001200155100002000167700002100187700002100208700001900229700002600248700001800274856008600292       2008                          d00aQuerying and Reasoning for Spatiotemporal Data Mining0 aQuerying and Reasoning for Spatiotemporal Data Mining  a335-3741 aManco, Giuseppe1 aBaglioni, Miriam1 aGiannotti, Fosca1 aKuijpers, Bart1 aRaffaetà, Alessandra1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/querying-and-reasoning-spatiotemporal-data-mining00595nas a2200157   4500008004100000245005900041210005800100260006700158100002000225700002100245700002100266700001900287700002600306700001800332856008700350       2008                        eng d00aQuerying and Reasoning for Spatio-Temporal Data Mining0 aQuerying and Reasoning for SpatioTemporal Data Mining  aMobility, Privacy, and Geographyba Knowledge Discovery vision1 aManco, Giuseppe1 aBaglioni, Miriam1 aGiannotti, Fosca1 aKuijpers, Bart1 aRaffaetà, Alessandra1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/querying-and-reasoning-spatio-temporal-data-mining00418nas a2200145   4500008003900000245003100039210003100070300001200101100001700113700001900130700002300149700001700172700002000189856006300209       2008                          d00aSpatiotemporal Data Mining0 aSpatiotemporal Data Mining  a267-2961 aNanni, Mirco1 aKuijpers, Bart1 aKörner, Christine1 aMay, Michael1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/spatiotemporal-data-mining00450nas a2200133   4500008004100000245005200041210005100093300001200144100002500156700002100181700001700202700001900219856007800238       2008                        eng d00aTemporal analysis of process logs: a case study0 aTemporal analysis of process logs a case study  a430-4371 aBerlingerio, Michele1 aGiannotti, Fosca1 aNanni, Mirco1 aPinelli, Fabio  uhttps://kdd.isti.cnr.it/content/temporal-analysis-process-logs-case-study00401nas a2200109   4500008003900000245005600039210005400095300001200149100002400161700002400185856008200209       2008                          d00aTyping Linear Constraints for Moding CLP() Programs0 aTyping Linear Constraints for Moding CLP Programs  a128-1431 aRuggieri, Salvatore1 aMesnard, Frédéric  uhttps://kdd.isti.cnr.it/content/typing-linear-constraints-moding-clp-programs00627nas a2200181   4500008003900000245007200039210006900111260002700180300001200207490000600219100001800225700002000243700001700263700002100280700002300301700002300324856009800347       2008                          d00aVisually driven analysis of movement data by progressive clustering0 aVisually driven analysis of movement data by progressive cluster  bPalgrave Macmillan Ltd  a225-2390 v71 aRinzivillo, S1 aPedreschi, Dino1 aNanni, Mirco1 aGiannotti, Fosca1 aAndrienko, Natalia1 aAndrienko, Gennady  uhttps://kdd.isti.cnr.it/content/visually-driven-analysis-movement-data-progressive-clustering00527nas a2200169   4500008004100000245007400041210006900115300001100184100001800195700002000213700001700233700002000250700001800270700001900288700001300307856003700320       2008                        eng d00aWireless Network Data Sources: Tracking and Synthesizing Trajectories0 aWireless Network Data Sources Tracking and Synthesizing Trajecto  a73-1001 aRenso, Chiara1 aPuntoni, Simone1 aFrentzos, E.1 aMazzoni, Andrea1 aMoelans, Bart1 aPelekis, Nikos1 aPini, F.  uhttps://kdd.isti.cnr.it/node/62900488nas a2200133   4500008004100000245006300041210006300104300001200167100002100179700002500200700001800225700002100243856009000264       2007                        eng d00aBuilding Geospatial Ontologies from Geographical Databases0 aBuilding Geospatial Ontologies from Geographical Databases  a195-2091 aBaglioni, Miriam1 aMasserotti, Maria, V1 aRenso, Chiara1 aSpinsanti, Laura  uhttps://kdd.isti.cnr.it/content/building-geospatial-ontologies-geographical-databases00422nas a2200133   4500008004100000245004100041210004100082300001200123100001600135700002100151700002200172700002100194856007300215       2007                        eng d00aHiding Sensitive Trajectory Patterns0 aHiding Sensitive Trajectory Patterns  a693-6981 aAbul, Osman1 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/hiding-sensitive-trajectory-patterns00364nas a2200133   4500008004100000245002100041210002100062300001200083100001600095700002100111700002200132700002100154856005500175       2007                        eng d00aHiding Sequences0 aHiding Sequences  a147-1561 aAbul, Osman1 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/hiding-sequences-000362nas a2200133   4500008004100000245002100041210002100062300001200083100001600095700002100111700002200132700002100154856005300175       2007                        eng d00aHiding Sequences0 aHiding Sequences  a233-2411 aAbul, Osman1 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/hiding-sequences00391nas a2200121   4500008003900000245005000039210005000089300000900139490000700148100001800155700001900173856007700192       2007                          d00aKnowledge discovery from spatial transactions0 aKnowledge discovery from spatial transactions  a1-220 v281 aRinzivillo, S1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/knowledge-discovery-spatial-transactions00490nas a2200133   4500008004100000245006500041210006400106300001200170100002500182700002200207700002100229700001900250856008700269       2007                        eng d00aMining Clinical Data with a Temporal Dimension: A Case Study0 aMining Clinical Data with a Temporal Dimension A Case Study  a429-4361 aBerlingerio, Michele1 aBonchi, Francesco1 aGiannotti, Fosca1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/mining-clinical-data-temporal-dimension-case-study00446nas a2200133   4500008003900000245006200039210006000101260002800161100002200189700002100211700002000232700001800252856004200270       2007                          d00aOntology-Driven Association Rule Extraction: A Case Study0 aOntologyDriven Association Rule Extraction A Case Study  aRoskilde, Denmarkc20071 aFurletti, Barbara1 aBellandi, Andrea1 aGrossi, Valerio1 aRomei, Andrea  uhttp://ceur-ws.org/Vol-298/paper1.pdf00496nas a2200145   4500008004100000245005700041210005600098300001200154100002100166700002200187700002100209700002000230700001600250856008400266       2007                        eng d00aPrivacy-Aware Knowledge Discovery from Location Data0 aPrivacyAware Knowledge Discovery from Location Data  a283-2871 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino1 aAbul, Osman  uhttps://kdd.isti.cnr.it/content/privacy-aware-knowledge-discovery-location-data00577nas a2200145   4500008003900000020002200039245008000061210006900141260000900210100002200219700002100241700001800262700002000280856013100300       2007                          d  a978-972-8924-44-700aPUSHING CONSTRAINTS IN ASSOCIATION RULE MINING: AN ONTOLOGY-BASED APPROACH 0 aPUSHING CONSTRAINTS IN ASSOCIATION RULE MINING AN ONTOLOGYBASED   c20071 aFurletti, Barbara1 aBellandi, Andrea1 aRomei, Andrea1 aGrossi, Valerio  uhttp://www.iadisportal.org/digital-library/mdownload/pushing-constraints-in-association-rule-mining-an-ontology-based-approach00460nas a2200133   4500008004100000245005300041210005200094300001200146100002500158700002200183700002100205700001900226856008100245       2007                        eng d00aTime-Annotated Sequences for Medical Data Mining0 aTimeAnnotated Sequences for Medical Data Mining  a133-1381 aBerlingerio, Michele1 aBonchi, Francesco1 aGiannotti, Fosca1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/time-annotated-sequences-medical-data-mining00409nas a2200121   4500008004100000245004500041210004400086300001200130100002500142700002200167700002100189856007700210       2007                        eng d00aTowards Constraint-Based Subgraph Mining0 aTowards ConstraintBased Subgraph Mining  a274-2811 aBerlingerio, Michele1 aBonchi, Francesco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/towards-constraint-based-subgraph-mining00386nas a2200133   4500008004100000245003000041210003000071300001200101100002100113700001700134700001900151700002000170856006200190       2007                        eng d00aTrajectory pattern mining0 aTrajectory pattern mining  a330-3391 aGiannotti, Fosca1 aNanni, Mirco1 aPinelli, Fabio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/trajectory-pattern-mining00598nas a2200157   4500008004100000245008300041210006900124300000800193100002200201700002100223700002200244700002300266700002100289700002200310856010800332       2006                        eng d00aConQueSt: a Constraint-based Querying System for Exploratory Pattern Discovery0 aConQueSt a Constraintbased Querying System for Exploratory Patte  a1591 aBonchi, Francesco1 aGiannotti, Fosca1 aLucchese, Claudio1 aOrlando, Salvatore1 aPerego, Raffaele1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/content/conquest-constraint-based-querying-system-exploratory-pattern-discovery00403nas a2200109   4500008004100000245005500041210005500096100002100151700001700172700002000189856008400209       2006                        eng d00aEfficient Mining of Temporally Annotated Sequences0 aEfficient Mining of Temporally Annotated Sequences1 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/efficient-mining-temporally-annotated-sequences00503nas a2200145   4500008003900000245007800039210006900117300001200186490000900198100001900207700002100226700002200247700001800269856007000287       2006                          d00aExamples of Integration of Induction and Deduction in Knowledge Discovery0 aExamples of Integration of Induction and Deduction in Knowledge   a307-3260 v41551 aTurini, Franco1 aBaglioni, Miriam1 aFurletti, Barbara1 aRinzivillo, S  uhttp://www.springerlink.com/content/m400v4507476n18g/fulltext.pdf00513nas a2200133   4500008003900000245007800039210006900117300001200186100001900198700002100217700002200238700001800260856010100278       2006                          d00aExamples of Integration of Induction and Deduction in Knowledge Discovery0 aExamples of Integration of Induction and Deduction in Knowledge   a307-3261 aTurini, Franco1 aBaglioni, Miriam1 aFurletti, Barbara1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/content/examples-integration-induction-and-deduction-knowledge-discovery00543nas a2200157   4500008004100000245006000041210005700101300001000158100002200168700002100190700002200211700002300233700002100256700002200277856008600299       2006                        eng d00aOn Interactive Pattern Mining from Relational Databases0 aInteractive Pattern Mining from Relational Databases  a42-621 aBonchi, Francesco1 aGiannotti, Fosca1 aLucchese, Claudio1 aOrlando, Salvatore1 aPerego, Raffaele1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/content/interactive-pattern-mining-relational-databases-000543nas a2200157   4500008004100000245006000041210005700101300001200158100002200170700002200192700002100214700002300235700002100258700002200279856008400301       2006                        eng d00aOn Interactive Pattern Mining from Relational Databases0 aInteractive Pattern Mining from Relational Databases  a329-3381 aLucchese, Claudio1 aBonchi, Francesco1 aGiannotti, Fosca1 aOrlando, Salvatore1 aPerego, Raffaele1 aTrasarti, Roberto  uhttps://kdd.isti.cnr.it/content/interactive-pattern-mining-relational-databases00350nas a2200109   4500008004100000245003800041210003800079100001400117700002500131700001800156856006600174       2006                        eng d00aMaximum Entropy Reasoning for GIS0 aMaximum Entropy Reasoning for GIS1 aHosni, H.1 aMasserotti, Maria, V1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/maximum-entropy-reasoning-gis00524nas a2200157   4500008004100000245005000041210005000091300001200141100002500153700002200178700002100200700002000221700002100241700002200262856008200284       2006                        eng d00aMining HLA Patterns Explaining Liver Diseases0 aMining HLA Patterns Explaining Liver Diseases  a702-7071 aBerlingerio, Michele1 aBonchi, Francesco1 aChelazzi, Silvia1 aCurcio, Michele1 aGiannotti, Fosca1 aScatena, Fabrizio  uhttps://kdd.isti.cnr.it/content/mining-hla-patterns-explaining-liver-diseases00432nas a2200133   4500008004100000245004700041210004700088300001200135100002100147700001700168700002000185700001900205856007400224       2006                        eng d00aMining sequences with temporal annotations0 aMining sequences with temporal annotations  a593-5971 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aPinelli, Fabio  uhttps://kdd.isti.cnr.it/content/mining-sequences-temporal-annotations00430nas a2200121   4500008004100000245006200041210006100103300001200164490000700176100001700183700002000200856008800220       2006                        eng d00aTime-focused clustering of trajectories of moving objects0 aTimefocused clustering of trajectories of moving objects  a267-2890 v271 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/time-focused-clustering-trajectories-moving-objects00568nas a2200133   4500008003900000020001800039245009200057210006900149260000900218100002100227700002200248700001900270856014500289       2006                          d  a972-8924-09-700aA Tool for Economic Plans analysis based on expert knowledge and data mining techniques0 aTool for Economic Plans analysis based on expert knowledge and d  c20061 aBaglioni, Miriam1 aFurletti, Barbara1 aTurini, Franco  uhttp://www.iadisportal.org/digital-library/mdownload/a-tool-for-economic-plans-analysis-based-on-expert-knowledge-and-data-mining-techniques00506nas a2200133   4500008004100000245006800041210006700109300001200176100002100188700002200209700002100231700002000252856010000272       2006                        eng d00aTowards low-perturbation anonymity preserving pattern discovery0 aTowards lowperturbation anonymity preserving pattern discovery  a588-5921 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/towards-low-perturbation-anonymity-preserving-pattern-discovery00388nas a2200133   4500008004100000245003000041210003000071490000700101100002100108700002200129700002100151700002000172856006200192       2005                        eng d00aAnonymity and data mining0 aAnonymity and data mining0 v201 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/anonymity-and-data-mining00495nas a2200133   4500008004100000245006500041210006500106300001200171100002100183700002200204700002100226700002000247856009400267       2005                        eng d00aBlocking Anonymity Threats Raised by Frequent Itemset Mining0 aBlocking Anonymity Threats Raised by Frequent Itemset Mining  a561-5641 aAtzori, Maurizio1 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/blocking-anonymity-threats-raised-frequent-itemset-mining00554nas a2200133   4500008004100000245009800041210006900139100001800208700001600226700002100242700001600263700001700279856012400296       2005                        eng d00aComparative indicators of regional poverty and deprivation: Poland versus EU-15 Member States0 aComparative indicators of regional poverty and deprivation Polan1 aBetti, Gianni1 aMulas, Anna1 aNatilli, Michela1 aNeri, Laura1 aVerma, Vijay  uhttps://kdd.isti.cnr.it/publications/comparative-indicators-regional-poverty-and-deprivation-poland-versus-eu-15-member00492nas a2200133   4500008003900000020001800039245005500057210005200112260003500164100002100199700002200220700001900242856009700261       2005                          d  a1-58113-964-000aDrC4.5: Improving C4.5 by means of Prior Knowledge0 aDrC45 Improving C45 by means of Prior Knowledge  aSanta Fe, New Mexico, USAbACM1 aBaglioni, Miriam1 aFurletti, Barbara1 aTurini, Franco  uhttp://dl.acm.org/ft_gateway.cfm?id=1066787&ftid=311609&dwn=1&CFID=96873366&CFTOKEN=5923351100545nas a2200145   4500008004100000245008100041210006900122300001200191490000600203100002200209700002100231700002200252700002000274856010500294       2005                        eng d00aEfficient breadth-first mining of frequent pattern with monotone constraints0 aEfficient breadthfirst mining of frequent pattern with monotone   a131-1530 v81 aBonchi, Francesco1 aGiannotti, Fosca1 aMazzanti, Alessio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/efficient-breadth-first-mining-frequent-pattern-monotone-constraints00501nas a2200145   4500008004100000245006300041210006100104300001000165490000700175100002200182700002100204700002200225700002000247856008800267       2005                        eng d00aExante: A Preprocessing Method for Frequent-Pattern Mining0 aExante A Preprocessing Method for FrequentPattern Mining  a25-310 v201 aBonchi, Francesco1 aGiannotti, Fosca1 aMazzanti, Alessio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/exante-preprocessing-method-frequent-pattern-mining00424nas a2200109   4500008003900000245006700039210006700106300001000173100001800183700001900201856009400220       2005                          d00aExtracting spatial association rules from spatial transactions0 aExtracting spatial association rules from spatial transactions  a79-861 aRinzivillo, S1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/extracting-spatial-association-rules-spatial-transactions00480nas a2200121   4500008004100000245006900041210006700110100001700177700001800194700002100212700001900233856010600252       2005                        eng d00aIndicators of social exclusion and poverty in Europe’s regions0 aIndicators of social exclusion and poverty in Europe s regions1 aVerma, Vijay1 aBetti, Gianni1 aNatilli, Michela1 aLemmi, Achille  uhttps://kdd.isti.cnr.it/publications/indicators-social-exclusion-and-poverty-europe%E2%80%99s-regions00479nas a2200133   4500008004100000245006200041210006200103300001000165100002100175700002000196700002000216700001800236856009100254       2005                        eng d00aSynthetic generation of cellular network positioning data0 aSynthetic generation of cellular network positioning data  a12-201 aGiannotti, Fosca1 aMazzoni, Andrea1 aPuntoni, Simone1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/synthetic-generation-cellular-network-positioning-data00481nas a2200133   4500008004100000245006200041210006200103300001000165100002100175700002000196700002000216700001800236856009300254       2005                        eng d00aSynthetic generation of cellular network positioning data0 aSynthetic generation of cellular network positioning data  a12-201 aGiannotti, Fosca1 aMazzoni, Andrea1 aPuntoni, Simone1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/synthetic-generation-cellular-network-positioning-data-000392nas a2200121   4500008004100000245004500041210004500086300001200131490000700143100002000150700002400170856007600194       2004                        eng d00aBounded Nondeterminism of Logic Programs0 aBounded Nondeterminism of Logic Programs  a313-3430 v421 aPedreschi, Dino1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/bounded-nondeterminism-logic-programs-000440nas a2200121   4500008004100000245005800041210005800099300001200157100002000169700002400189700002100213856008400234       2004                        eng d00aCharacterisations of Termination in Logic Programming0 aCharacterisations of Termination in Logic Programming  a376-4311 aPedreschi, Dino1 aRuggieri, Salvatore1 aSmaus, Jan-Georg  uhttps://kdd.isti.cnr.it/content/characterisations-termination-logic-programming00392nas a2200109   4500008003900000245005500039210005500094300001200149100001800161700001900179856008400198       2004                          d00aClassification in Geographical Information Systems0 aClassification in Geographical Information Systems  a374-3851 aRinzivillo, S1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/classification-geographical-information-systems00480nas a2200133   4500008004100000245006200041210006100103300001100164100001700175700002600192700001800218700001900236856009100255       2004                        eng d00aDeductive and Inductive Reasoning on Spatio-Temporal Data0 aDeductive and Inductive Reasoning on SpatioTemporal Data  a98-1151 aNanni, Mirco1 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/deductive-and-inductive-reasoning-spatio-temporal-data00457nas a2200133   4500008004100000245005400041210005400095300001100149100001700160700002600177700001800203700001900221856008300240       2004                        eng d00aDeductive and Inductive Reasoning on Trajectories0 aDeductive and Inductive Reasoning on Trajectories  a98-1051 aNanni, Mirco1 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/deductive-and-inductive-reasoning-trajectories00472nas a2200133   4500008004100000245006100041210006100102300001000163100001700173700002100190700001700211700002000228856009000248       2004                        eng d00aDiscovery of ads web hosts through traffic data analysis0 aDiscovery of ads web hosts through traffic data analysis  a76-811 aBacarella, V1 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/discovery-ads-web-hosts-through-traffic-data-analysis00452nas a2200121   4500008004100000245006200041210006200103300001200165100002200177700002100199700002000220856009000240       2004                        eng d00aFrequent Pattern Queries for Flexible Knowledge Discovery0 aFrequent Pattern Queries for Flexible Knowledge Discovery  a250-2611 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/frequent-pattern-queries-flexible-knowledge-discovery00571nas a2200145   4500008004100000245009100041210006900132300001200201490000700213100002200220700002600242700001800268700001900286856012000305       2004                        eng d00aIntegrating knowledge representation and reasoning in Geographical Information Systems0 aIntegrating knowledge representation and reasoning in Geographic  a417-4470 v181 aMancarella, Paolo1 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/integrating-knowledge-representation-and-reasoning-geographical-information-systems00488nas a2200121   4500008004100000245007100041210006900112100002200181700002600203700001800229700001900247856010000266       2004                        eng d00aIntegrating Knowledge Representation and Reasoning in Geographical0 aIntegrating Knowledge Representation and Reasoning in Geographic1 aMancarella, Paolo1 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/integrating-knowledge-representation-and-reasoning-geographical00446nas a2200133   4500008004100000245005400041210005300095300000800148100001500156700002400171700002000195700001600215856008100231       2004                        eng d00aIT4PS: information technology for problem solving0 aIT4PS information technology for problem solving  a2411 aAlfonsi, C1 aScarabottolo, Nello1 aPedreschi, Dino1 aSimi, Maria  uhttps://kdd.isti.cnr.it/content/it4ps-information-technology-problem-solving00711nas a2200157   4500008004100000020001800041245018500059210006900244260001300313490000900326100003000335700002300365700002100388700002000409856012400429       2004                        eng d  a3-540-23108-000aKnowledge Discovery in Databases: PKDD 2004, 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pisa, Italy, September 20-24, 2004, Proceedings0 aKnowledge Discovery in Databases PKDD 2004 8th European Conferen  bSpringer0 v32021 aBoulicaut, Jean-François1 aEsposito, Floriana1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/knowledge-discovery-databases-pkdd-2004-8th-european-conference-principles-and-practice00658nas a2200157   4500008004100000020001800041245012700059210006900186260001300255490000900268100003000277700002300307700002100330700002000351856012900371       2004                        eng d  a3-540-23105-600aMachine Learning: ECML 2004, 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings0 aMachine Learning ECML 2004 15th European Conference on Machine L  bSpringer0 v32011 aBoulicaut, Jean-François1 aEsposito, Floriana1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/machine-learning-ecml-2004-15th-european-conference-machine-learning-pisa-italy-september-2000488nas a2200121   4500008004100000245007700041210006900118100001700187700002600204700001800230700001900248856009900267       2004                        eng d00a\newblock{A Declarative Framework for Reasoning on Spatio-temporal Data}0 anewblock A Declarative Framework for Reasoning on Spatiotemporal1 aNanni, Mirco1 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/newblocka-declarative-framework-reasoning-spatio-temporal-data00379nas a2200109   4500008004100000245004900041210004900090300000900139100002200148700002100170856007800191       2004                        eng d00aPushing Constraints to Detect Local Patterns0 aPushing Constraints to Detect Local Patterns  a1-191 aBonchi, Francesco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/pushing-constraints-detect-local-patterns00466nas a2200121   4500008004100000245006900041210006600110300001000176100002200186700002100208700002000229856009500249       2004                        eng d00aA Relational Query Primitive for Constraint-Based Pattern Mining0 aRelational Query Primitive for ConstraintBased Pattern Mining  a14-371 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/relational-query-primitive-constraint-based-pattern-mining00496nas a2200133   4500008004100000245007200041210006900113300001400182490000700196100002100203700002000224700001900244856009900263       2004                        eng d00aSpecifying Mining Algorithms with Iterative User-Defined Aggregates0 aSpecifying Mining Algorithms with Iterative UserDefined Aggregat  a1232-12460 v161 aGiannotti, Fosca1 aManco, Giuseppe1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/specifying-mining-algorithms-iterative-user-defined-aggregates00412nas a2200121   4500008004100000245005100041210005100092300001000143100002100153700002000174700001900194856007700213       2004                        eng d00aTowards a Logic Query Language for Data Mining0 aTowards a Logic Query Language for Data Mining  a76-941 aGiannotti, Fosca1 aManco, Giuseppe1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/towards-logic-query-language-data-mining00476nas a2200133   4500008004100000245005900041210005900100300001000159100002200169700002100191700002200212700002000234856008800254       2003                        eng d00aAdaptive Constraint Pushing in Frequent Pattern Mining0 aAdaptive Constraint Pushing in Frequent Pattern Mining  a47-581 aBonchi, Francesco1 aGiannotti, Fosca1 aMazzanti, Alessio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/adaptive-constraint-pushing-frequent-pattern-mining00533nas a2200133   4500008004100000245008400041210006900125300001000194100002200204700002100226700002200247700002000269856011000289       2003                        eng d00aExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraint0 aExAMiner Optimized Levelwise Frequent Pattern Mining with Monoto  a11-181 aBonchi, Francesco1 aGiannotti, Fosca1 aMazzanti, Alessio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/examiner-optimized-level-wise-frequent-pattern-mining-monotone-constraint00504nas a2200133   4500008004100000245006900041210006800110300001000178100002200188700002100210700002200231700002000253856009700273       2003                        eng d00aExAnte: Anticipated Data Reduction in Constrained Pattern Mining0 aExAnte Anticipated Data Reduction in Constrained Pattern Mining  a59-701 aBonchi, Francesco1 aGiannotti, Fosca1 aMazzanti, Alessio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/exante-anticipated-data-reduction-constrained-pattern-mining00373nas a2200121   4500008004100000245004200041210003900083300001200122490000700134100002000141700002400161856006600185       2003                        eng d00aOn logic programs that always succeed0 alogic programs that always succeed  a163-1960 v481 aPedreschi, Dino1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/logic-programs-always-succeed00374nas a2200121   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   2003                        eng d00aPre-processing for Constrained Pattern Mining0 aPreprocessing for Constrained Pattern Mining  a519-5301 aBonchi, Francesco1 aGiannotti, Fosca1 aMazzanti, Alessio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/pre-processing-constrained-pattern-mining00434nas a2200121   4500008004100000245005700041210005700098300001000155100002600165700001800191700001900209856008400228       2003                        eng d00aQualitative Spatial Reasoning in a Logical Framework0 aQualitative Spatial Reasoning in a Logical Framework  a78-901 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/qualitative-spatial-reasoning-logical-framework00432nas a2200121   4500008003900000245005700039210005700096300001200153100002300165700002300188700001800211856008100229       2003                          d00aUsing Spin to Generate Tests from ASM Specifications0 aUsing Spin to Generate Tests from ASM Specifications  a263-2771 aGargantini, Angelo1 aRiccobene, Elvinia1 aRinzivillo, S  uhttps://kdd.isti.cnr.it/content/using-spin-generate-tests-asm-specifications00467nas a2200133   4500008004100000245005900041210005800100300001200158100002100170700001700191700002000208700001800228856008700246       2003                        eng d00aWebCat: Automatic Categorization of Web Search Results0 aWebCat Automatic Categorization of Web Search Results  a507-5181 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino1 aSamaritani, F  uhttps://kdd.isti.cnr.it/content/webcat-automatic-categorization-web-search-results00497nas a2200121   4500008004100000245008500041210006900126300000800195100002100203700002000224700002000244856011100264       2002                        eng d00aCharacterizing Web User Accesses: A Transactional Approach to Web Log Clustering0 aCharacterizing Web User Accesses A Transactional Approach to Web  a3121 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/characterizing-web-user-accesses-transactional-approach-web-log-clustering00415nas a2200133   4500008004100000245004200041210004200083300001200125490000600137100002000143700002400163700002100187856007300208       2002                        eng d00aClasses of terminating logic programs0 aClasses of terminating logic programs  a369-4180 v21 aPedreschi, Dino1 aRuggieri, Salvatore1 aSmaus, Jan-Georg  uhttps://kdd.isti.cnr.it/content/classes-terminating-logic-programs-000372nas a2200121   4500008004100000245003400041210003400075300001200109100002100121700002000142700002000162856006800182       2002                        eng d00aClustering Transactional Data0 aClustering Transactional Data  a175-1871 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/clustering-transactional-data-000327nas a2200109   4500008004100000245003400041210003000075300001100105100002200116700002000138856005900158       2002                        eng d00aThe Declarative Side of Magic0 aDeclarative Side of Magic  a83-1081 aMascellani, Paolo1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/declarative-side-magic00410nas a2200121   4500008004100000245004900041210004800090300001000138100002600148700001900174700001800193856007700211       2002                        eng d00aEnhancing GISs for spatio-temporal reasoning0 aEnhancing GISs for spatiotemporal reasoning  a42-481 aRaffaetà, Alessandra1 aTurini, Franco1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/enhancing-giss-spatio-temporal-reasoning00358nas a2200097   4500008004100000245005400041210005300095300000600148100002100154856008500175       2002                        eng d00aInvited talk: Logical Data Mining Query Languages0 aInvited talk Logical Data Mining Query Languages  a11 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/invited-talk-logical-data-mining-query-languages00457nas a2200109   4500008004100000245008300041210006900124300001200193100002100205700002000226856010100246       2002                        eng d00aLDL-M$_{\mbox{ine}}$: Integrating Data Mining with Intelligent Query Answering0 aLDLM mbox ine Integrating Data Mining with Intelligent Query Ans  a517-5201 aGiannotti, Fosca1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/ldl-mmboxine-integrating-data-mining-intelligent-query-answering00480nas a2200121   4500008004100000245007100041210006900112300001200181100002200193700002000215700002400235856009900259       2002                        eng d00aNegation as Failure through Abduction: Reasoning about Termination0 aNegation as Failure through Abduction Reasoning about Terminatio  a240-2721 aMancarella, Paolo1 aPedreschi, Dino1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/negation-failure-through-abduction-reasoning-about-termination00432nas a2200121   4500008004100000245005600041210005500097300001200152100002600164700001800190700001900208856008300227       2002                        eng d00aQualitative Reasoning in a Spatio-Temporal Language0 aQualitative Reasoning in a SpatioTemporal Language  a105-1181 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/qualitative-reasoning-spatio-temporal-language00398nas a2200121   4500008004100000245004200041210004200083490001500125100002000140700002400160700002100184856007100205       2001                        eng d00aClasses of Terminating Logic Programs0 aClasses of Terminating Logic Programs0 vcs.LO/01061 aPedreschi, Dino1 aRuggieri, Salvatore1 aSmaus, Jan-Georg  uhttps://kdd.isti.cnr.it/content/classes-terminating-logic-programs00370nas a2200121   4500008004100000245003400041210003400075300001200109100002100121700002000142700002000162856006600182       2001                        eng d00aClustering Transactional Data0 aClustering Transactional Data  a163-1761 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/clustering-transactional-data00429nas a2200133   4500008004100000245004300041210004300084300001200127100002100139700002600160700001800186700001900204856007200223       2001                        eng d00aComplex Reasoning on Geographical Data0 aComplex Reasoning on Geographical Data  a331-3381 aGiannotti, Fosca1 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/complex-reasoning-geographical-data00431nas a2200133   4500008004100000245004300041210004300084300001200127100002100139700002600160700001800186700001900204856007400223       2001                        eng d00aComplex Reasoning on Geographical Data0 aComplex Reasoning on Geographical Data  a331-3381 aGiannotti, Fosca1 aRaffaetà, Alessandra1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/complex-reasoning-geographical-data-000527nas a2200169   4500008004100000245004400041210004400085300001200129100002200141700002100163700002000184700001800204700001700222700002000239700002400259856007400283       2001                        eng d00aData Mining for Intelligent Web Caching0 aData Mining for Intelligent Web Caching  a599-6031 aBonchi, Francesco1 aGiannotti, Fosca1 aManco, Giuseppe1 aRenso, Chiara1 aNanni, Mirco1 aPedreschi, Dino1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/data-mining-intelligent-web-caching-000525nas a2200169   4500008004100000245004400041210004400085300001200129100002200141700002100163700002000184700001800204700001700222700002000239700002400259856007200283       2001                        eng d00aData Mining for Intelligent Web Caching0 aData Mining for Intelligent Web Caching  a599-6031 aBonchi, Francesco1 aGiannotti, Fosca1 aManco, Giuseppe1 aRenso, Chiara1 aNanni, Mirco1 aPedreschi, Dino1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/data-mining-intelligent-web-caching00467nas a2200145  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uhttps://kdd.isti.cnr.it/content/web-log-data-warehousing-and-mining-intelligent-web-caching-000607nas a2200169   4500008004100000245006800041210006800109100002200177700002100199700002000220700002000240700001700260700002000277700001800297700002400315856009800339       2001                        eng d00aWeb Log Data Warehousing and Mining for Intelligent Web Caching0 aWeb Log Data Warehousing and Mining for Intelligent Web Caching1 aBonchi, Francesco1 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/web-log-data-warehousing-and-mining-intelligent-web-caching-100648nas a2200193   4500008004100000245006800041210006800109300001200177490000700189100002200196700002100218700002000239700002000259700001700279700002000296700001800316700002400334856009600358       2001                        eng d00aWeb log data warehousing and mining for intelligent web caching0 aWeb log data warehousing and mining for intelligent web caching  a165-1890 v391 aBonchi, Francesco1 aGiannotti, Fosca1 aGozzi, Cristian1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino1 aRenso, Chiara1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/web-log-data-warehousing-and-mining-intelligent-web-caching00456nas a2200109   4500008004100000245007800041210006900119300001200188100002100200700002000221856010500241       2000                        eng d00aDeclarative Knowledge Extraction with Interactive User-Defined Aggregates0 aDeclarative Knowledge Extraction with Interactive UserDefined Ag  a435-4441 aGiannotti, Fosca1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/declarative-knowledge-extraction-interactive-user-defined-aggregates00468nas a2200145   4500008004100000245005100041210005100092300001200143490000700155100002100162700001700183700002000200700002200220856008000242       2000                        eng d00aFoundations of distributed interaction systems0 aFoundations of distributed interaction systems  a127-1680 v281 aFayzullin, Marat1 aNanni, Mirco1 aPedreschi, Dino1 aSubrahmanian, V S  uhttps://kdd.isti.cnr.it/content/foundations-distributed-interaction-systems00408nas a2200121   4500008004100000245004900041210004800090300001200138100002100150700001700171700002000188856007800208       2000                        eng d00aLogic-Based Knowledge Discovery in Databases0 aLogicBased Knowledge Discovery in Databases  a279-2831 aGiannotti, Fosca1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/logic-based-knowledge-discovery-databases00395nas a2200109   4500008004100000245005300041210005300094300001200147100002100159700002000180856008500200       2000                        eng d00aMaking Knowledge Extraction and Reasoning Closer0 aMaking Knowledge Extraction and Reasoning Closer  a360-3711 aGiannotti, Fosca1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/making-knowledge-extraction-and-reasoning-closer00413nas a2200109   4500008004100000245005900041210005900100300001200159100002600171700001800197856008800215       2000                        eng d00aTemporal Reasoning in Geographical Information Systems0 aTemporal Reasoning in Geographical Information Systems  a899-9051 aRaffaetà, Alessandra1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/temporal-reasoning-geographical-information-systems00486nas a2200157   4500008004100000245004800041210004800089300000900137490000700146100002300153700002300176700001500199700001800214700001900232856007700251       2000                        eng d00aUsing MedLan to Integrate Geographical Data0 aUsing MedLan to Integrate Geographical Data  a3-140 v431 aAquilino, Domenico1 aAsirelli, Patrizia1 aFormuso, A1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/using-medlan-integrate-geographical-data00471nas a2200145   4500008004100000245004800041210004800089300001100137100002300148700002300171700001500194700001800209700001900227856007900246       2000                        eng d00aUsing Medlan to Integrate Geographical Data0 aUsing Medlan to Integrate Geographical Data  a3–141 aAquilino, Domenico1 aAsirelli, Patrizia1 aFormuso, A1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/using-medlan-integrate-geographical-data-000405nas a2200121   4500008004100000245004800041210004500089300001200134100002200146700002100168700002000189856007400209       2000                        eng d00aOn Verification in Logic Database Languages0 aVerification in Logic Database Languages  a957-9711 aBonchi, Francesco1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/verification-logic-database-languages00476nas a2200121   4500008004100000245007200041210006900113300000800182100002100190700002300211700002400234856009600258       1999                        eng d00aBeyond Current Technology: The Perspective of Three EC GIS Projects0 aBeyond Current Technology The Perspective of Three EC GIS Projec  a5101 aGiannotti, Fosca1 aJeansoulin, Robert1 aTheodoridis, Yannis  uhttps://kdd.isti.cnr.it/content/beyond-current-technology-perspective-three-ec-gis-projects00371nas a2200109   4500008004100000245004500041210004500086300001200131100002000143700002400163856007400187       1999                        eng d00aBounded Nondeterminism of Logic Programs0 aBounded Nondeterminism of Logic Programs  a350-3641 aPedreschi, Dino1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/bounded-nondeterminism-logic-programs00539nas a2200133   4500008004100000245008800041210006900129300001200198100002200210700002100232700002100253700002000274856011100294       1999                        eng d00aA Classification-Based Methodology for Planning Audit Strategies in Fraud Detection0 aClassificationBased Methodology for Planning Audit Strategies in  a175-1841 aBonchi, Francesco1 aGiannotti, Fosca1 aMainetto, Gianni1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/classification-based-methodology-planning-audit-strategies-fraud-detection00444nas a2200133   4500008004100000245005500041210005500096300001200151490000700163100001900170700001800189700001900207856008400226       1999                        eng d00aDynamic composition of parameterised logic modules0 aDynamic composition of parameterised logic modules  a211-2420 v251 aBrogi, Antonio1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/dynamic-composition-parameterised-logic-modules00429nas a2200121   4500008004100000245005500041210005500096300001400151100001900165700001800184700001900202856008600221       1999                        eng d00aDynamic Composition of Parameterised Logic Modules0 aDynamic Composition of Parameterised Logic Modules  a211–2421 aBrogi, Antonio1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/dynamic-composition-parameterised-logic-modules-000511nas a2200133   4500008004100000245007500041210006900116300001200185100002100197700002000218700002000238700001900258856010000277       1999                        eng d00aExperiences with a Logic-Based Knowledge Discovery Support Environment0 aExperiences with a LogicBased Knowledge Discovery Support Enviro  a202-2131 aGiannotti, Fosca1 aManco, Giuseppe1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/experiences-logic-based-knowledge-discovery-support-environment00489nas a2200121   4500008004100000245007500041210006900116100002100185700002000206700002000226700001900246856010200265       1999                        eng d00aExperiences with a Logic-based knowledge discovery Support Environment0 aExperiences with a Logicbased knowledge discovery Support Enviro1 aGiannotti, Fosca1 aManco, Giuseppe1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/experiences-logic-based-knowledge-discovery-support-environment-000544nas a2200145   4500008004100000245007700041210006900118300001200187100002100199700002000220700001700240700002000257700001900277856010200296       1999                        eng d00aIntegration of Deduction and Induction for Mining Supermarket Sales Data0 aIntegration of Deduction and Induction for Mining Supermarket Sa  a117-1311 aGiannotti, Fosca1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/integration-deduction-and-induction-mining-supermarket-sales-data00340nas a2200109   4500008004100000245003900041210003600080490000700116100002000123700002400143856006300167       1999                        eng d00aOn Logic Programs That Do Not Fail0 aLogic Programs That Do Not Fail0 v301 aPedreschi, Dino1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/logic-programs-do-not-fail00449nas a2200109   4500008004100000245007300041210006900114300001200183100002100195700002000216856010300236       1999                        eng d00aQuerying inductive Databases via Logic-Based user-defined aggregates0 aQuerying inductive Databases via LogicBased userdefined aggregat  a605-6201 aGiannotti, Fosca1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/querying-inductive-databases-logic-based-user-defined-aggregates-000447nas a2200109   4500008004100000245007300041210006900114300001200183100002100195700002000216856010100236       1999                        eng d00aQuerying Inductive Databases via Logic-Based User-Defined Aggregates0 aQuerying Inductive Databases via LogicBased UserDefined Aggregat  a125-1351 aGiannotti, Fosca1 aManco, Giuseppe  uhttps://kdd.isti.cnr.it/content/querying-inductive-databases-logic-based-user-defined-aggregates00575nas a2200133   4500008004100000245011500041210006900156300001000225100002200235700002100257700002100278700002000299856012200319       1999                        eng d00aUna Metodologia Basata sulla Classificazione per la Pianificazione degli Accertamenti nel Rilevamento di Frodi0 aUna Metodologia Basata sulla Classificazione per la Pianificazio  a69-841 aBonchi, Francesco1 aGiannotti, Fosca1 aMainetto, Gianni1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/una-metodologia-basata-sulla-classificazione-la-pianificazione-degli-accertamenti-nel00477nas a2200133   4500008004100000245005900041210005900100300001200159100002200171700002100193700002100214700002000235856008800255       1999                        eng d00aUsing Data Mining Techniques in Fiscal Fraud Detection0 aUsing Data Mining Techniques in Fiscal Fraud Detection  a369-3761 aBonchi, Francesco1 aGiannotti, Fosca1 aMainetto, Gianni1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/using-data-mining-techniques-fiscal-fraud-detection00360nas a2200121   4500008004100000245003500041210003500076300001200111490000700123100002000130700002400150856006400174       1999                        eng d00aVerification of Logic Programs0 aVerification of Logic Programs  a125-1760 v391 aPedreschi, Dino1 aRuggieri, Salvatore  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4500008004100000245009100041210006900132300001000201100002100211700002000232700001700252700002000269856011100289       1998                        eng d00aOn the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases0 aEffective Semantics of Nondeterministic Nonmonotonic Temporal Lo  a58-721 aGiannotti, Fosca1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/effective-semantics-nondeterministic-nonmonotonic-temporal-logic-databases00359nam a2200085   4500008004100000245006300041210006300104100001800167856008800185       1998                        eng d00aMechanisms for Semantic Integration of Deductive Databases0 aMechanisms for Semantic Integration of Deductive Databases1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/mechanisms-semantic-integration-deductive-databases00358nas a2200097   4500008004100000245005100041210004900092100001800141700002400159856007700183       1998                        eng d00aA Mediator 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uhttps://kdd.isti.cnr.it/content/applying-restriction-constraints-deductive-databases00464nas a2200133   4500008004100000245006000041210005600101300001200157100002100169700002000190700001700210700002000227856008300247       1997                        eng d00aDatalog++: A Basis for Active Object-Oriented Databases0 aDatalog A Basis for Active ObjectOriented Databases  a283-3011 aGiannotti, Fosca1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/datalog-basis-active-object-oriented-databases00463nas a2200133   4500008004100000245006000041210005600101300001200157100002100169700002000190700001700210700002000227856008200247       1997                        eng d00aDatalog++: a Basis for Active Object.Oriented Databases0 aDatalog a Basis for Active ObjectOriented Databases  a325-3401 aGiannotti, Fosca1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/datalog-basis-active-objectoriented-databases00485nas a2200121   4500008004100000245007700041210006900118300001200187100002100199700002000220700002000240856010300260       1997                        eng d00aA Deductive Data Model for Representing and Querying Semistructured Data0 aDeductive Data Model for Representing and Querying Semistructure  a129-1401 aGiannotti, Fosca1 aManco, Giuseppe1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/deductive-data-model-representing-and-querying-semistructured-data00403nas a2200121   4500008004100000245005300041210005000094300000800144490000700152100002000159700002200179856008000201       1997                        eng d00aNon-determinism in Deductive Databases - Preface0 aNondeterminism in Deductive Databases Preface  a1-20 v191 aPedreschi, Dino1 aSubrahmanian, V S  uhttps://kdd.isti.cnr.it/content/non-determinism-deductive-databases-preface00487nas a2200145   4500008004100000245006000041210005900101300001100160490000700171100002100178700001800199700002100217700001900238856008400257       1997            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a267-3030 v71 aPedreschi, Dino1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/verification-meta-interpreters00433nas a2200133   4500008004100000245004900041210004700090300001200137490000700149100002200156700002500178700002000203856007600223       1996                        eng d00aA Closer Look at Declarative Interpretations0 aCloser Look at Declarative Interpretations  a147-1800 v281 aApt, Krzysztof, R1 aGabbrielli, Maurizio1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/closer-look-declarative-interpretations00473nas a2200121   4500008004100000245007200041210006900113300001200182100002300194700001800217700001900235856009700254       1996                        eng d00aLanguage Extensions for Semantic Integration of Deductive Databases0 aLanguage Extensions for Semantic Integration of Deductive Databa  a415-4341 aAsirelli, Patrizia1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/language-extensions-semantic-integration-deductive-databases00551nas a2200133   4500008004100000020001800041245010300059210006900162260001300231490000900244100002000253700001900273856012500292       1996                        eng d  a3-540-61814-700aLogic in Databases, International Workshop LID'96, San Miniato, Italy, July 1-2, 1996, Proceedings0 aLogic in Databases International Workshop LID96 San Miniato Ital  bSpringer0 v11541 aPedreschi, Dino1 aZaniolo, Carlo  uhttps://kdd.isti.cnr.it/content/logic-databases-international-workshop-lid96-san-miniato-italy-july-1-2-1996-proceedings00584nas a2200133   4500008004100000245011800041210006900159300001000228100002500238700002100263700002100284700002500305856012000330       1996                        eng d00aRagionamento spazio-temporale con LDLT: primi esperimenti verso un sistema deduttivo per applicazioni geografiche0 aRagionamento spaziotemporale con LDLT primi esperimenti verso un  a73-901 aCarboni, Marilisa, E1 aDeo, Annalisa Di1 aGiannotti, Fosca1 aMasserotti, Maria, V  uhttps://kdd.isti.cnr.it/content/ragionamento-spazio-temporale-con-ldlt-primi-esperimenti-verso-un-sistema-deduttivo00575nas a2200133   4500008004100000245011000041210006900151300001200220100002500232700002100257700002100278700002500299856011700324       1996                        eng d00aSpatio-Temporal Reasoning with LDLT: First Steps Towards a Deductive System for Geographical Applications0 aSpatioTemporal Reasoning with LDLT First Steps Towards a Deducti  a135-1511 aCarboni, Marilisa, E1 aDeo, Annalisa Di1 aGiannotti, Fosca1 aMasserotti, Maria, V  uhttps://kdd.isti.cnr.it/content/spatio-temporal-reasoning-ldlt-first-steps-towards-deductive-system-geographical00389nas a2200121   4500008004100000245004300041210003900084300001300123100002300136700001800159700001900177856007100196       1996                        eng d00aTowards {D}eclarative {GIS} {A}nalysis0 aTowards D eclarative GIS A nalysis  a99–1051 aAquilino, Domenico1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/towards-declarative-gis-analysis-000377nas a2200121   4500008004100000245003700041210003700078300001100115100002300126700001800149700001900167856006900186       1996                        eng d00aTowards Declarative GIS Analysis0 aTowards Declarative GIS Analysis  a98-1041 aAquilino, Domenico1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/towards-declarative-gis-analysis00453nas a2200121   4500008004100000245006400041210006400105300001200169100002000181700001800201700001900219856009300238       1996                        eng d00aUsing Temporary Integrity Constraints to Optimize Databases0 aUsing Temporary Integrity Constraints to Optimize Databases  a430-4351 aMontesi, Danilo1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/using-temporary-integrity-constraints-optimize-databases00450nas a2200109   4500008004100000245007600041210006900117300001200186100002000198700002400218856009800242       1995                   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for Composing Deductive Databases with Theories of Constraints0 aOperator for Composing Deductive Databases with Theories of Cons  a57–701 aAquilino, Domenico1 aAsirelli, Patrizia1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/operator-composing-deductive-databases-theories-constraints-000415nas a2200109   4500008004100000245006500041210006200106300001200168100001800180700002100198856008600219       1994                        eng d00aAn abstract interpreter for the specification language LOTOS0 aabstract interpreter for the specification language LOTOS  a309-3231 aFiore, Franco1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/abstract-interpreter-specification-language-lotos00474nas a2200121   4500008004100000245007300041210006900114300001200183100001900195700001800214700001900232856010100251       1994                        eng d00aAmalgamating Language and Meta-language for Composing Logic Programs0 aAmalgamating Language and Metalanguage for Composing Logic Progr  a408-4221 aBrogi, Antonio1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/amalgamating-language-and-meta-language-composing-logic-programs00538nas a2200121   4500008004100000245011300041210006900154300001200223100002000235700002100255700002100276856011900297       1994                        eng d00aConservative Multigranularity Locking for an Obiect-Oriented Persistent Language via Abstract Interpretation0 aConservative Multigranularity Locking for an ObiectOriented Pers  a329-3491 aAmato, Giuseppe1 aGiannotti, Fosca1 aMainetto, Gianni  uhttps://kdd.isti.cnr.it/content/conservative-multigranularity-locking-obiect-oriented-persistent-language-abstract00514nas a2200133   4500008004100000245007800041210006900119300001000188100001900198700002100217700002000238700001900258856010300277       1994                        eng d00aExpressive Power of Non-Deterministic Operators for Logic-based Languages0 aExpressive Power of NonDeterministic Operators for Logicbased La  a27-401 aCorciulo, Luca1 aGiannotti, Fosca1 aPedreschi, Dino1 aZaniolo, Carlo  uhttps://kdd.isti.cnr.it/content/expressive-power-non-deterministic-operators-logic-based-languages00468nas a2200121   4500008004100000245007300041210006900114300001200183490000700195100002100202700001900223856010400242       1994                        eng d00aGate Splitting in LOTOS Specifications Using Abstract Interpretation0 aGate Splitting in LOTOS Specifications Using Abstract Interpreta  a127-1490 v231 aGiannotti, Fosca1 aLatella, Diego  uhttps://kdd.isti.cnr.it/content/gate-splitting-lotos-specifications-using-abstract-interpretation-000549nas a2200169   4500008004100000245005400041210005400095300001400149100001900163700001700182700002200199700001600221700002000237700001800257700001900275856008500294       1994                        eng d00aImplementations of Program Composition Operations0 aImplementations of Program Composition Operations  a292–3071 aBrogi, Antonio1 aChiarelli, A1 aMancarella, Paolo1 aMazzotta, V1 aPedreschi, Dino1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/implementations-program-composition-operations-100545nas a2200169   4500008004100000245005400041210005400095300001200149100001900161700001700180700002200197700001600219700002000235700001800255700001900273856008300292       1994                        eng d00aImplementations of Program Composition Operations0 aImplementations of Program Composition Operations  a292-3071 aBrogi, Antonio1 aChiarelli, A1 aMancarella, Paolo1 aMazzotta, V1 aPedreschi, Dino1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/implementations-program-composition-operations00547nas a2200169   4500008004100000245005400041210005400095300001200149100001900161700001700180700002200197700001600219700002000235700001800255700001900273856008500292       1994                        eng d00aImplementations of Program Composition Operations0 aImplementations of Program Composition Operations  a292-3071 aBrogi, Antonio1 aChiarelli, A1 aMancarella, Paolo1 aMazzotta, V1 aPedreschi, Dino1 aRenso, Chiara1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/implementations-program-composition-operations-000410nas a2200145   4500008004100000245003000041210003000071300001400101490000700115100001900122700002200141700002000163700001900183856006200202       1994                        eng d00aModular Logic Programming0 aModular Logic Programming  a1361-13980 v161 aBrogi, Antonio1 aMancarella, Paolo1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/modular-logic-programming00375nas a2200097   4500008004100000245006100041210005900102300001200161100002000173856008400193       1994                        eng d00aA Proof Method for Runtime Properties of Prolog Programs0 aProof Method for Runtime Properties of Prolog Programs  a584-5981 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/proof-method-runtime-properties-prolog-programs00361nas a2200109   4500008004100000245004300041210004300084300001000127100002200137700002000159856007200179       1994                        eng d00aProving termination of Prolog programs0 aProving termination of Prolog programs  a46-611 aMascellani, Paolo1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/proving-termination-prolog-programs00510nas a2200121   4500008004100000245009100041210006900132300001200201100002000213700002100233700002100254856011300275       1993                        eng d00aData Sharing Analysis for a Database Programming Lanaguage via Abstract Interpretation0 aData Sharing Analysis for a Database Programming Lanaguage via A  a405-4151 aAmato, Giuseppe1 aGiannotti, Fosca1 aMainetto, Gianni  uhttps://kdd.isti.cnr.it/content/data-sharing-analysis-database-programming-lanaguage-abstract-interpretation00441nas a2200121   4500008004100000245006100041210005900102300001000161100001900171700002100190700002000211856008800231       1993                        eng d00aDatalog with Non-Deterministic Choice Computes NDB-PTIME0 aDatalog with NonDeterministic Choice Computes NDBPTIME  a49-661 aCorciulo, Luca1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/datalog-non-deterministic-choice-computes-ndb-ptime00447nas a2200109   4500008004100000245007300041210006900114300001200183100002100195700001900216856010200235       1993                        eng d00aGate Splitting in LOTOS Specifications Using Abstract Interpretation0 aGate Splitting in LOTOS Specifications Using Abstract Interpreta  a437-4521 aGiannotti, Fosca1 aLatella, Diego  uhttps://kdd.isti.cnr.it/content/gate-splitting-lotos-specifications-using-abstract-interpretation00422nas a2200121   4500008004100000245005600041210005600097300001200153490000800165100002200173700002000195856008500215       1993                        eng d00aReasoning about Termination of Pure Prolog Programs0 aReasoning about Termination of Pure Prolog Programs  a109-1570 v1061 aApt, Krzysztof, R1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/reasoning-about-termination-pure-prolog-programs00494nas a2200121   4500008004100000245008400041210006900125300001000194100002000204700002100224700002100245856010600266       1993                        eng d00aStatic Analysis of Transactions: an Experiment of Abstract Interpretation Usage0 aStatic Analysis of Transactions an Experiment of Abstract Interp  a19-291 aAmato, Giuseppe1 aGiannotti, Fosca1 aMainetto, Gianni  uhttps://kdd.isti.cnr.it/content/static-analysis-transactions-experiment-abstract-interpretation-usage00418nas a2200121   4500008004100000245005600041210005400097300001200151100001700163700001600180700001800196856008200214       1993                        eng d00aA WAM Estesa per la Composizione di Programi Logici0 aWAM Estesa per la Composizione di Programi Logici  a189-2021 aChiarelli, A1 aMazzotta, V1 aRenso, Chiara  uhttps://kdd.isti.cnr.it/content/wam-estesa-la-composizione-di-programi-logici00502nas a2200121   4500008004100000245008600041210006900127300001200196100002000208700002100228700002100249856011000270       1992                        eng d00aAnalysis of Concurrent Transactions in a Functional Database Programming Language0 aAnalysis of Concurrent Transactions in a Functional Database Pro  a174-1841 aAmato, Giuseppe1 aGiannotti, Fosca1 aMainetto, Gianni  uhttps://kdd.isti.cnr.it/content/analysis-concurrent-transactions-functional-database-programming-language00427nas a2200133   4500008004100000245004400041210004400085300001200129100001900141700002200160700002000182700001900202856007200221       1992                        eng d00aMeta for Modularising Logic Programming0 aMeta for Modularising Logic Programming  a105-1191 aBrogi, Antonio1 aMancarella, Paolo1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/meta-modularising-logic-programming00364nam a2200133   4500008004100000245002700041210002300068300001200091100002100103700001500124700002000139700001900159856005200178       1992                        eng d00aThe Type System of LML0 aType System of LML  a313-3321 aBertolino, Bruno1 aMeo, Luigi1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/type-system-lml00451nas a2200109   4500008004100000245007700041210006900118300001200187100002100199700001900220856010200239       1992                        eng d00aUsing Abstract Interpretation for Gate splitting in LOTOS Specifications0 aUsing Abstract Interpretation for Gate splitting in LOTOS Specif  a194-2041 aGiannotti, Fosca1 aLatella, Diego  uhttps://kdd.isti.cnr.it/content/using-abstract-interpretation-gate-splitting-lotos-specifications00425nas a2200133   4500008004100000245004300041210004200084300001200126100002100138700002000159700002100179700001900200856007200219       1991                        eng d00aNon-Determinism in Deductive Databases0 aNonDeterminism in Deductive Databases  a129-1461 aGiannotti, Fosca1 aPedreschi, Dino1 aSaccà, Domenico1 aZaniolo, Carlo  uhttps://kdd.isti.cnr.it/content/non-determinism-deductive-databases00387nas a2200109   4500008004100000245005100041210005100092300001200143100002200155700002000177856008000197       1991                        eng d00aProving Termination of General Prolog Programs0 aProving Termination of General Prolog Programs  a265-2891 aApt, Krzysztof, R1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/proving-termination-general-prolog-programs00481nas a2200109   4500008004100000245008700041210006900128300001200197100002100209700002800230856011300258       1991                        eng d00aA Technique for Recursive Invariance Detection and Selective Program Specification0 aTechnique for Recursive Invariance Detection and Selective Progr  a323-3341 aGiannotti, Fosca1 aHermenegildo, Manuel, V  uhttps://kdd.isti.cnr.it/content/technique-recursive-invariance-detection-and-selective-program-specification00437nas a2200133   4500008004100000245004700041210004700088300001200135100001900147700002200166700002000188700001900208856007600227       1991                        eng d00aTheory Construction in Computational Logic0 aTheory Construction in Computational Logic  a241-2501 aBrogi, Antonio1 aMancarella, Paolo1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/theory-construction-computational-logic00457nas a2200133   4500008004100000245005400041210005400095300001000149100002200159700002000181700002300201700002100224856007800245       1990                        eng d00aAlgebraic Properties of a Class of Logic Programs0 aAlgebraic Properties of a Class of Logic Programs  a23-391 aMancarella, Paolo1 aPedreschi, Dino1 aRondinelli, Marina1 aTagliatti, Marco  uhttps://kdd.isti.cnr.it/content/algebraic-properties-class-logic-programs00434nas a2200109   4500008004100000245006900041210006900110300001000179100002100189700002000210856009400230       1990                        eng d00aDeclarative Semantics for Pruning Operators in Logic Programming0 aDeclarative Semantics for Pruning Operators in Logic Programming  a27-371 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/declarative-semantics-pruning-operators-logic-programming00453nas a2200133   4500008004100000245005200041210005200093300001200145100001900157700002200176700002000198700001900218856008200237       1990                        eng d00aLogic Programming within a Functional Framework0 aLogic Programming within a Functional Framework  a372-3861 aBrogi, Antonio1 aMancarella, Paolo1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/logic-programming-within-functional-framework00385nas a2200109   4500008004100000245005300041210005200094300001200146100001800158700002100176856007800197       1990                        eng d00aRASP: A Resource Allocator for Software Projects0 aRASP A Resource Allocator for Software Projects  a628-6371 aBertazzoni, C1 aGiannotti, Fosca  uhttps://kdd.isti.cnr.it/content/rasp-resource-allocator-software-projects00504nas a2200145   4500008004100000245006500041210006300106300001200169490000600181100002100187700002200208700002000230700001900250856008900269       1990                        eng d00aA Transformational Approach to Negation in Logic Programming0 aTransformational Approach to Negation in Logic Programming  a201-2280 v81 aBarbuti, Roberto1 aMancarella, Paolo1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/transformational-approach-negation-logic-programming00434nas a2200133   4500008004100000245004600041210004600087300001200133100001900145700002200164700002000186700001900206856007500225       1990                        eng d00aUniversal Quantification by Case Analysis0 aUniversal Quantification by Case Analysis  a111-1161 aBrogi, Antonio1 aMancarella, Paolo1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/universal-quantification-case-analysis00329nas a2200109   4500008004100000245003300041210003000074300001400104100002200118700002000140856005900160       1988                        eng d00aAn Algebra of Logic Programs0 aAlgebra of Logic Programs  a1006-10231 aMancarella, Paolo1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/algebra-logic-programs00443nas a2200133   4500008004100000245005400041210005300095300001200148490000600160100002200166700002000188700002000208856008100228       1988                        eng d00aComplete Logic Programs with Domain-Closure Axiom0 aComplete Logic Programs with DomainClosure Axiom  a263-2760 v51 aMancarella, Paolo1 aMartini, Simone1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/complete-logic-programs-domain-closure-axiom00467nas a2200157   4500008004100000245004100041210003900082300001200121100002100133700002200154700001500176700001500191700002000206700001900226856006400245       1988                        eng d00aA Progress Report on the LML Project0 aProgress Report on the LML Project  a675-6841 aBertolino, Bruno1 aMancarella, Paolo1 aMeo, Luigi1 aNini, Luca1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/progress-report-lml-project00531nas a2200133   4500008004100000245008300041210006900124300001100193100002100204700002200225700002000247700001900267856011100286       1987                        eng d00aIntensional Negation of Logic Programs: Examples and Implementation Techniques0 aIntensional Negation of Logic Programs Examples and Implementati  a96-1101 aBarbuti, Roberto1 aMancarella, Paolo1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/intensional-negation-logic-programs-examples-and-implementation-techniques00522nas a2200145   4500008004100000245006900041210006900110300001200179490000600191100002100197700002300218700002000241700001900261856009600280       1987                        eng d00aSymbolic Evaluation with Structural Recursive Symbolic Constants0 aSymbolic Evaluation with Structural Recursive Symbolic Constants  a161-1770 v91 aGiannotti, Fosca1 aMatteucci, Attilio1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/symbolic-evaluation-structural-recursive-symbolic-constants00456nas a2200145   4500008004100000245004500041210004500086300001200131490000700143100002300150700002100173700002000194700001900214856007700233       1985                        eng d00aSymbolic Semantics and Program Reduction0 aSymbolic Semantics and Program Reduction  a784-7940 v111 aAmbriola, Vincenzo1 aGiannotti, Fosca1 aPedreschi, Dino1 aTurini, Franco  uhttps://kdd.isti.cnr.it/content/symbolic-semantics-and-program-reduction00380nas a2200133   4500008004100000245003100041210002700072300001200099100002000111700002100131700001800152700002000170856005600190       1985                        eng d00aThe Type System of Galileo0 aType System of Galileo  a175-1951 aAlbano, Antonio1 aGiannotti, Fosca1 aOrsini, Renzo1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/type-system-galileo00382nas a2200133   4500008004100000245003100041210002700072300001200099100002000111700002100131700001800152700002000170856005800190       1985                        eng d00aThe Type System of Galileo0 aType System of Galileo  a101-1191 aAlbano, Antonio1 aGiannotti, Fosca1 aOrsini, Renzo1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/type-system-galileo-0