02085nas 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/s10676-023-09728-401472nas 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=101538nas 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/10.1145/3557915.356097701528nas 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-inference02096nas 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-healthcare00645nas 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/2102.1307603888nas 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/s41060-021-00253-502976nas 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_1401223nas 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.pdf01953nas 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_1201567nas 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-x01537nas 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/1424-8220/20/24/712201588nas 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/s41060-020-00211-701787nas 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%2Fs41060-020-00213-502283nas 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/2004.1127803169nas 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/2006.0314100389nas 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.021324601492nas 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-x01664nas 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/892013801943nas 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/10.1145/334317201689nas 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-bias00741nas 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/s41109-018-0100-501271nas 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/s13688-018-0133-001229nas 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/s12876-018-0832-901318nas 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/s11042-018-6511-600538nas 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.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/10.1145/323600902012nas 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-users02550nas 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-innovation01579nas 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_1901445nas 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_2501536nas 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-sequences01274nas 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/S246869641730105201373nas 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_1501783nas 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_900584nas 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-data01416nas 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-sum01199nas 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.shtml01549nas 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-services01970nas 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-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-analysis01914nas 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-retail01157nas 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_900574nas 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=2808797.280938402380nas 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/s10618-014-0393-702029nas 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.pdf01436nas 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=2808797.280940101921nas 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-data00674nas 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-sources01995nas 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/s10506-014-9154-600498nas 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.2014.11401701nas 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/10.1145/2554850.255504300447nas 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-cyclists01798nas 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-mining00505nas 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.2014.705809002340nas 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.pdf00483nas 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-car00369nas 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/63400410nas 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-data00434nas 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-networks01416nas 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-databases02131nas 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.2013.12200526nas 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-mobility00487nas 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-algorithm00453nas 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/10.1145/2536669.253667301029nas 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%252fe2013-01715-500754nas 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.2012.08.00101748nas 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-systems00477nas 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-vehicles00430nas 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/10.1145/2339530.233963000502nas 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-communities01549nas 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.2012.18001411nas 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.2012.5100381nas 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-mobility00495nas 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=3599403901804nas 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-analysis01493nas 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-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-data00539nas 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-000537nas 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-system01803nas 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-networks00537nas 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.pdf00607nas 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-mobility01363nas 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-data00457nas 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-discrimination00468nas 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-records00477nas 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-data01604nas 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-000486nas 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-trajectories00451nas 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-discovery00378nas 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-mining00411nas 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-project01351nas 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-threats00418nas 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-mining00627nas 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-clustering00496nas 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-data00386nas 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-mining00403nas 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-sequences00432nas 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-objects00506nas 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-mining00545nas 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-mining00392nas 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-programming00472nas 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-discovery00446nas 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-2000466nas 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-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-succeed00449nas a2200133   4500008004100000245005000041210004900091300001200140100002200152700002100174700002200195700002000217856007800237       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-mining00467nas 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-results00415nas 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-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-magic00480nas 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-termination00398nas 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-programs00527nas 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   4500008004100000245005100041210005000092300001200142490000700154100002100161700002000182700001700202700002000219856008200239       2001                        eng d00aNondeterministic, Nonmonotonic Logic Databases0 aNondeterministic Nonmonotonic Logic Databases  a813-8230 v131 aGiannotti, Fosca1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/nondeterministic-nonmonotonic-logic-databases00525nas a2200133   4500008004100000245008900041210006900130300001000199490000700209100002100216700002000237700001900257856011500276       2001                        eng d00aSemantics and Expressive Power of Nondeterministic Constructs in Deductive Databases0 aSemantics and Expressive Power of Nondeterministic Constructs in  a15-420 v621 aGiannotti, Fosca1 aPedreschi, Dino1 aZaniolo, Carlo  uhttps://kdd.isti.cnr.it/content/semantics-and-expressive-power-nondeterministic-constructs-deductive-databases00650nas a2200193   4500008004100000245006800041210006800109300001200177490000700189100002200196700002100218700002000239700002000259700001700279700002000296700001800316700002400334856009800358       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-caching-000648nas 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-caching00607nas 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-100468nas 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-databases00405nas 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-languages00371nas 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-detection00511nas 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-fail00575nas 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  uhttps://kdd.isti.cnr.it/content/verification-logic-programs00433nas a2200121   4500008004100000245006200041210006000103300001100163490000700174100002100181700002000202856008900222       1998                        eng d00aDatalog with Non-Deterministic Choice Computers NDB-PTIME0 aDatalog with NonDeterministic Choice Computers NDBPTIME  a79-1010 v351 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/datalog-non-deterministic-choice-computers-ndb-ptime00534nas a2200133   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-databases00502nas a2200133   4500008004100000245007000041210006900111300001200180100002100192700002000213700001700233700002000250856009800270       1998                        eng d00aQuery Answering in Nondeterministic, Nonmonotonic Logic Databases0 aQuery Answering in Nondeterministic Nonmonotonic Logic Databases  a175-1871 aGiannotti, Fosca1 aManco, Giuseppe1 aNanni, Mirco1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/query-answering-nondeterministic-nonmonotonic-logic-databases00407nas a2200121   4500008004100000245005100041210005100092300001200143490000700155100002000162700002400182856007900206       1998                        eng d00aWeakest Preconditions for Pure Prolog Programs0 aWeakest Preconditions for Pure Prolog Programs  a145-1500 v671 aPedreschi, Dino1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/weakest-preconditions-pure-prolog-programs00463nas a2200133   4500008004100000245006000041210005600101300001200157100002100169700002000190700001700210700002000227856008200247       1997     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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-preface00367nas a2200121   4500008004100000245003800041210003700079300001200116490000600128100002000134700002400154856006700178       1997                        eng d00aVerification of Meta-Interpreters0 aVerification of MetaInterpreters  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-interpretations00551nas 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-proceedings00450nas a2200109   4500008004100000245007600041210006900117300001200186100002000198700002400218856009800242       1995                        eng d00aA Case Study in Logic Program Verification: the Vanilla Metainterpreter0 aCase Study in Logic Program Verification the Vanilla Metainterpr  a643-6541 aPedreschi, Dino1 aRuggieri, Salvatore  uhttps://kdd.isti.cnr.it/content/case-study-logic-program-verification-vanilla-metainterpreter00531nas a2200133   4500008004100000245008500041210006900126300001200195100002500207700001400232700002100246700002000267856011000287       1995                        eng d00aDeclarative Reconstruction of Updates in Logic Databases: a Compilative Approach0 aDeclarative Reconstruction of Updates in Logic Databases a Compi  a169-1821 aCarboni, Marilisa, E1 aFoddai, V1 aGiannotti, Fosca1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/declarative-reconstruction-updates-logic-databases-compilative-approach-000526nas a2200133   4500008004100000245008500041210006900126300000900195100002500204700002100229700001400250700002000264856010800284       1995                        eng d00aDeclarative Reconstruction of Updates in Logic Databases: A Compilative Approach0 aDeclarative Reconstruction of Updates in Logic Databases A Compi  a3-131 aCarboni, Marilisa, E1 aGiannotti, Fosca1 aFoddai, V1 aPedreschi, Dino  uhttps://kdd.isti.cnr.it/content/declarative-reconstruction-updates-logic-databases-compilative-approach00514nas 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-languages00545nas 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-operations00549nas 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-100547nas 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-programs00441nas 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-ptime00422nas 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-programs00427nas 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-lml00425nas 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-programs00437nas 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-framework00504nas 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-reduction00382nas 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-000380nas 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-galileo