@conference {1547,
	title = {Agnostic Label-Only Membership Inference Attack},
	booktitle = {17th International Conference on Network and System Security},
	year = {2023},
	publisher = {Springer},
	organization = {Springer},
	author = {Anna Monreale and Francesca Naretto and Simone Rizzo}
}
@article {1556,
	title = {Attributed Stream Hypergraphs: temporal modeling of node-attributed high-order interactions},
	journal = {Applied Network Science},
	volume = {8},
	number = {1},
	year = {2023},
	pages = {1{\textendash}19},
	doi = {https://doi.org/10.1002/widm.1356},
	author = {Failla, Andrea and Citraro, Salvatore and Rossetti, Giulio}
}
@conference {1517,
	title = {AUC-based Selective Classification},
	booktitle = {International Conference on Artificial Intelligence and Statistics, 25-27 April 2023, Palau de Congressos, Valencia, Spain},
	year = {2023},
	publisher = {PMLR},
	organization = {PMLR},
	abstract = {Selective classification (or classification with a reject option) pairs a classifier with a selection function to determine whether or not a prediction should be accepted. This framework trades off coverage (probability of accepting a prediction) with predictive performance, typically measured by distributive loss functions. In many application scenarios, such as credit scoring, performance is instead measured by ranking metrics, such as the Area Under the ROC Curve (AUC). We propose a model-agnostic approach to associate a selection function to a given probabilistic binary classifier. The approach is specifically targeted at optimizing the AUC. We provide both theoretical justifications and a novel algorithm, called AUCROSS, to achieve such a goal. Experiments show that our method succeeds in trading-off coverage for AUC, improving over existing selective classification methods targeted at optimizing accuracy.},
	url = {https://proceedings.mlr.press/v206/pugnana23a.html},
	author = {Andrea Pugnana and Salvatore Ruggieri}
}
@conference {1548,
	title = {Evaluating the Privacy Exposure of Interpretable Global and Local Explainers},
	booktitle = {Submitted at Journal of Artificial Intelligence and Law},
	year = {2023},
	author = {Francesca Naretto and Anna Monreale and Fosca Giannotti}
}
@conference {1546,
	title = {EXPHLOT: EXplainable Privacy assessment for Human LOcation Trajectories},
	booktitle = {Discovery Science },
	year = {2023},
	author = {Francesca Naretto and Roberto Pellungrini and Daniele Fadda and Salvo Rinzivillo}
}
@conference {1568,
	title = {Explain and Interpret Few-Shot Learning},
	booktitle = {Joint Proceedings of the xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023), Lisbon, Portugal, July 26-28, 2023},
	year = {2023},
	publisher = {CEUR-WS.org},
	organization = {CEUR-WS.org},
	abstract = {Recent advancements in Artificial Intelligence have been fueled by vast datasets, powerful computing resources, and sophisticated algorithms. However, traditional Machine Learning models face limitations in handling scarce data. Few-Shot Learning (FSL) offers a promising solution by training models on a small number of examples per class. This manuscript introduces FXI-FSL, a framework for eXplainability and Interpretability in FSL, which aims to develop post-hoc explainability algorithms and interpretableby-design alternatives. A noteworthy contribution is the SIamese Network EXplainer (SINEX), a post-hoc approach shedding light on Siamese Network behavior. The proposed framework seeks to unveil the rationale behind FSL models, instilling trust in their real-world applications. Moreover, it emerges as a safeguard for developers, facilitating models fine-tuning prior to deployment, and as a guide for end users navigating the decisions of these models},
	url = {https://ceur-ws.org/Vol-3554/paper38.pdf},
	author = {Andrea Fedele}
}
@article {1550,
	title = {Fair Federated Learning methodology based on Multi-Objective Optimization},
	journal = {Submitted at JAIR},
	year = {2023},
	author = {Michele Fontana, Francesca Naretto, Anna Monreale, Mirco Nanni, Fosca Giannotti}
}
@article {1541,
	title = {Generative AI models should include detection mechanisms as a condition for public releaseAbstract},
	journal = {Ethics and Information Technology},
	volume = {25},
	year = {2023},
	month = {Jan-12-2023},
	abstract = {The new wave of {\textquoteleft}foundation models{\textquoteright}{\textemdash}general-purpose generative AI models, for production of text (e.g., ChatGPT) or images (e.g., MidJourney){\textemdash}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{\textquoteright}s design, and summarize a number of points where further input from policymakers and researchers would be required.},
	issn = {1388-1957},
	doi = {10.1007/s10676-023-09728-4},
	url = {https://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/s10676-023-09728-4},
	author = {Knott, Alistair and Pedreschi, Dino and Chatila, Raja and Chakraborti, Tapabrata and Leavy, Susan and Baeza-Yates, Ricardo and Eyers, David and Trotman, Andrew and Teal, Paul D. and Biecek, Przemyslaw and Russell, Stuart and Bengio, Yoshua}
}
@article {1532,
	title = {Mobility Constraints in Segregation Models},
	journal = {Scientific Reports},
	volume = {13},
	year = {2023},
	pages = {12087},
	abstract = {Since the development of the original Schelling model of urban segregation, several enhancements have been proposed, but none have considered the impact of mobility constraints on model dynamics. Recent studies have shown that human mobility follows specific patterns, such as a preference for short distances and dense locations. This paper proposes a segregation model incorporating mobility constraints to make agents select their location based on distance and location relevance. Our findings indicate that the mobility-constrained model produces lower segregation levels but takes longer to converge than the original Schelling model. We identified a few persistently unhappy agents from the minority group who cause this prolonged convergence time and lower segregation level as they move around the grid centre. Our study presents a more realistic representation of how agents move in urban areas and provides a novel and insightful approach to analyzing the impact of mobility constraints on segregation models. We highlight the significance of incorporating mobility constraints when policymakers design interventions to address urban segregation.},
	doi = {10.1038/s41598-023-38519-6},
	author = {Daniele Gambetta and Giovanni Mauro and Luca Pappalardo}
}
@conference {1518,
	title = {A Model-Agnostic Heuristics for Selective Classification},
	booktitle = {Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023, 2023, Washington, DC, USA, February 7-14, 2023},
	year = {2023},
	publisher = {AAAI Press},
	organization = {AAAI Press},
	abstract = {Selective classification (also known as classification with reject option) conservatively extends a classifier with a selection function to determine whether or not a prediction should be accepted (i.e., trusted, used, deployed). This is a highly relevant issue in socially sensitive tasks, such as credit scoring. State-of-the-art approaches rely on Deep Neural Networks (DNNs) that train at the same time both the classifier and the selection function. These approaches are model-specific and computationally expensive. We propose a model-agnostic approach, as it can work with any base probabilistic binary classification algorithm, and it can be scalable to large tabular datasets if the base classifier is so. The proposed algorithm, called SCROSS, exploits a cross-fitting strategy and theoretical results for quantile estimation to build the selection function. Experiments on real-world data show that SCROSS improves over existing methods.},
	doi = {10.1609/aaai.v37i8.26133},
	url = {https://doi.org/10.1609/aaai.v37i8.26133},
	author = {Andrea Pugnana and Salvatore Ruggieri}
}
@conference {1519,
	title = {Topics in Selective Classification},
	booktitle = {Thirty-Seventh AAAI Conference on Artificial Intelligence, AAAI 2023},
	year = {2023},
	publisher = {AAAI Press},
	organization = {AAAI Press},
	abstract = {In recent decades, advancements in information technology allowed Artificial Intelligence (AI) systems to predict future outcomes with unprecedented success. This brought the widespread deployment of these methods in many fields, intending to support decision-making. A pressing question is how to make AI systems robust to common challenges in real-life scenarios and trustworthy. In my work, I plan to explore ways to enhance the trustworthiness of AI through the selective classification framework. In this setting, the AI system can refrain from predicting whenever it is not confident enough, allowing it to trade off coverage, i.e. the percentage of instances that receive a prediction, for performance.},
	doi = {10.1609/aaai.v37i13.26925},
	url = {https://doi.org/10.1609/aaai.v37i13.26925},
	author = {Andrea Pugnana}
}
@conference {1558,
	title = {Attribute-aware Community Events in Feature-rich Dynamic Networks},
	booktitle = {Complex Networks and Their Applications XI: Proceedings of The Eleventh International Conference on Complex Networks and Their Applications: COMPLEX NETWORKS 2022{\textemdash}Book of Abstracts},
	year = {2022},
	publisher = {Springer},
	organization = {Springer},
	author = {Failla, Andrea and Mazzoni, Federico and Citraro, Salvatore}
}
@conference {1557,
	title = {Attributed stream-hypernetwork analysis: homophilic behaviors in pairwise and group political discussions on reddit},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2022},
	publisher = {Springer},
	organization = {Springer},
	doi = {https://doi.org/10.1007/978-3-031-21127-0_13},
	author = {Failla, Andrea and Citraro, Salvatore and Rossetti, Giulio}
}
@conference {1493,
	title = {Connected Vehicle Simulation Framework for Parking Occupancy Prediction (Demo Paper)},
	booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},
	year = {2022},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {This paper demonstrates a simulation framework that collects data about connected vehicles{\textquoteright} locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications.},
	isbn = {9781450395298},
	doi = {10.1145/3557915.3560995},
	url = {https://doi.org/10.1145/3557915.3560995},
	author = {Resce, Pierpaolo and Vorwerk, Lukas and Han, Zhiwei and Cornacchia, Giuliano and Alamdari, Omid Isfahani and Mirco Nanni and Luca Pappalardo and Weimer, Daniel and Liu, Yuanting}
}
@article {1540,
	title = {Explaining Black Box with visual exploration of Latent Space},
	journal = {EuroVis{\textendash}Short Papers},
	year = {2022},
	abstract = {Autoencoders 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.},
	url = {https://diglib.eg.org/xmlui/bitstream/handle/10.2312/evs20221098/085-089.pdf?sequence=1},
	author = {Bodria, Francesco and Rinzivillo, Salvatore and Fadda, Daniele and Guidotti, Riccardo and Giannotti, Fosca and Pedreschi, Dino}
}
@conference {1567,
	title = {Explaining Siamese Networks in Few-Shot Learning for Audio Data},
	booktitle = {Discovery Science - 25th International Conference, DS 2022, Montpellier, France, October 10-12, 2022, Proceedings},
	year = {2022},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Machine learning models are not able to generalize correctly when queried on samples belonging to class distributions that were never seen during training. This is a critical issue, since real world applications might need to quickly adapt without the necessity of re-training. To overcome these limitations, few-shot learning frameworks have been proposed and their applicability has been studied widely for computer vision tasks. Siamese Networks learn pairs similarity in form of a metric that can be easily extended on new unseen classes. Unfortunately, the downside of such systems is the lack of explainability. We propose a method to explain the outcomes of Siamese Networks in the context of few-shot learning for audio data. This objective is pursued through a local perturbation-based approach that evaluates segments-weighted-average contributions to the final outcome considering the interplay between different areas of the audio spectrogram. Qualitative and quantitative results demonstrate that our method is able to show common intra-class characteristics and erroneous reliance on silent sections.},
	doi = {10.1007/978-3-031-18840-4_36},
	url = {https://doi.org/10.1007/978-3-031-18840-4_36},
	author = {Andrea Fedele and Riccardo Guidotti and Dino Pedreschi}
}
@conference {1485,
	title = {From Mean-Field to Complex Topologies: Network Effects on the Algorithmic Bias Model},
	booktitle = {Complex Networks \& Their Applications X},
	year = {2022},
	author = {Valentina Pansanella and Giulio Rossetti and Letizia Milli}
}
@article {1531,
	title = {Generating mobility networks with generative adversarial networks},
	journal = {EPJ data science},
	volume = {11},
	number = {1},
	year = {2022},
	pages = {58},
	abstract = {The increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city{\textquoteright}s entire mobility network, a weighted directed graph in which nodes are geographic locations and weighted edges represent people{\textquoteright}s movements between those locations, thus describing the entire mobility set flows within a city. Our solution is MoGAN, a model based on Generative Adversarial Networks (GANs) to generate realistic mobility networks. We conduct extensive experiments on public datasets of bike and taxi rides to show that MoGAN outperforms the classical Gravity and Radiation models regarding the realism of the generated networks. Our model can be used for data augmentation and performing simulations and what-if analysis.},
	doi = {https://doi.org/10.1140/epjds/s13688-022-00372-4},
	author = {Giovanni Mauro and Luca, Massimiliano and Longa, Antonio and Lepri, Bruno and Luca Pappalardo}
}
@proceedings {1509,
	title = {GET-Viz: a library for automatic generation of visual dashboard for geographical time series},
	year = {2022},
	address = {Chicago, USA},
	author = {Fadda, Daniele and Michela Natilli and S Rinzivillo}
}
@conference {1492,
	title = {How Routing Strategies Impact Urban Emissions},
	booktitle = {Proceedings of the 30th International Conference on Advances in Geographic Information Systems},
	year = {2022},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	address = {New York, NY, USA},
	abstract = {Navigation apps use routing algorithms to suggest the best path to reach a user{\textquoteright}s desired destination. Although undoubtedly useful, navigation apps{\textquoteright} 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{\textquoteright}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{\textquoteright} 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.},
	isbn = {9781450395298},
	doi = {10.1145/3557915.3560977},
	url = {https://doi.org/10.1145/3557915.3560977},
	author = {Cornacchia, Giuliano and B{\"o}hm, Matteo and Giovanni Mauro and Mirco Nanni and Dino Pedreschi and Luca Pappalardo}
}
@article {1488,
	title = {The long-tail effect of the COVID-19 lockdown on Italians{\textquoteright} quality of life, sleep and physical activity},
	journal = {Scientific Data},
	volume = {9},
	number = {1},
	year = {2022},
	pages = {1{\textendash}10},
	abstract = {From March 2020 to May 2021, several lockdown periods caused by the COVID-19 pandemic have limited people{\textquoteright}s usual activities and mobility in Italy, as well as around the world. These unprecedented confinement measures dramatically modified citizens{\textquoteright} daily lifestyles and behaviours. However, with the advent of summer 2021 and thanks to the vaccination campaign that significantly prevents serious illness and death, and reduces the risk of contagion, all the Italian regions finally returned to regular behaviours and routines. Anyhow, it is unclear if there is a long-tail effect on people{\textquoteright}s quality of life, sleep- and physical activity-related behaviours. Thanks to the dataset described in this paper, it will be possible to obtain accurate insights of the changes induced by the lockdown period in the Italians{\textquoteright} health that will permit to provide practical suggestions at local, regional, and state institutions and companies to improve infrastructures and services that could be beneficial to Italians{\textquoteright} well being.},
	url = {https://www.nature.com/articles/s41597-022-01376-5},
	author = {Michela Natilli and Alessio Rossi and Trecroci, Athos and Cavaggioni, Luca and Merati, Giampiero and Formenti, Damiano}
}
@article {1489,
	title = {Methods and tools for causal discovery and causal inference},
	journal = {WIREs Data Mining Knowl. Discov.},
	volume = {12},
	number = {2},
	year = {2022},
	abstract = {Causality 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.},
	author = {Ana Rita Nogueira and Andrea Pugnana and Salvatore Ruggieri and Dino Pedreschi and Jo{\~a}o Gama}
}
@conference {1552,
	title = {Monitoring Fairness in HOLDA},
	booktitle = {HHAI 2022: Augmenting Human Intellect - Proceedings of the First International Conference on Hybrid Human-Artificial Intelligence, Amsterdam, The Netherlands, 13-17 June 2022},
	year = {2022},
	publisher = {IOS Press},
	organization = {IOS Press},
	doi = {10.3233/FAIA220205},
	url = {https://doi.org/10.3233/FAIA220205},
	author = {Michele Fontana and Francesca Naretto and Anna Monreale and Fosca Giannotti},
	editor = {Stefan Schlobach and Mar{\'\i}a P{\'e}rez-Ortiz and Myrthe Tielman}
}
@proceedings {1498,
	title = {Semantic Enrichment of XAI Explanations for Healthcare},
	year = {2022},
	abstract = {Explaining black-box models decisions is crucial to increase doctors{\textquoteright} 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{\textquoteright}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.},
	author = {Corbucci, Luca and Anna Monreale and Cecilia Panigutti and Michela Natilli and Smiraglio, Simona and Dino Pedreschi}
}
@proceedings {1500,
	title = {SoBigData++: European Integrated Infrastructure for Social Mining and Big Data Analytics.},
	year = {2022},
	address = {Tirrenia, Pisa},
	abstract = {SoBigData RI has the ambition to support the rising demand for cross-disciplinary research and innovation on the multiple aspects of social complexity from combined data and model-driven perspectives and the increasing importance of ethics and data scientists{\textquoteright} responsibility as pillars of trustworthy use of Big Data and analytical technology. Digital traces of human activities offer a considerable opportunity to scrutinize the ground truth of individual and collective behaviour at an unprecedented detail and on a global scale. This increasing wealth of data is a chance to understand social complexity, provided we can rely on social mining, i.e., adequate means for accessing big social data and models for extracting knowledge from them. SoBigData RI, with its tools and services, empowers researchers and innovators through a platform for the design and execution of large-scale social mining experiments, open to users with diverse backgrounds, accessible on the cloud (aligned with EOSC), and also exploiting supercomputing facilities. Pushing the FAIR (Findable, Accessible, Interoperable) and FACT (Fair, Accountable, Confidential, and Transparent) principles will render social mining experiments more efficiently designed, adjusted, and repeatable by domain experts that are not data scientists. SoBigData RI moves forward from the simple awareness of ethical and legal challenges in social mining to the development of concrete tools that operationalize ethics with value-sensitive design, incorporating values and norms for privacy protection, fairness, transparency, and pluralism. SoBigData RI is the result of two H2020 grants (g.a. n.654024 and 871042), and it is part of the ESFRI 2021 Roadmap.},
	author = {Trasarti, Roberto and Grossi, Valerio and Michela Natilli and Rapisarda, Beatrice}
}
@article {1545,
	title = {Stable and actionable explanations of black-box models through factual and counterfactual rules},
	journal = {Data Mining and Knowledge Discovery},
	year = {2022},
	author = {Guidotti, Riccardo and Monreale, Anna and Ruggieri, Salvatore and Naretto, Francesca and Turini, Franco and Pedreschi, Dino and Giannotti, Fosca}
}
@article {1476,
	title = {Benchmarking and Survey of Explanation Methods for Black Box Models},
	journal = {CoRR},
	volume = {abs/2102.13076},
	year = {2021},
	url = {https://arxiv.org/abs/2102.13076},
	author = {Francesco Bodria and Fosca Giannotti and Riccardo Guidotti and Francesca Naretto and Dino Pedreschi and S Rinzivillo}
}
@article {1484,
	title = {Cognitive network science quantifies feelings expressed in suicide letters and Reddit mental health communities},
	journal = {arXiv preprint arXiv:2110.15269},
	year = {2021},
	author = {Joseph, Simmi Marina and Salvatore Citraro and Morini, Virginia and Giulio Rossetti and Stella, Massimo}
}
@article {1429,
	title = {Conformity: a Path-Aware Homophily measure for Node-Attributed Networks},
	journal = {IEEE Intelligent SystemsIEEE Intelligent Systems},
	year = {2021},
	month = {2021},
	pages = {1 - 1},
	abstract = {Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Differently from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.},
	isbn = {1941-1294},
	doi = {https://doi.org/10.1109/MIS.2021.3051291},
	url = {https://ieeexplore.ieee.org/document/9321348},
	author = {Giulio Rossetti and Salvatore Citraro and Letizia Milli}
}
@article {1402,
	title = {Estimating the Total Volume of Queries to a Search Engine},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	year = {2021},
	pages = {1-1},
	abstract = {We study the problem of estimating the total number of searches (volume) of queries in a specific domain, which were submitted to a search engine in a given time period. Our statistical model assumes that the distribution of searches follows a Zipf{\textquoteright}s law, and that the observed sample volumes are biased accordingly to three possible scenarios. These assumptions are consistent with empirical data, with keyword research practices, and with approximate algorithms used to take counts of query frequencies. A few estimators of the parameters of the distribution are devised and experimented, based on the nature of the empirical/simulated data. We apply the methods on the domain of recipes and cooking queries searched in Italian in 2017. The observed volumes of sample queries are collected from Google Trends (continuous data) and SearchVolume (binned data). The estimated total number of queries and total volume are computed for the two cases, and the results are compared and discussed.},
	doi = {10.1109/TKDE.2021.3054668},
	url = {https://ieeexplore.ieee.org/abstract/document/9336245},
	author = {F. Lillo and Salvatore Ruggieri}
}
@conference {1551,
	title = {Explainable for Trustworthy AI},
	booktitle = {Human-Centered Artificial Intelligence - Advanced Lectures, 18th European Advanced Course on AI, ACAI 2021, Berlin, Germany, October 11-15, 2021, extended and improved lecture notes},
	year = {2021},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-031-24349-3_10},
	url = {https://doi.org/10.1007/978-3-031-24349-3_10},
	author = {Fosca Giannotti and Francesca Naretto and Francesco Bodria}
}
@article {1470,
	title = {Explaining the difference between men{\textquoteright}s and women{\textquoteright}s football},
	journal = {PLOS ONE},
	volume = {16},
	year = {2021},
	month = {Apr-08-2021},
	pages = {e0255407},
	abstract = {Women{\textquoteright}s football is gaining supporters and practitioners worldwide, raising questions about what the differences are with men{\textquoteright}s football. While the two sports are often compared based on the players{\textquoteright} physical attributes, we analyze the spatio-temporal events during matches in the last World Cups to compare male and female teams based on their technical performance. We train an artificial intelligence model to recognize if a team is male or female based on variables that describe a match{\textquoteright}s playing intensity, accuracy, and performance quality. Our model accurately distinguishes between men{\textquoteright}s and women{\textquoteright}s football, revealing crucial technical differences, which we investigate through the extraction of explanations from the classifier{\textquoteright}s decisions. The differences between men{\textquoteright}s and women{\textquoteright}s football are rooted in play accuracy, the recovery time of ball possession, and the players{\textquoteright} performance quality. Our methodology may help journalists and fans understand what makes women{\textquoteright}s football a distinct sport and coaches design tactics tailored to female teams.},
	doi = {https://doi.org/10.1371/journal.pone.0255407},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0255407},
	author = {Luca Pappalardo and Alessio Rossi and Michela Natilli and Paolo Cintia},
	editor = {Constantinou, Anthony C.}
}
@article {1400,
	title = {Give more data, awareness and control to individual citizens, and they will help COVID-19 containment},
	year = {2021},
	month = {2021/02/02},
	abstract = {The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the {\textquotedblleft}phase 2{\textquotedblright} 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{\textquoteright} 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{\textquoteright} {\textquotedblleft}personal data stores{\textquotedblright}, 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{\textemdash}if and when they want and for specific aims{\textemdash}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.},
	isbn = {1572-8439},
	doi = {https://doi.org/10.1007/s10676-020-09572-w},
	url = {https://link.springer.com/article/10.1007/s10676-020-09572-w},
	author = {Mirco Nanni and Andrienko, Gennady and Barabasi, Albert-Laszlo and Boldrini, Chiara and Bonchi, Francesco and Cattuto, Ciro and Chiaromonte, Francesca and Comand{\'e}, Giovanni and Conti, Marco and Cot{\'e}, Mark and Dignum, Frank and Dignum, Virginia and Domingo-Ferrer, Josep and Ferragina, Paolo and Fosca Giannotti and Riccardo Guidotti and Helbing, Dirk and Kaski, Kimmo and Kert{\'e}sz, J{\'a}nos and Lehmann, Sune and Lepri, Bruno and Lukowicz, Paul and Matwin, Stan and Jim{\'e}nez, David Meg{\'\i}as and Anna Monreale and Morik, Katharina and Oliver, Nuria and Passarella, Andrea and Passerini, Andrea and Dino Pedreschi and Pentland, Alex and Pianesi, Fabio and Francesca Pratesi and S Rinzivillo and Salvatore Ruggieri and Siebes, Arno and Torra, Vicenc and Roberto Trasarti and Hoven, Jeroen van den and Vespignani, Alessandro}
}
@article {1401,
	title = {GLocalX - From Local to Global Explanations of Black Box AI Models},
	volume = {294},
	year = {2021},
	month = {2021/05/01/},
	pages = {103457},
	abstract = {Artificial 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 {\textquotedblleft}black boxes{\textquotedblright} 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 {\textquotedblleft}local{\textquotedblright} explanations. We present GLocalX, a {\textquotedblleft}local-first{\textquotedblright} 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.},
	isbn = {0004-3702},
	doi = {https://doi.org/10.1016/j.artint.2021.103457},
	url = {https://www.sciencedirect.com/science/article/pii/S0004370221000084},
	author = {Mattia Setzu and Riccardo Guidotti and Anna Monreale and Franco Turini and Dino Pedreschi and Fosca Giannotti}
}
@article {1435,
	title = {Introduction to the special issue on social mining and big data ecosystem for open, responsible data science},
	year = {2021},
	month = {2021/03/05},
	isbn = {2364-4168},
	doi = {https://link.springer.com/article/10.1007/s41060-021-00253-5},
	url = {https://doi.org/10.1007/s41060-021-00253-5},
	author = {Luca Pappalardo and Grossi, Valerio and Dino Pedreschi}
}
@article {1463,
	title = {A Mechanistic Data-Driven Approach to Synthesize Human Mobility Considering the Spatial, Temporal, and Social Dimensions Together},
	journal = {ISPRS International Journal of Geo-Information},
	volume = {10},
	number = {9},
	year = {2021},
	pages = {599},
	abstract = {Modelling human mobility is crucial in several areas, from urban planning to epidemic modelling, traffic forecasting, and what-if analysis. Existing generative models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected. Those models that capture some social perspectives of human mobility utilize trivial and unrealistic spatial and temporal mechanisms. In this paper, we propose the Spatial, Temporal and Social Exploration and Preferential Return model (STS-EPR), which embeds mechanisms to capture the spatial, temporal, and social aspects together. We compare the trajectories produced by STS-EPR with respect to real-world trajectories and synthetic trajectories generated by two state-of-the-art generative models on a set of standard mobility measures. Our experiments conducted on an open dataset show that STS-EPR, overall, outperforms existing spatial-temporal or social models demonstrating the importance of modelling adequately the sociality to capture precisely all the other dimensions of human mobility. We further investigate the impact of the tile shape of the spatial tessellation on the performance of our model. STS-EPR, which is open-source and tested on open data, represents a step towards the design of a mechanistic data-driven model that captures all the aspects of human mobility comprehensively.},
	issn = {2220-9964},
	doi = {10.3390/ijgi10090599},
	url = {https://www.mdpi.com/2220-9964/10/9/599},
	author = {Cornacchia, Giuliano and Luca Pappalardo}
}
@conference {1549,
	title = {A new approach for cross-silo federated learning and its privacy risks},
	booktitle = {2021 18th International Conference on Privacy, Security and Trust (PST)},
	year = {2021},
	doi = {10.1109/PST52912.2021.9647753},
	author = {Fontana, Michele and Naretto, Francesca and Monreale, Anna}
}
@conference {1478,
	title = {A new approach for cross-silo federated learning and its privacy risks},
	booktitle = {18th International Conference on Privacy, Security and Trust, PST 2021, Auckland, New Zealand, December 13-15, 2021},
	year = {2021},
	publisher = {IEEE},
	organization = {IEEE},
	doi = {10.1109/PST52912.2021.9647753},
	url = {https://doi.org/10.1109/PST52912.2021.9647753},
	author = {Michele Fontana and Francesca Naretto and Anna Monreale}
}
@conference {1477,
	title = {Privacy Risk Assessment of Individual Psychometric Profiles},
	booktitle = {Discovery Science - 24th International Conference, DS 2021, Halifax, NS, Canada, October 11-13, 2021, Proceedings},
	year = {2021},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-030-88942-5_32},
	url = {https://doi.org/10.1007/978-3-030-88942-5_32},
	author = {Giacomo Mariani and Anna Monreale and Francesca Naretto},
	editor = {Carlos Soares and Lu{\'\i}s Torgo}
}
@article {1449,
	title = {STS-EPR: Modelling individual mobility considering the spatial, temporal, and social dimensions together},
	year = {2021},
	month = {05},
	doi = {10.1016/j.procs.2021.03.035},
	author = {Cornacchia, Giuliano and Luca Pappalardo}
}
@article {1483,
	title = {Toward a Standard Approach for Echo Chamber Detection: Reddit Case Study},
	journal = {Applied Sciences},
	volume = {11},
	number = {12},
	year = {2021},
	pages = {5390},
	author = {Morini, Virginia and Pollacci, Laura and Giulio Rossetti}
}
@article {1501,
	title = {Understanding eating choices among university students: A study using data from cafeteria cashiers{\textquoteright} transactions},
	journal = {Health Policy},
	volume = {125},
	number = {5},
	year = {2021},
	pages = {665{\textendash}673},
	author = {Lorenzoni, Valentina and Triulzi, Isotta and Martinucci, Irene and Toncelli, Letizia and Michela Natilli and Barale, Roberto and Turchetti, Giuseppe}
}
@conference {1408,
	title = {Analysis and Visualization of Performance Indicators in University Admission Tests},
	booktitle = {Formal Methods. FM 2019 International Workshops},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {This 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.},
	isbn = {978-3-030-54994-7},
	doi = {https://doi.org/10.1007/978-3-030-54994-7_14},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-54994-7_14},
	author = {Michela Natilli and Daniele Fadda and S Rinzivillo and Dino Pedreschi and Licari, Federica},
	editor = {Sekerinski, Emil and Moreira, Nelma and Oliveira, Jos{\'e} N. and Ratiu, Daniel and Riccardo Guidotti and Farrell, Marie and Luckcuck, Matt and Marmsoler, Diego and Campos, Jos{\'e} and Astarte, Troy and Gonnord, Laure and Cerone, Antonio and Couto, Luis and Dongol, Brijesh and Kutrib, Martin and Monteiro, Pedro and Delmas, David}
}
@article {1370,
	title = {ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks},
	journal = {Applied Network Science},
	volume = {5},
	number = {1},
	year = {2020},
	pages = {1{\textendash}23},
	abstract = {Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.},
	doi = {https://doi.org/10.1007/s41109-020-00270-6},
	url = {https://link.springer.com/article/10.1007/s41109-020-00270-6},
	author = {Giulio Rossetti}
}
@article {1336,
	title = {Artificial Intelligence (AI): new developments and innovations applied to e-commerce},
	year = {2020},
	month = {05/2020},
	institution = {European Parliament{\textquoteright}s committee on the Internal Market and Consumer Protection},
	abstract = {This 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).},
	url = {https://www.europarl.europa.eu/thinktank/en/document.html?reference=IPOL_IDA(2020)648791},
	author = {Dino Pedreschi and Ioanna Miliou}
}
@article {1344,
	title = {Authenticated Outlier Mining for Outsourced Databases},
	journal = {IEEE Transactions on Dependable and Secure Computing},
	volume = {17},
	year = {2020},
	month = {Jan-03-2020},
	pages = {222 - 235},
	abstract = {The 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.},
	issn = {1545-5971},
	doi = {10.1109/TDSC.885810.1109/TDSC.2017.2754493},
	url = {https://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.pdf},
	author = {Dong, Boxiang and Wang, Hui and Anna Monreale and Dino Pedreschi and Fosca Giannotti and Guo, Wenge}
}
@article {1428,
	title = {Bias in data-driven artificial intelligence systems{\textemdash}An introductory survey},
	journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
	volume = {10},
	number = {3},
	year = {2020},
	pages = {e1356},
	abstract = {Artificial Intelligence (AI)-based systems are widely employed nowadays to make decisions that have far-reaching impact on individuals and society. Their decisions might affect everyone, everywhere, and anytime, entailing concerns about potential human rights issues. Therefore, it is necessary to move beyond traditional AI algorithms optimized for predictive performance and embed ethical and legal principles in their design, training, and deployment to ensure social good while still benefiting from the huge potential of the AI technology. The goal of this survey is to provide a broad multidisciplinary overview of the area of bias in AI systems, focusing on technical challenges and solutions as well as to suggest new research directions towards approaches well-grounded in a legal frame. In this survey, we focus on data-driven AI, as a large part of AI is powered nowadays by (big) data and powerful machine learning algorithms. If otherwise not specified, we use the general term bias to describe problems related to the gathering or processing of data that might result in prejudiced decisions on the bases of demographic features such as race, sex, and so forth.},
	doi = {https://doi.org/10.1002/widm.1356},
	url = {https://onlinelibrary.wiley.com/doi/full/10.1002/widm.1356},
	author = {Ntoutsi, Eirini and Fafalios, Pavlos and Gadiraju, Ujwal and Iosifidis, Vasileios and Nejdl, Wolfgang and Vidal, Maria-Esther and Salvatore Ruggieri and Franco Turini and Papadopoulos, Symeon and Krasanakis, Emmanouil and others}
}
@conference {1406,
	title = {Black Box Explanation by Learning Image Exemplars in the Latent Feature Space},
	booktitle = {Machine Learning and Knowledge Discovery in Databases},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {We 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 {\textquotedblleft}morphing{\textquotedblright} 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.},
	isbn = {978-3-030-46150-8},
	doi = {https://doi.org/10.1007/978-3-030-46150-8_12},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-46150-8_12},
	author = {Riccardo Guidotti and Anna Monreale and Matwin, Stan and Dino Pedreschi},
	editor = {Brefeld, Ulf and Fromont, Elisa and Hotho, Andreas and Knobbe, Arno and Maathuis, Marloes and Robardet, C{\'e}line}
}
@conference {1371,
	title = {Capturing Political Polarization of Reddit Submissions in the Trump Era},
	booktitle = {SEBD},
	year = {2020},
	author = {Giulio Rossetti and Morini, Virginia and Pollacci, Laura}
}
@article {1423,
	title = {Causal inference for social discrimination reasoning},
	volume = {54},
	year = {2020},
	month = {2020/04/01},
	pages = {425 - 437},
	abstract = {The 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.},
	isbn = {1573-7675},
	doi = {https://doi.org/10.1007/s10844-019-00580-x},
	url = {https://link.springer.com/article/10.1007/s10844-019-00580-x},
	author = {Qureshi, Bilal and Kamiran, Faisal and Karim, Asim and Salvatore Ruggieri and Dino Pedreschi}
}
@article {1364,
	title = {Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks},
	journal = {arXiv preprint arXiv:2012.05195},
	year = {2020},
	author = {Giulio Rossetti and Salvatore Citraro and Letizia Milli}
}
@conference {1323,
	title = {Digital Footprints of International Migration on Twitter},
	booktitle = {International Symposium on Intelligent Data Analysis},
	year = {2020},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Studying migration using traditional data has some limitations. To date, there have been several studies proposing innovative methodologies to measure migration stocks and flows from social big data. Nevertheless, a uniform definition of a migrant is difficult to find as it varies from one work to another depending on the purpose of the study and nature of the dataset used. In this work, a generic methodology is developed to identify migrants within the Twitter population. This describes a migrant as a person who has the current residence different from the nationality. The residence is defined as the location where a user spends most of his/her time in a certain year. The nationality is inferred from linguistic and social connections to a migrant{\textquoteright}s country of origin. This methodology is validated first with an internal gold standard dataset and second with two official statistics, and shows strong performance scores and correlation coefficients. Our method has the advantage that it can identify both immigrants and emigrants, regardless of the origin/destination countries. The new methodology can be used to study various aspects of migration, including opinions, integration, attachment, stocks and flows, motivations for migration, etc. Here, we exemplify how trending topics across and throughout different migrant communities can be observed.},
	doi = {https://doi.org/10.1007/978-3-030-44584-3_22},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-44584-3_22},
	author = {Jisu Kim and Alina Sirbu and Fosca Giannotti and Lorenzo Gabrielli}
}
@conference {1285,
	title = {Doctor XAI: an ontology-based approach to black-box sequential data classification explanations},
	booktitle = {Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency},
	year = {2020},
	abstract = {Several 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.},
	doi = {10.1145/3351095.3372855},
	url = {https://dl.acm.org/doi/pdf/10.1145/3351095.3372855?download=true},
	author = {Cecilia Panigutti and Perotti, Alan and Dino Pedreschi}
}
@article {1403,
	title = {Error Estimation of Ultra-Short Heart Rate Variability Parameters: Effect of Missing Data Caused by Motion Artifacts},
	journal = {Sensors},
	volume = {20},
	number = {24},
	year = {2020},
	pages = {7122},
	abstract = {Application 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.},
	issn = {1424-8220},
	doi = {10.3390/s20247122},
	url = {https://www.mdpi.com/1424-8220/20/24/7122},
	author = {Alessio Rossi and Dino Pedreschi and Clifton, David A. and Morelli, Davide}
}
@conference {1420,
	title = {Estimating countries{\textquoteright} peace index through the lens of the world news as monitored by GDELT},
	booktitle = {2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)},
	year = {2020},
	month = {2020},
	abstract = {Peacefulness is a principal dimension of well-being, and its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital data streams have drastically changed research in this field. In the current study, we exploit information extracted from Global Data on Events, Location, and Tone (GDELT) digital news database, to capture peacefulness through the Global Peace Index (GPI). Applying machine learning techniques, we demonstrate that news media attention, sentiment, and social stability from GDELT can be used as proxies for measuring GPI at a monthly level. Additionally, through the variable importance analysis, we show that each country{\textquoteright}s socio-economic, political, and military profile emerges. This could bring added value to researchers interested in "Data Science for Social Good", to policy-makers, and peacekeeping organizations since they could monitor peacefulness almost real-time, and therefore facilitate timely and more efficient policy-making.
},
	doi = {https://doi.org/10.1109/DSAA49011.2020.00034},
	url = {https://ieeexplore.ieee.org/abstract/document/9260052},
	author = {V. Voukelatou and Luca Pappalardo and Lorenzo Gabrielli and Fosca Giannotti}
}
@article {1405,
	title = {An ethico-legal framework for social data science},
	year = {2020},
	month = {2020/03/31},
	abstract = {This 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.},
	isbn = {2364-4168},
	doi = {https://doi.org/10.1007/s41060-020-00211-7},
	url = {https://link.springer.com/article/10.1007/s41060-020-00211-7},
	author = {Forg{\'o}, Nikolaus and H{\"a}nold, Stefanie and van~den Hoven, Jeroen and Kr{\"u}gel, Tina and Lishchuk, Iryna and Mahieu, Ren{\'e} and Anna Monreale and Dino Pedreschi and Francesca Pratesi and van Putten, David}
}
@article {1369,
	title = {Evaluating community detection algorithms for progressively evolving graphs},
	journal = {arXiv preprint arXiv:2007.08635},
	year = {2020},
	author = {Cazabet, R{\'e}my and Boudebza, Souaad and Giulio Rossetti}
}
@conference {1480,
	title = {Explainability Methods for Natural Language Processing: Applications to Sentiment Analysis.},
	booktitle = {SEBD},
	year = {2020},
	author = {Francesco Bodria and Panisson, Andr{\'e} and Perotti, Alan and Piaggesi, Simone}
}
@conference {1412,
	title = {Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars},
	booktitle = {Discovery Science},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {We present xspells, a model-agnostic local approach for explaining the decisions of a black box model for sentiment classification of short texts. The explanations provided consist of a set of exemplar sentences and a set of counter-exemplar sentences. The former are examples classified by the black box with the same label as the text to explain. The latter are examples classified with a different label (a form of counter-factuals). Both are close in meaning to the text to explain, and both are meaningful sentences {\textendash} albeit they are synthetically generated. xspells generates neighbors of the text to explain in a latent space using Variational Autoencoders for encoding text and decoding latent instances. A decision tree is learned from randomly generated neighbors, and used to drive the selection of the exemplars and counter-exemplars. We report experiments on two datasets showing that xspells outperforms the well-known lime method in terms of quality of explanations, fidelity, and usefulness, and that is comparable to it in terms of stability.},
	isbn = {978-3-030-61527-7},
	doi = {https://doi.org/10.1007/978-3-030-61527-7_24},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-61527-7_24},
	author = {Lampridis, Orestis and Riccardo Guidotti and Salvatore Ruggieri},
	editor = {Appice, Annalisa and Tsoumakas, Grigorios and Manolopoulos, Yannis and Matwin, Stan}
}
@conference {1426,
	title = {Global Explanations with Local Scoring},
	booktitle = {Machine Learning and Knowledge Discovery in Databases},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Artificial Intelligence systems often adopt machine learning models encoding complex algorithms with potentially unknown behavior. As the application of these {\textquotedblleft}black box{\textquotedblright} models grows, it is our responsibility to understand their inner working and formulate them in human-understandable explanations. To this end, we propose a rule-based model-agnostic explanation method that follows a local-to-global schema: it generalizes a global explanation summarizing the decision logic of a black box starting from the local explanations of single predicted instances. We define a scoring system based on a rule relevance score to extract global explanations from a set of local explanations in the form of decision rules. Experiments on several datasets and black boxes show the stability, and low complexity of the global explanations provided by the proposed solution in comparison with baselines and state-of-the-art global explainers.},
	isbn = {978-3-030-43823-4},
	doi = {https://doi.org/10.1007/978-3-030-43823-4_14},
	url = {https://link.springer.com/chapter/10.1007\%2F978-3-030-43823-4_14},
	author = {Mattia Setzu and Riccardo Guidotti and Anna Monreale and Franco Turini},
	editor = {Cellier, Peggy and Driessens, Kurt}
}
@article {1404,
	title = {Human migration: the big data perspective},
	journal = {International Journal of Data Science and Analytics},
	year = {2020},
	month = {2020/03/23},
	pages = {1{\textendash}20},
	abstract = {How 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.},
	isbn = {2364-4168},
	doi = {https://doi.org/10.1007/s41060-020-00213-5},
	url = {https://link.springer.com/article/10.1007\%2Fs41060-020-00213-5},
	author = {Alina Sirbu and Andrienko, Gennady and Andrienko, Natalia and Boldrini, Chiara and Conti, Marco and Fosca Giannotti and Riccardo Guidotti and Bertoli, Simone and Jisu Kim and Muntean, Cristina Ioana and Luca Pappalardo and Passarella, Andrea and Dino Pedreschi and Pollacci, Laura and Francesca Pratesi and Sharma, Rajesh}
}
@article {1358,
	title = {Identifying and exploiting homogeneous communities in labeled networks},
	journal = {Applied Network Science},
	volume = {5},
	number = {1},
	year = {2020},
	pages = {1{\textendash}20},
	abstract = {Attribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a challenging task presenting EVA, an algorithmic approach designed to maximize a quality function tailoring both structural and homophilic clustering criteria. We evaluate EVA on several real-world labeled networks carrying both nominal and ordinal information, and we compare our approach to other classic and attribute-aware algorithms. Our results suggest that EVA is the only method, among the compared ones, able to discover homogeneous clusters without considerably degrading partition modularity.We also investigate two well-defined applicative scenarios to characterize better EVA: i) the clustering of a mental lexicon, i.e., a linguistic network modeling human semantic memory, and (ii) the node label prediction task, namely the problem of inferring the missing label of a node.},
	doi = {https://doi.org/10.1007/s41109-020-00302-1},
	url = {https://appliednetsci.springeropen.com/articles/10.1007/s41109-020-00302-1},
	author = {Salvatore Citraro and Giulio Rossetti}
}
@conference {1409,
	title = {{\textquotedblleft}Know Thyself{\textquotedblright} How Personal Music Tastes Shape the Last.Fm Online Social Network},
	booktitle = {Formal Methods. FM 2019 International Workshops},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {As Nietzsche once wrote {\textquotedblleft}Without music, life would be a mistake{\textquotedblright} (Twilight of the Idols, 1889.). The music we listen to reflects our personality, our way to approach life. In order to enforce self-awareness, we devised a Personal Listening Data Model that allows for capturing individual music preferences and patterns of music consumption. We applied our model to 30k users of Last.Fm for which we collected both friendship ties and multiple listening. Starting from such rich data we performed an analysis whose final aim was twofold: (i) capture, and characterize, the individual dimension of music consumption in order to identify clusters of like-minded Last.Fm users; (ii) analyze if, and how, such clusters relate to the social structure expressed by the users in the service. Do there exist individuals having similar Personal Listening Data Models? If so, are they directly connected in the social graph or belong to the same community?.},
	isbn = {978-3-030-54994-7},
	doi = {https://doi.org/10.1007/978-3-030-54994-7_11},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-54994-7_11},
	author = {Riccardo Guidotti and Giulio Rossetti},
	editor = {Sekerinski, Emil and Moreira, Nelma and Oliveira, Jos{\'e} N. and Ratiu, Daniel and Riccardo Guidotti and Farrell, Marie and Luckcuck, Matt and Marmsoler, Diego and Campos, Jos{\'e} and Astarte, Troy and Gonnord, Laure and Cerone, Antonio and Couto, Luis and Dongol, Brijesh and Kutrib, Martin and Monteiro, Pedro and Delmas, David}
}
@booklet {1425,
	title = {Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown},
	year = {2020},
	abstract = {Understanding 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?},
	doi = {https://dx.doi.org/10.32079/ISTI-TR-2020/005},
	url = {https://arxiv.org/abs/2004.11278},
	author = {Pietro Bonato and Paolo Cintia and Francesco Fabbri and Daniele Fadda and Fosca Giannotti and Pier Luigi Lopalco and Sara Mazzilli and Mirco Nanni and Luca Pappalardo and Dino Pedreschi and Francesco Penone and S Rinzivillo and Giulio Rossetti and Marcello Savarese and Lara Tavoschi}
}
@article {1410,
	title = {Modeling Adversarial Behavior Against Mobility Data Privacy},
	journal = {IEEE Transactions on Intelligent Transportation SystemsIEEE Transactions on Intelligent Transportation Systems},
	year = {2020},
	month = {2020},
	pages = {1 - 14},
	abstract = {Privacy risk assessment is a crucial issue in any privacy-aware analysis process. Traditional frameworks for privacy risk assessment systematically generate the assumed knowledge for a potential adversary, evaluating the risk without realistically modelling the collection of the background knowledge used by the adversary when performing the attack. In this work, we propose Simulated Privacy Annealing (SPA), a new adversarial behavior model for privacy risk assessment in mobility data. We model the behavior of an adversary as a mobility trajectory and introduce an optimization approach to find the most effective adversary trajectory in terms of privacy risk produced for the individuals represented in a mobility data set. We use simulated annealing to optimize the movement of the adversary and simulate a possible attack on mobility data. We finally test the effectiveness of our approach on real human mobility data, showing that it can simulate the knowledge gathering process for an adversary in a more realistic way.},
	isbn = {1558-0016},
	doi = {https://doi.org/10.1109/TITS.2020.3021911},
	url = {https://ieeexplore.ieee.org/abstract/document/9199893},
	author = {Roberto Pellungrini and Luca Pappalardo and F. Simini and Anna Monreale}
}
@article {1368,
	title = {Modelling Human Mobility considering Spatial, Temporal and Social Dimensions},
	journal = {arXiv preprint arXiv:2007.02371},
	year = {2020},
	author = {Cornacchia, Giuliano and Giulio Rossetti and Luca Pappalardo}
}
@conference {1365,
	title = {Opinion Dynamic Modeling of Fake News Perception},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2020},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Fake news diffusion represents one of the most pressing issues of our online society. In recent years, fake news has been analyzed from several points of view, primarily to improve our ability to separate them from the legit ones as well as identify their sources. Among such vast literature, a rarely discussed theme is likely to play uttermost importance in our understanding of such a controversial phenomenon: the analysis of fake news{\textquoteright} perception. In this work, we approach such a problem by proposing a family of opinion dynamic models tailored to study how specific social interaction patterns concur to the acceptance, or refusal, of fake news by a population of interacting individuals. To discuss the peculiarities of the proposed models, we tested them on several synthetic network topologies, thus underlying when/how they affect the stable states reached by the performed simulations.},
	doi = {https://doi.org/10.1007/978-3-030-65347-7_31},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-65347-7_31},
	author = {Toccaceli, Cecilia and Letizia Milli and Giulio Rossetti}
}
@conference {1411,
	title = {Predicting and Explaining Privacy Risk Exposure in Mobility Data},
	booktitle = {Discovery Science},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Mobility data is a proxy of different social dynamics and its analysis enables a wide range of user services. Unfortunately, mobility data are very sensitive because the sharing of people{\textquoteright}s whereabouts may arise serious privacy concerns. Existing frameworks for privacy risk assessment provide tools to identify and measure privacy risks, but they often (i) have high computational complexity; and (ii) are not able to provide users with a justification of the reported risks. In this paper, we propose expert, a new framework for the prediction and explanation of privacy risk on mobility data. We empirically evaluate privacy risk on real data, simulating a privacy attack with a state-of-the-art privacy risk assessment framework. We then extract individual mobility profiles from the data for predicting their risk. We compare the performance of several machine learning algorithms in order to identify the best approach for our task. Finally, we show how it is possible to explain privacy risk prediction on real data, using two algorithms: Shap, a feature importance-based method and Lore, a rule-based method. Overall, expert is able to provide a user with the privacy risk and an explanation of the risk itself. The experiments show excellent performance for the prediction task.},
	isbn = {978-3-030-61527-7},
	doi = {https://doi.org/10.1007/978-3-030-61527-7_27},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-61527-7_27},
	author = {Francesca Naretto and Roberto Pellungrini and Anna Monreale and Nardini, Franco Maria and Musolesi, Mirco},
	editor = {Appice, Annalisa and Tsoumakas, Grigorios and Manolopoulos, Yannis and Matwin, Stan}
}
@conference {1430,
	title = {Prediction and Explanation of Privacy Risk on Mobility Data with Neural Networks},
	booktitle = {ECML PKDD 2020 Workshops},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {The analysis of privacy risk for mobility data is a fundamental part of any privacy-aware process based on such data. Mobility data are highly sensitive. Therefore, the correct identification of the privacy risk before releasing the data to the public is of utmost importance. However, existing privacy risk assessment frameworks have high computational complexity. To tackle these issues, some recent work proposed a solution based on classification approaches to predict privacy risk using mobility features extracted from the data. In this paper, we propose an improvement of this approach by applying long short-term memory (LSTM) neural networks to predict the privacy risk directly from original mobility data. We empirically evaluate privacy risk on real data by applying our LSTM-based approach. Results show that our proposed method based on a LSTM network is effective in predicting the privacy risk with results in terms of F1 of up~to 0.91. Moreover, to explain the predictions of our model, we employ a state-of-the-art explanation algorithm, Shap. We explore the resulting explanation, showing how it is possible to provide effective predictions while explaining them to the end-user.},
	isbn = {978-3-030-65965-3},
	doi = {https://doi.org/10.1007/978-3-030-65965-3_34},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-65965-3_34},
	author = {Francesca Naretto and Roberto Pellungrini and Nardini, Franco Maria and Fosca Giannotti},
	editor = {Koprinska, Irena and Kamp, Michael and Appice, Annalisa and Loglisci, Corrado and Antonie, Luiza and Zimmermann, Albrecht and Riccardo Guidotti and {\"O}zg{\"o}bek, {\"O}zlem and Ribeiro, Rita P. and Gavald{\`a}, Ricard and Gama, Jo{\~a}o and Adilova, Linara and Krishnamurthy, Yamuna and Ferreira, Pedro M. and Malerba, Donato and Medeiros, Ib{\'e}ria and Ceci, Michelangelo and Manco, Giuseppe and Masciari, Elio and Ras, Zbigniew W. and Christen, Peter and Ntoutsi, Eirini and Schubert, Erich and Zimek, Arthur and Anna Monreale and Biecek, Przemyslaw and S Rinzivillo and Kille, Benjamin and Lommatzsch, Andreas and Gulla, Jon Atle}
}
@article {1421,
	title = {PRIMULE: Privacy risk mitigation for user profiles},
	volume = {125},
	year = {2020},
	month = {2020/01/01/},
	pages = {101786},
	abstract = {The availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the inference of personal and private information. In this paper, we address the privacy issues related to the sharing of user profiles, derived from mobile phone data, by proposing PRIMULE, a privacy risk mitigation strategy. Such a method relies on PRUDEnce (Pratesi et~al., 2018), a privacy risk assessment framework that provides a methodology for systematically identifying risky-users in a set of data. An extensive experimentation on real-world data shows the effectiveness of PRIMULE strategy in terms of both quality of mobile user profiles and utility of these profiles for analytical services such as the Sociometer (Furletti et~al., 2013), a data mining tool for city users classification.},
	isbn = {0169-023X},
	doi = {https://doi.org/10.1016/j.datak.2019.101786},
	url = {https://www.sciencedirect.com/science/article/pii/S0169023X18305342},
	author = {Francesca Pratesi and Lorenzo Gabrielli and Paolo Cintia and Anna Monreale and Fosca Giannotti}
}
@article {1339,
	title = {The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy},
	journal = {arXiv preprint arXiv:2006.03141},
	year = {2020},
	abstract = {We 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{\textquoteright}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.},
	url = {https://arxiv.org/abs/2006.03141},
	author = {Paolo Cintia and Daniele Fadda and Fosca Giannotti and Luca Pappalardo and Giulio Rossetti and Dino Pedreschi and S Rinzivillo and Bonato, Pietro and Fabbri, Francesco and Penone, Francesco and Savarese, Marcello and Checchi, Daniele and Chiaromonte, Francesca and Vineis , Paolo and Guzzetta, Giorgio and Riccardo, Flavia and Marziano, Valentina and Poletti, Piero and Trentini, Filippo and Bella, Antonio and Andrianou, Xanthi and Del Manso, Martina and Fabiani, Massimo and Bellino, Stefania and Boros, Stefano and Mateo Urdiales, Alberto and Vescio, Maria Fenicia and Brusaferro, Silvio and Rezza, Giovanni and Pezzotti, Patrizio and Ajelli, Marco and Merler, Stefano}
}
@article {1302,
	title = {(So) Big Data and the transformation of the city},
	journal = {International Journal of Data Science and Analytics},
	year = {2020},
	abstract = {The exponential increase in the availability of large-scale mobility data has fueled the vision of smart cities that will transform our lives. The truth is that we have just scratched the surface of the research challenges that should be tackled in order to make this vision a reality. Consequently, there is an increasing interest among different research communities (ranging from civil engineering to computer science) and industrial stakeholders in building knowledge discovery pipelines over such data sources. At the same time, this widespread data availability also raises privacy issues that must be considered by both industrial and academic stakeholders. In this paper, we provide a wide perspective on the role that big data have in reshaping cities. The paper covers the main aspects of urban data analytics, focusing on privacy issues, algorithms, applications and services, and georeferenced data from social media. In discussing these aspects, we leverage, as concrete examples and case studies of urban data science tools, the results obtained in the {\textquotedblleft}City of Citizens{\textquotedblright} thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality.},
	doi = {https://doi.org/10.1007/s41060-020-00207-3},
	url = {https://link.springer.com/article/10.1007/s41060-020-00207-3},
	author = {Andrienko, Gennady and Andrienko, Natalia and Boldrini, Chiara and Caldarelli, Guido and Paolo Cintia and Cresci, Stefano and Facchini, Angelo and Fosca Giannotti and Gionis, Aristides and Riccardo Guidotti and others}
}
@article {1367,
	title = {UTLDR: an agent-based framework for modeling infectious diseases and public interventions},
	journal = {arXiv preprint arXiv:2011.05606},
	year = {2020},
	author = {Giulio Rossetti and Letizia Milli and Salvatore Citraro and Morini, Virginia}
}
@article {1260,
	title = {The AI black box Explanation Problem},
	journal = {ERCIM NEWS},
	number = {116},
	year = {2019},
	pages = {12{\textendash}13},
	author = {Riccardo Guidotti and Anna Monreale and Dino Pedreschi}
}
@article {1216,
	title = {Algorithmic bias amplifies opinion fragmentation and polarization: A bounded confidence model},
	journal = {PloS one},
	volume = {14},
	number = {3},
	year = {2019},
	pages = {e0213246},
	abstract = {The 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.},
	doi = {10.1371/journal.pone.0213246},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0213246},
	author = {Alina Sirbu and Dino Pedreschi and Fosca Giannotti and Kert{\'e}sz, J{\'a}nos}
}
@article {1245,
	title = {Analysis of the Impact of Interpolation Methods of Missing RR-intervals Caused by Motion Artifacts on HRV Features Estimations},
	journal = {Sensors},
	volume = {19},
	number = {14},
	year = {2019},
	pages = {3163},
	abstract = {Wearable physiological monitors have become increasingly popular, often worn during people{\textquoteright}s daily life, collecting data 24 hours a day, 7 days a week. In the last decade, these devices have attracted the attention of the scientific community as they allow us to automatically extract information about user physiology (e.g., heart rate, sleep quality and physical activity) enabling inference on their health. However, the biggest issue about the data recorded by wearable devices is the missing values due to motion and mechanical artifacts induced by external stimuli during data acquisition. This missing data could negatively affect the assessment of heart rate (HR) response and estimation of heart rate variability (HRV), that could in turn provide misleading insights concerning the health status of the individual. In this study, we focus on healthy subjects with normal heart activity and investigate the effects of missing variation of the timing between beats (RR-intervals) caused by motion artifacts on HRV features estimation by randomly introducing missing values within a five min time windows of RR-intervals obtained from the nsr2db PhysioNet dataset by using Gilbert burst method. We then evaluate several strategies for estimating HRV in the presence of missing values by interpolating periods of missing values, covering the range of techniques often deployed in the literature, via linear, quadratic, cubic, and cubic spline functions. We thereby compare the HRV features obtained by handling missing data in RR-interval time series against HRV features obtained from the same data without missing values. Finally, we assess the difference between the use of interpolation methods on time (i.e., the timestamp when the heartbeats happen) and on duration (i.e., the duration of the heartbeats), in order to identify the best methodology to handle the missing RR-intervals. The main novel finding of this study is that the interpolation of missing data on time produces more reliable HRV estimations when compared to interpolation on duration. Hence, we can conclude that interpolation on duration modifies the power spectrum of the RR signal, negatively affecting the estimation of the HRV features as the amount of missing values increases. We can conclude that interpolation in time is the optimal method among those considered for handling data with large amounts of missing values, such as data from wearable sensors.},
	doi = {10.3390/s19143163},
	url = {https://www.mdpi.com/1424-8220/19/14/3163},
	author = {Morelli, Davide and Alessio Rossi and Cairo, Massimo and Clifton, David A}
}
@article {1296,
	title = {Causal inference for social discrimination reasoning},
	journal = {Journal of Intelligent Information Systems},
	year = {2019},
	pages = {1{\textendash}13},
	abstract = {The 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.},
	doi = {10.1007/s10844-019-00580-x},
	url = {https://link.springer.com/article/10.1007/s10844-019-00580-x},
	author = {Qureshi, Bilal and Kamiran, Faisal and Karim, Asim and Salvatore Ruggieri and Dino Pedreschi}
}
@article {1407,
	title = {CDLIB: a python library to extract, compare and evaluate communities from complex networks},
	journal = {Applied Network Science},
	volume = {4},
	year = {2019},
	month = {2019/07/29},
	pages = {52},
	abstract = {Community Discovery is among the most studied problems in complex network analysis. During the last decade, many algorithms have been proposed to address such task; however, only a few of them have been integrated into a common framework, making it hard to use and compare different solutions. To support developers, researchers and practitioners, in this paper we introduce a python library - namely CDlib - designed to serve this need. The aim of CDlib is to allow easy and standardized access to a wide variety of network clustering algorithms, to evaluate and compare the results they provide, and to visualize them. It notably provides the largest available collection of community detection implementations, with a total of 39 algorithms.},
	isbn = {2364-8228},
	doi = {https://doi.org/10.1007/s41109-019-0165-9},
	url = {https://link.springer.com/article/10.1007/s41109-019-0165-9},
	author = {Giulio Rossetti and Letizia Milli and Cazabet, R{\'e}my}
}
@inbook {1293,
	title = {Challenges in community discovery on temporal networks},
	booktitle = {Temporal Network Theory},
	year = {2019},
	pages = {181{\textendash}197},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Community discovery is one of the most studied problems in network science. In recent years, many works have focused on discovering communities in temporal networks, thus identifying dynamic communities. Interestingly, dynamic communities are not mere sequences of static ones; new challenges arise from their dynamic nature. Despite the large number of algorithms introduced in the literature, some of these challenges have been overlooked or little studied until recently. In this chapter, we will discuss some of these challenges and recent propositions to tackle them. We will, among other topics, discuss of community events in gradually evolving networks, on the notion of identity through change and the ship of Theseus paradox, on dynamic communities in different types of networks including link streams, on the smoothness of dynamic communities, and on the different types of complexity of algorithms for their discovery. We will also list available tools and libraries adapted to work with this problem.},
	doi = {10.1007/978-3-030-23495-9_10},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-23495-9_10},
	author = {Cazabet, R{\'e}my and Giulio Rossetti}
}
@conference {1372,
	title = {Community-Aware Content Diffusion: Embeddednes and Permeability},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	author = {Letizia Milli and Giulio Rossetti}
}
@conference {1379,
	title = {A complex network approach to semantic spaces: How meaning organizes itself},
	booktitle = {SEBD},
	year = {2019},
	author = {Salvatore Citraro and Giulio Rossetti}
}
@article {1265,
	title = {Defining Geographic Markets from Probabilistic Clusters: A Machine Learning Algorithm Applied to Supermarket Scanner Data},
	journal = {Available at SSRN 3452058},
	year = {2019},
	author = {Bruestle, Stephen and Luca Pappalardo and Riccardo Guidotti}
}
@article {1297,
	title = {Do {\textquotedblleft}girls just wanna have fun{\textquotedblright}? Participation trends and motivational profiles of women in Norway{\textquoteright}s ultimate mass participation ski event},
	journal = {Frontiers in Psychology},
	volume = {10},
	year = {2019},
	pages = {2548},
	abstract = {Mass participation sporting events (MPSEs) are viewed as encouraging regular exercise in the population, but concerns have been expressed about the extent to which they are inclusive for women. This study focuses on an iconic cross-country skiing MPSE in Norway, the Birkebeiner race (BR), which includes different variants (main, Friday, half-distance, and women-only races). In order to shed light on women{\textquoteright}s participation in this specific MPSE, as well as add to the understanding of women{\textquoteright}s MPSEs participation in general, this study was set up to: (i) analyze trends in women{\textquoteright}s participation, (ii) examine the characteristics, and (iii) identify key factors characterizing the motivational profile of women in different BR races, with emphasis on the full-distance vs. the women-only races. Entries in the different races throughout the period 1996{\textendash}2018 were analyzed using an autoregressive model. Information on women{\textquoteright}s sociodemographic characteristics, sport and exercise participation, and a range of psychological variables (motives, perceptions, overall satisfaction, and future participation intention) were extracted from a market survey and analyzed using a machine learning (ML) approach (n = 1,149). Additionally, qualitative information generated through open-ended questions was analyzed thematically (n = 116). The relative prevalence of women in the main BR was generally low (< 20\%). While the other variants contributed to boosting women{\textquoteright}s participation in the overall event, a future increment of women in the main BR was predicted, with women{\textquoteright}s ratings possibly matching the men{\textquoteright}s by the year 2034. Across all races, most of the women were physically active, of medium-high income, and living in the most urbanized region of Norway. Satisfaction and future participation intention were relatively high, especially among the participants in the women-only races. {\textquotedblleft}Exercise goal{\textquotedblright} was the predominant participation motive. The participants in women-only races assigned greater importance to social aspects, and perceived the race as a tradition, whereas those in the full-distance races were younger and gave more importance to performance aspects. These findings corroborate known trends and challenges in MPSE participation, but also contribute to greater understanding in this under-researched field. Further research is needed in order to gain more knowledge on how to foster women{\textquoteright}s participation in MPSEs.},
	doi = {10.3389/fpsyg.2019.02548},
	url = {https://www.frontiersin.org/articles/10.3389/fpsyg.2019.02548/full},
	author = {Calogiuri, Giovanna and Johansen, Patrick Foss and Alessio Rossi and Thurston, Miranda}
}
@article {1377,
	title = {DynComm R Package{\textendash}Dynamic Community Detection for Evolving Networks},
	journal = {arXiv preprint arXiv:1905.01498},
	year = {2019},
	author = {Sarmento, Rui Portocarrero and Lemos, Lu{\'\i}s and Cordeiro, M{\'a}rio and Giulio Rossetti and Cardoso, Douglas}
}
@conference {1357,
	title = {Eva: Attribute-Aware Network Segmentation},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.},
	author = {Salvatore Citraro and Giulio Rossetti}
}
@conference {1373,
	title = {Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery},
	booktitle = {International Conference on Complex Networks and Their Applications},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	author = {Giulio Rossetti}
}
@conference {1262,
	title = {Explaining multi-label black-box classifiers for health applications},
	booktitle = {International Workshop on Health Intelligence},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Today the state-of-the-art performance in classification is achieved by the so-called {\textquotedblleft}black boxes{\textquotedblright}, 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.},
	doi = {10.1007/978-3-030-24409-5_9},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-24409-5_9},
	author = {Cecilia Panigutti and Riccardo Guidotti and Anna Monreale and Dino Pedreschi}
}
@article {1283,
	title = {Factual and Counterfactual Explanations for Black Box Decision Making},
	journal = {IEEE Intelligent Systems},
	year = {2019},
	abstract = {The 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.},
	doi = {10.1109/MIS.2019.2957223},
	url = {https://ieeexplore.ieee.org/abstract/document/8920138},
	author = {Riccardo Guidotti and Anna Monreale and Fosca Giannotti and Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@conference {1195,
	title = {Human Mobility from theory to practice: Data, Models and Applications},
	booktitle = {Companion of The 2019 World Wide Web Conference, WWW 2019, San Francisco, CA, USA, May 13-17, 2019.},
	year = {2019},
	abstract = {The inclusion of tracking technologies in personal devices opened the doors to the analysis of large sets of mobility data like GPS traces and call detail records. This tutorial presents an overview of both modeling principles of human mobility and machine learning models applicable to specific problems. We review the state of the art of five main aspects in human mobility: (1) human mobility data landscape; (2) key measures of individual and collective mobility; (3) generative models at the level of individual, population and mixture of the two; (4) next location prediction algorithms; (5) applications for social good. For each aspect, we show experiments and simulations using the Python library {\textquotedblright}scikit-mobility{\textquotedblright} developed by the presenters of the tutorial.},
	doi = {10.1145/3308560.3320099},
	url = {https://doi.org/10.1145/3308560.3320099},
	author = {Luca Pappalardo and Gianni Barlacchi and Roberto Pellungrini and Filippo Simini}
}
@conference {1263,
	title = {Investigating Neighborhood Generation Methods for Explanations of Obscure Image Classifiers},
	booktitle = {Pacific-Asia Conference on Knowledge Discovery and Data Mining},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Given the wide use of machine learning approaches based on opaque prediction models, understanding the reasons behind decisions of black box decision systems is nowadays a crucial topic. We address the problem of providing meaningful explanations in the widely-applied image classification tasks. In particular, we explore the impact of changing the neighborhood generation function for a local interpretable model-agnostic explanator by proposing four different variants. All the proposed methods are based on a grid-based segmentation of the images, but each of them proposes a different strategy for generating the neighborhood of the image for which an explanation is required. A deep experimentation shows both improvements and weakness of each proposed approach.},
	doi = {10.1007/978-3-030-16148-4_5},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-16148-4_5},
	author = {Riccardo Guidotti and Anna Monreale and Cariaggi, Leonardo}
}
@conference {1376,
	title = {{\textquotedblleft}Know Thyself{\textquotedblright} How Personal Music Tastes Shape the Last. Fm Online Social Network},
	booktitle = {International Symposium on Formal Methods},
	year = {2019},
	publisher = {Springer},
	organization = {Springer},
	author = {Riccardo Guidotti and Giulio Rossetti}
}
@conference {1215,
	title = {Meaningful explanations of Black Box AI decision systems},
	booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
	year = {2019},
	abstract = {Black box AI systems for automated decision making, often based on machine learning over (big) data, map a user{\textquoteright}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.},
	doi = {10.1609/aaai.v33i01.33019780},
	url = {https://aaai.org/ojs/index.php/AAAI/article/view/5050},
	author = {Dino Pedreschi and Fosca Giannotti and Riccardo Guidotti and Anna Monreale and Salvatore Ruggieri and Franco Turini}
}
@article {1294,
	title = {PlayeRank: data-driven performance evaluation and player ranking in soccer via a machine learning approach},
	journal = {ACM Transactions on Intelligent Systems and Technology (TIST)},
	volume = {10},
	number = {5},
	year = {2019},
	pages = {1{\textendash}27},
	abstract = {The 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{\textquoteright} 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{\textemdash}i.e. searching players and player versatility{\textemdash}showing its flexibility and efficiency, which makes it worth to be used in the design of a scalable platform for soccer analytics.},
	doi = {10.1145/3343172},
	url = {https://dl.acm.org/doi/abs/10.1145/3343172},
	author = {Luca Pappalardo and Paolo Cintia and Ferragina, Paolo and Massucco, Emanuele and Dino Pedreschi and Fosca Giannotti}
}
@conference {1432,
	title = {Privacy Risk for Individual Basket Patterns},
	booktitle = {ECML PKDD 2018 Workshops},
	year = {2019},
	month = {2019//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {Retail data are of fundamental importance for businesses and enterprises that want to understand the purchasing behaviour of their customers. Such data is also useful to develop analytical services and for marketing purposes, often based on individual purchasing patterns. However, retail data and extracted models may also provide very sensitive information to possible malicious third parties. Therefore, in this paper we propose a methodology for empirically assessing privacy risk in the releasing of individual purchasing data. The experiments on real-world retail data show that although individual patterns describe a summary of the customer activity, they may be successful used for the customer re-identifiation.},
	isbn = {978-3-030-13463-1},
	doi = {https://doi.org/10.1007/978-3-030-13463-1_11},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-13463-1_11},
	author = {Roberto Pellungrini and Anna Monreale and Riccardo Guidotti},
	editor = {Alzate, Carlos and Anna Monreale and Bioglio, Livio and Bitetta, Valerio and Bordino, Ilaria and Caldarelli, Guido and Ferretti, Andrea and Riccardo Guidotti and Gullo, Francesco and Pascolutti, Stefano and Pensa, Ruggero G. and Robardet, C{\'e}line and Squartini, Tiziano}
}
@article {1266,
	title = {A public data set of spatio-temporal match events in soccer competitions},
	journal = {Scientific data},
	volume = {6},
	number = {1},
	year = {2019},
	pages = {1{\textendash}15},
	abstract = {Soccer 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.},
	doi = {10.1038/s41597-019-0247-7},
	url = {https://www.nature.com/articles/s41597-019-0247-7},
	author = {Luca Pappalardo and Paolo Cintia and Alessio Rossi and Massucco, Emanuele and Ferragina, Paolo and Dino Pedreschi and Fosca Giannotti}
}
@article {1217,
	title = {Public opinion and Algorithmic bias},
	journal = {ERCIM News},
	number = {116},
	year = {2019},
	url = {https://ercim-news.ercim.eu/en116/special/public-opinion-and-algorithmic-bias},
	author = {Alina Sirbu and Fosca Giannotti and Dino Pedreschi and Kert{\'e}sz, J{\'a}nos}
}
@article {1295,
	title = {Relationship between External and Internal Workloads in Elite Soccer Players: Comparison between Rate of Perceived Exertion and Training Load},
	journal = {Applied Sciences},
	volume = {9},
	number = {23},
	year = {2019},
	pages = {5174},
	abstract = {The use of machine learning (ML) in soccer allows for the management of a large amount of data deriving from the monitoring of sessions and matches. Although the rate of perceived exertion (RPE), training load (S-RPE), and global position system (GPS) are standard methodologies used in team sports to assess the internal and external workload; how the external workload affects RPE and S-RPE remains still unclear. This study explores the relationship between both RPE and S-RPE and the training workload through ML. Data were recorded from 22 elite soccer players, in 160 training sessions and 35 matches during the 2015/2016 season, by using GPS tracking technology. A feature selection process was applied to understand which workload features influence RPE and S-RPE the most. Our results show that the training workloads performed in the previous week have a strong effect on perceived exertion and training load. On the other hand, the analysis of our predictions shows higher accuracy for medium RPE and S-RPE values compared with the extremes. These results provide further evidence of the usefulness of ML as a support to athletic trainers and coaches in understanding the relationship between training load and individual-response in team sports.},
	doi = {10.3390/app9235174},
	url = {https://www.mdpi.com/2076-3417/9/23/5174/htm},
	author = {Alessio Rossi and Perri, Enrico and Luca Pappalardo and Paolo Cintia and Iaia, F Marcello}
}
@proceedings {1391,
	title = {SAI a Sensible Artificial Intelligence that plays Go},
	year = {2019},
	author = {F Morandin and G Amato and R Gini and C Metta and M Parton and G.C. Pascutto}
}
@book {1225,
	title = {Sar{\`o} Franco - Vita di Franco Turini, executive chef dell{\textquoteright}Universit{\`a} di Pisa},
	year = {2019},
	pages = {20},
	publisher = {Pisa University Press},
	organization = {Pisa University Press},
	address = {Pisa, Italy},
	abstract = {Chi {\`e} Franco Turini? Come molti sanno, uno dei pionieri dell{\textquoteright}informatica italiana. Ma non {\`e} questa la domanda che ci interessa. Quella a cui questo breve saggio si propone di rispondere {\`e} una questione molto pi{\`u} importante: chi avrebbe voluto essere Franco Turini?
In questo scritto, la vita e la carriera di Turini vengono ripercorse alla luce della sua vera, unica e irredimibile passione: la cucina. In un intreccio romanzesco, denso di colpi di scena e assolutamente falso e tendenzioso, il contributo di Franco Turini all{\textquoteright}informatica e all{\textquoteright}intelligenza artificiale si dipana, indissolubilmente intrecciato alla sua passione per i fornelli, attraverso le molte intuizioni geniali che lo hanno colpito mentre cucinava.},
	issn = {978-88-3339-252-3},
	url = {https://store.streetlib.com/it/marco-malvaldi/saro-franco-9788833392523/},
	author = {Marco Malvaldi}
}
@conference {1264,
	title = {On The Stability of Interpretable Models},
	booktitle = {2019 International Joint Conference on Neural Networks (IJCNN)},
	year = {2019},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process. Bias in data collection and preparation, or in model{\textquoteright}s construction may severely affect the accountability of the design process. We conduct an experimental study of the stability of interpretable models with respect to feature selection, instance selection, and model selection. Our conclusions should raise awareness and attention of the scientific community on the need of a stability impact assessment of interpretable models.},
	doi = {https://doi.org/10.1109/IJCNN.2019.8852158},
	url = {https://ieeexplore.ieee.org/abstract/document/8852158},
	author = {Riccardo Guidotti and Salvatore Ruggieri}
}
@article {1382,
	title = {Towards the dynamic community discovery in decentralized online social networks},
	journal = {Journal of Grid Computing},
	volume = {17},
	number = {1},
	year = {2019},
	pages = {23{\textendash}44},
	author = {Guidi, Barbara and Michienzi, Andrea and Giulio Rossetti}
}
@article {1218,
	title = {Transparency in Algorithmic Decision Making},
	journal = {ERCIM News},
	number = {116},
	year = {2019},
	url = {https://ercim-news.ercim.eu/en116/special/transparency-in-algorithmic-decision-making-introduction-to-the-special-theme},
	author = {Andreas Rauber and Roberto Trasarti and Fosca Giannotti}
}
@article {1272,
	title = {A Visual Analytics Platform to Measure Performance on University Entrance Tests (Discussion Paper)},
	year = {2019},
	author = {Boncoraglio, Daniele and Deri, Francesca and Distefano, Francesco and Daniele Fadda and Filippi, Giorgio and Forte, Giuseppe and Licari, Federica and Michela Natilli and Dino Pedreschi and S Rinzivillo}
}
@article {1179,
	title = {Active and passive diffusion processes in complex networks},
	journal = {Applied network science},
	volume = {3},
	number = {1},
	year = {2018},
	pages = {42},
	abstract = {Ideas, 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.},
	doi = {https://doi.org/10.1007/s41109-018-0100-5},
	url = {https://link.springer.com/article/10.1007/s41109-018-0100-5},
	author = {Letizia Milli and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@conference {1197,
	title = {Analyzing Privacy Risk in Human Mobility Data},
	booktitle = {Software Technologies: Applications and Foundations - STAF 2018 Collocated Workshops, Toulouse, France, June 25-29, 2018, Revised Selected Papers},
	year = {2018},
	abstract = {Mobility data are of fundamental importance for understanding the patterns of human movements, developing analytical services and modeling human dynamics. Unfortunately, mobility data also contain individual sensitive information, making it necessary an accurate privacy risk assessment for the individuals involved. In this paper, we propose a methodology for assessing privacy risk in human mobility data. Given a set of individual and collective mobility features, we define the minimum data format necessary for the computation of each feature and we define a set of possible attacks on these data formats. We perform experiments computing the empirical risk in a real-world mobility dataset, and show how the distributions of the considered mobility features are affected by the removal of individuals with different levels of privacy risk.},
	doi = {10.1007/978-3-030-04771-9_10},
	url = {https://doi.org/10.1007/978-3-030-04771-9_10},
	author = {Roberto Pellungrini and Luca Pappalardo and Francesca Pratesi and Anna Monreale}
}
@article {1135,
	title = {Assessing the Stability of Interpretable Models},
	journal = {arXiv preprint arXiv:1810.09352},
	year = {2018},
	abstract = {Interpretable classification models are built with the purpose of providing a comprehensible description of the decision logic to an external oversight agent. When considered in isolation, a decision tree, a set of classification rules, or a linear model, are widely recognized as human-interpretable. However, such models are generated as part of a larger analytical process, which, in particular, comprises data collection and filtering. Selection bias in data collection or in data pre-processing may affect the model learned. Although model induction algorithms are designed to learn to generalize, they pursue optimization of predictive accuracy. It remains unclear how interpretability is instead impacted. We conduct an experimental analysis to investigate whether interpretable models are able to cope with data selection bias as far as interpretability is concerned.},
	author = {Riccardo Guidotti and Salvatore Ruggieri}
}
@article {999,
	title = {Community Discovery in Dynamic Networks: a Survey},
	journal = {Journal ACM Computing Surveys},
	volume = {51},
	number = {2},
	year = {2018},
	abstract = {Networks built to model real world phenomena are characeterised by some properties that have attracted the attention of the scientific community: (i) they are organised according to community structure and (ii) their structure evolves with time. Many researchers have worked on methods that can efficiently unveil substructures in complex networks, giving birth to the field of community discovery. A novel and challenging problem started capturing researcher interest recently: the identification of evolving communities. To model the evolution of a system, dynamic networks can be used: nodes and edges are mutable and their presence, or absence, deeply impacts the community structure that composes them. The aim of this survey is to present the distinctive features and challenges of dynamic community discovery, and propose a classification of published approaches. As a "user manual", this work organizes state of art methodologies into a taxonomy, based on their rationale, and their specific instanciation. Given a desired definition of network dynamics, community characteristics and analytical needs, this survey will support researchers to identify the set of approaches that best fit their needs. The proposed classification could also help researchers to choose in which direction should future research be oriented. },
	doi = {10.1145/3172867},
	url = {https://dl.acm.org/citation.cfm?id=3172867},
	author = {Giulio Rossetti and Cazabet, R{\'e}my}
}
@conference {1021,
	title = {Diffusive Phenomena in Dynamic Networks: a data-driven study},
	booktitle = {International Conference on Complex Networks CompleNet},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	address = {Boston March 5-8 2018},
	abstract = {Everyday, 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 {\textendash} following a data-driven approach {\textendash} 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.},
	doi = {10.1007/978-3-319-73198-8_13},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-73198-8_13},
	author = {Letizia Milli and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@conference {1046,
	title = {Discovering Mobility Functional Areas: A Mobility Data Analysis Approach},
	booktitle = {International Workshop on Complex Networks},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	abstract = {How 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.},
	doi = {10.1007/978-3-319-73198-8_27},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-73198-8_27},
	author = {Lorenzo Gabrielli and Daniele Fadda and Giulio Rossetti and Mirco Nanni and Piccinini, Leonardo and Dino Pedreschi and Fosca Giannotti and Patrizia Lattarulo}
}
@article {1130,
	title = {Discovering temporal regularities in retail customers{\textquoteright} shopping behavior},
	journal = {EPJ Data Science},
	volume = {7},
	number = {1},
	year = {2018},
	month = {01/2018},
	pages = {6},
	abstract = {In 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{\textquoteright}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.},
	doi = {10.1140/epjds/s13688-018-0133-0},
	url = {https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0133-0},
	author = {Riccardo Guidotti and Lorenzo Gabrielli and Anna Monreale and Dino Pedreschi and Fosca Giannotti}
}
@article {1086,
	title = {Effective injury forecasting in soccer with GPS training data and machine learning},
	journal = {PloS one},
	volume = {13},
	number = {7},
	year = {2018},
	pages = {e0201264},
	abstract = {Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer.},
	doi = {https://doi.org/10.1371/journal.pone.0201264},
	url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0201264},
	author = {Alessio Rossi and Luca Pappalardo and Paolo Cintia and Iaia, F Marcello and Fern{\`a}ndez, Javier and Medina, Daniel}
}
@conference {1136,
	title = {Explaining successful docker images using pattern mining analysis},
	booktitle = {Federation of International Conferences on Software Technologies: Applications and Foundations},
	year = {2018},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Docker is on the rise in today{\textquoteright}s enterprise IT. It permits shipping applications inside portable containers, which run from so-called Docker images. Docker images are distributed in public registries, which also monitor their popularity. The popularity of an image directly impacts on its usage, and hence on the potential revenues of its developers. In this paper, we present a frequent pattern mining-based approach for understanding how to improve an image to increase its popularity. The results in this work can provide valuable insights to Docker image providers, helping them to design more competitive software products.},
	doi = {10.1007/978-3-030-04771-9_9},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-04771-9_9},
	author = {Riccardo Guidotti and Soldani, Jacopo and Neri, Davide and Antonio Brogi}
}
@conference {1269,
	title = {Exploring Students Eating Habits Through Individual Profiling and Clustering Analysis},
	booktitle = {ECML PKDD 2018 Workshops},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	author = {Michela Natilli and Anna Monreale and Riccardo Guidotti and Luca Pappalardo}
}
@conference {1039,
	title = {The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Nowadays 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 {\textquotedblleft}fractal{\textquotedblright} 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{\textquoteright} 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.},
	doi = {10.1007/978-3-319-76111-4_19},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-76111-4_19},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@article {1194,
	title = {Gastroesophageal reflux symptoms among Italian university students: epidemiology and dietary correlates using automatically recorded transactions},
	journal = {BMC gastroenterology},
	volume = {18},
	number = {1},
	year = {2018},
	pages = {116},
	abstract = {Background: 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{\textendash}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 students},
	doi = {10.1186/s12876-018-0832-9},
	url = {https://bmcgastroenterol.biomedcentral.com/articles/10.1186/s12876-018-0832-9},
	author = {Martinucci, Irene and Michela Natilli and Lorenzoni, Valentina and Luca Pappalardo and Anna Monreale and Turchetti, Giuseppe and Dino Pedreschi and Marchi, Santino and Barale, Roberto and de Bortoli, Nicola}
}
@article {1193,
	title = {Gravity and scaling laws of city to city migration},
	journal = {PLOS ONE},
	volume = {13},
	number = {7},
	year = {2018},
	month = {07},
	pages = {1-19},
	abstract = {Models of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities. Building upon these findings we develop a scaling model which describes internal migration as a two-step decision process, demonstrating that it can partially explain migration fluxes based solely on city size. We then consider the impact of distance and construct a gravity-scaling model by combining the observed scaling patterns with the gravity law of migration. Results show that the scaling laws are a significant feature of human migration and that the inclusion of scaling can overcome the limits of the gravity and the radiation models of human migration.},
	doi = {10.1371/journal.pone.0199892},
	url = {https://doi.org/10.1371/journal.pone.0199892},
	author = {Prieto Curiel, Rafael and Luca Pappalardo and Lorenzo Gabrielli and Bishop, Steven Richard}
}
@conference {1292,
	title = {Helping your docker images to spread based on explainable models},
	booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Docker is on the rise in today{\textquoteright}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.},
	doi = {10.1007/978-3-030-10997-4_13},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-10997-4_13},
	author = {Riccardo Guidotti and Soldani, Jacopo and Neri, Davide and Brogi, Antonio and Dino Pedreschi}
}
@inbook {1422,
	title = {How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science},
	booktitle = {A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years},
	year = {2018},
	pages = {287 - 306},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {During 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.},
	isbn = {978-3-319-61893-7},
	doi = {https://doi.org/10.1007/978-3-319-61893-7_17},
	url = {https://link.springer.com/chapter/10.1007\%2F978-3-319-61893-7_17},
	author = {Amato, G. and Candela, L. and Castelli, D. and Esuli, A. and Falchi, F. and Gennaro, C. and Fosca Giannotti and Anna Monreale and Mirco Nanni and Pagano, P. and Luca Pappalardo and Dino Pedreschi and Francesca Pratesi and Rabitti, F. and S Rinzivillo and Giulio Rossetti and Salvatore Ruggieri and Sebastiani, F. and Tesconi, M.},
	editor = {Flesca, Sergio and Greco, Sergio and Masciari, Elio and Sacc{\`a}, Domenico}
}
@article {1133,
	title = {The italian music superdiversity},
	journal = {Multimedia Tools and Applications},
	year = {2018},
	pages = {1{\textendash}23},
	abstract = {Globalization 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{\textquoteright} melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.},
	doi = {10.1007/s11042-018-6511-6},
	url = {https://link.springer.com/article/10.1007/s11042-018-6511-6},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@conference {1427,
	title = {Learning Data Mining},
	booktitle = {2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)},
	year = {2018},
	month = {2018},
	abstract = {In the last decade the usage and study of data mining and machine learning algorithms have received an increasing attention from several and heterogeneous fields of research. Learning how and why a certain algorithm returns a particular result, and understanding which are the main problems connected to its execution is a hot topic in the education of data mining methods. In order to support data mining beginners, students, teachers, and researchers we introduce a novel didactic environment. The Didactic Data Mining Environment (DDME) allows to execute a data mining algorithm on a dataset and to observe the algorithm behavior step by step to learn how and why a certain result is returned. DDME can be practically exploited by teachers and students for having a more interactive learning of data mining. Indeed, on top of the core didactic library, we designed a visual platform that allows online execution of experiments and the visualization of the algorithm steps. The visual platform abstracts the coding activity and makes available the execution of algorithms to non-technicians.
},
	doi = {https://doi.org/10.1109/DSAA.2018.00047},
	url = {https://ieeexplore.ieee.org/document/8631453},
	author = {Riccardo Guidotti and Anna Monreale and S Rinzivillo}
}
@article {1131,
	title = {Local Rule-Based Explanations of Black Box Decision Systems},
	year = {2018},
	author = {Riccardo Guidotti and Anna Monreale and Salvatore Ruggieri and Dino Pedreschi and Franco Turini and Fosca Giannotti}
}
@article {1047,
	title = {NDlib: a python library to model and analyze diffusion processes over complex networks},
	journal = {International Journal of Data Science and Analytics},
	volume = {5},
	number = {1},
	year = {2018},
	pages = {61{\textendash}79},
	abstract = {Nowadays 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.},
	doi = {10.1007/s41060-017-0086-6},
	url = {https://link.springer.com/article/10.1007/s41060-017-0086-6},
	author = {Giulio Rossetti and Letizia Milli and S Rinzivillo and Alina Sirbu and Dino Pedreschi and Fosca Giannotti}
}
@article {1132,
	title = {Open the Black Box Data-Driven Explanation of Black Box Decision Systems},
	year = {2018},
	author = {Dino Pedreschi and Fosca Giannotti and Riccardo Guidotti and Anna Monreale and Luca Pappalardo and Salvatore Ruggieri and Franco Turini}
}
@article {1134,
	title = {Personalized Market Basket Prediction with Temporal Annotated Recurring Sequences},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	year = {2018},
	abstract = {Nowadays, 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{\textquoteright}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{\textquoteright}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.},
	doi = {10.1109/TKDE.2018.2872587},
	url = {https://ieeexplore.ieee.org/abstract/document/8477157},
	author = {Riccardo Guidotti and Giulio Rossetti and Luca Pappalardo and Fosca Giannotti and Dino Pedreschi}
}
@article {1138,
	title = {PRUDEnce: a system for assessing privacy risk vs utility in data sharing ecosystems},
	journal = {Transactions on Data Privacy},
	volume = {11},
	number = {2},
	year = {2018},
	month = {08/2018},
	abstract = {Data 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{\textquoteright}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.},
	url = {http://www.tdp.cat/issues16/tdp.a284a17.pdf},
	author = {Francesca Pratesi and Anna Monreale and Roberto Trasarti and Fosca Giannotti and Dino Pedreschi and Yanagihara, Tadashi}
}
@conference {1053,
	title = {SoBigData: Social Mining \& Big Data Ecosystem},
	booktitle = {Companion of the The Web Conference 2018 on The Web Conference 2018},
	year = {2018},
	publisher = {International World Wide Web Conferences Steering Committee},
	organization = {International World Wide Web Conferences Steering Committee},
	abstract = {One of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena. Such challenge requires high-level analytics, modeling and reasoning across all the social dimensions above. There is a need to harness these opportunities for scientific advancement and for the social good, compared to the currently prevalent exploitation of big data for commercial purposes or, worse, social control and surveillance. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open ecosystem where big data and social mining research can be carried out. The SoBigData Research Infrastructure (RI) provides an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life as recorded by "big data". The research community uses the SoBigData facilities as a "secure digital wind-tunnel" for large-scale social data analysis and simulation experiments. SoBigData promotes repeatable and open science and supports data science research projects by providing: i) an ever-growing, distributed data ecosystem for procurement, access and curation and management of big social data, to underpin social data mining research within an ethic-sensitive context; ii) an ever-growing, distributed platform of interoperable, social data mining methods and associated skills: tools, methodologies and services for mining, analysing, and visualising complex and massive datasets, harnessing the techno-legal barriers to the ethically safe deployment of big data for social mining; iii) an ecosystem where protection of personal information and the respect for fundamental human rights can coexist with a safe use of the same information for scientific purposes of broad and central societal interest. SoBigData has a dedicated ethical and legal board, which is implementing a legal and ethical framework.},
	url = {http://www.sobigdata.eu/sites/default/files/www\%202018.pdf},
	author = {Fosca Giannotti and Roberto Trasarti and Bontcheva, Kalina and Valerio Grossi}
}
@article {1261,
	title = {A survey of methods for explaining black box models},
	journal = {ACM computing surveys (CSUR)},
	volume = {51},
	number = {5},
	year = {2018},
	pages = {93},
	abstract = {In 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.},
	doi = {10.1145/3236009},
	url = {https://dl.acm.org/doi/abs/10.1145/3236009},
	author = {Riccardo Guidotti and Anna Monreale and Salvatore Ruggieri and Franco Turini and Fosca Giannotti and Dino Pedreschi}
}
@conference {1291,
	title = {Weak nodes detection in urban transport systems: Planning for resilience in Singapore},
	booktitle = {2018 IEEE 5th international conference on data science and advanced analytics (DSAA)},
	year = {2018},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The availability of massive data-sets describing human mobility offers the possibility to design simulation tools to monitor and improve the resilience of transport systems in response to traumatic events such as natural and man-made disasters (e.g., floods, terrorist attacks, etc. . . ). In this perspective, we propose ACHILLES, an application to models people{\textquoteright}s movements in a given transport mode through a multiplex network representation based on mobility data. ACHILLES is a web-based application which provides an easy-to-use interface to explore the mobility fluxes and the connectivity of every urban zone in a city, as well as to visualize changes in the transport system resulting from the addition or removal of transport modes, urban zones, and single stops. Notably, our application allows the user to assess the overall resilience of the transport network by identifying its weakest node, i.e. Urban Achilles Heel, with reference to the ancient Greek mythology. To demonstrate the impact of ACHILLES for humanitarian aid we consider its application to a real-world scenario by exploring human mobility in Singapore in response to flood prevention.},
	doi = {10.1109/DSAA.2018.00061},
	url = {https://ieeexplore.ieee.org/abstract/document/8631413/authors$\#$authors},
	author = {Ferretti, Michele and Barlacchi, Gianni and Luca Pappalardo and Lucchini, Lorenzo and Lepri, Bruno}
}
@inbook {848,
	title = {Applications for Environmental Sensing in EveryAware},
	booktitle = {Participatory Sensing, Opinions and Collective Awareness},
	year = {2017},
	pages = {135{\textendash}155},
	publisher = {Springer},
	organization = {Springer},
	abstract = {This chapter provides a technical description of the EveryAware applications for air quality and noise monitoring. Specifically, we introduce AirProbe, for measuring air quality, and WideNoise Plus for estimating environmental noise. We also include an overview on hardware components and smartphone-based measurement technology, and we present the according web backend, e.g., providing for real-time tracking, data storage, analysis and visualizations.

},
	doi = {10.1007/978-3-319-25658-0_7},
	url = {http://link.springer.com/chapter/10.1007/978-3-319-25658-0_7},
	author = {Atzmueller, Martin and Becker, Martin and Molino, Andrea and Mueller, Juergen and Peters, Jan and Alina Sirbu}
}
@conference {1198,
	title = {Assessing Privacy Risk in Retail Data},
	booktitle = {Personal Analytics and Privacy. An Individual and Collective Perspective - First International Workshop, PAP 2017, Held in Conjunction with ECML PKDD 2017, Skopje, Macedonia, September 18, 2017, Revised Selected Papers},
	year = {2017},
	abstract = {Retail data are one of the most requested commodities by commercial companies. Unfortunately, from this data it is possible to retrieve highly sensitive information about individuals. Thus, there exists the need for accurate individual privacy risk evaluation. In this paper, we propose a methodology for assessing privacy risk in retail data. We define the data formats for representing retail data, the privacy framework for calculating privacy risk and some possible privacy attacks for this kind of data. We perform experiments in a real-world retail dataset, and show the distribution of privacy risk for the various attacks.},
	doi = {10.1007/978-3-319-71970-2_3},
	url = {https://doi.org/10.1007/978-3-319-71970-2_3},
	author = {Roberto Pellungrini and Francesca Pratesi and Luca Pappalardo}
}
@article {1178,
	title = {Assessing the use of mobile phone data to describe recurrent mobility patterns in spatial epidemic models},
	journal = {Royal Society open science},
	volume = {4},
	number = {5},
	year = {2017},
	pages = {160950},
	abstract = {The recent availability of large-scale call detail record data has substantially improved our ability of quantifying human travel patterns with broad applications in epidemiology. Notwithstanding a number of successful case studies, previous works have shown that using different mobility data sources, such as mobile phone data or census surveys, to parametrize infectious disease models can generate divergent outcomes. Thus, it remains unclear to what extent epidemic modelling results may vary when using different proxies for human movements. Here, we systematically compare 658 000 simulated outbreaks generated with a spatially structured epidemic model based on two different human mobility networks: a commuting network of France extracted from mobile phone data and another extracted from a census survey. We compare epidemic patterns originating from all the 329 possible outbreak seed locations and identify the structural network properties of the seeding nodes that best predict spatial and temporal epidemic patterns to be alike. We find that similarity of simulated epidemics is significantly correlated to connectivity, traffic and population size of the seeding nodes, suggesting that the adequacy of mobile phone data for infectious disease models becomes higher when epidemics spread between highly connected and heavily populated locations, such as large urban areas.},
	author = {Cecilia Panigutti and Tizzoni, Michele and Bajardi, Paolo and Smoreda, Zbigniew and Colizza, Vittoria}
}
@article {1051,
	title = {Authenticated Outlier Mining for Outsourced Databases},
	journal = {IEEE Transactions on Dependable and Secure Computing},
	year = {2017},
	abstract = {The 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.},
	doi = {10.1109/TDSC.2017.2754493},
	url = {https://ieeexplore.ieee.org/document/8048342/},
	author = {Dong, Boxiang and Hui Wendy Wang and Anna Monreale and Dino Pedreschi and Fosca Giannotti and W Guo}
}
@conference {953,
	title = {Clustering Individual Transactional Data for Masses of Users},
	booktitle = {Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	year = {2017},
	publisher = {ACM},
	organization = {ACM},
	abstract = {Mining 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 txmeans},
	doi = {10.1145/3097983.3098034},
	author = {Riccardo Guidotti and Anna Monreale and Mirco Nanni and Fosca Giannotti and Dino Pedreschi}
}
@article {1013,
	title = {A Data Mining Approach to Assess Privacy Risk in Human Mobility Data},
	journal = {ACM Trans. Intell. Syst. Technol.},
	volume = {9},
	number = {3},
	year = {2017},
	pages = {31:1{\textendash}31:27},
	abstract = {Human mobility data are an important proxy to understand human mobility dynamics, develop analytical services, and design mathematical models for simulation and what-if analysis. Unfortunately mobility data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment provide data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be recomputed every time new data records become available and for every selection of individuals, geographic areas, or time windows. In this article, we propose a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.},
	issn = {2157-6904},
	doi = {10.1145/3106774},
	url = {http://doi.acm.org/10.1145/3106774},
	author = {Roberto Pellungrini and Luca Pappalardo and Francesca Pratesi and Anna Monreale}
}
@unpublished {1512,
	title = {Data Science a Game-changer for Science and Innovation},
	year = {2017},
	month = {03/2017},
	publisher = {G7 Academy},
	abstract = {Digital technology is ubiquitous and very much part of public and private organizations and of individuals{\textquoteright} 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.},
	author = {Fabio Beltram and Fosca Giannotti and Dino Pedreschi}
}
@article {1023,
	title = {Data-driven generation of spatio-temporal routines in human mobility},
	journal = {Data Mining and Knowledge Discovery},
	year = {2017},
	month = {Dec},
	abstract = {The generation of realistic spatio-temporal trajectories of human mobility is of fundamental importance in a wide range of applications, such as the developing of protocols for mobile ad-hoc networks or what-if analysis in urban ecosystems. Current generative algorithms fail in accurately reproducing the individuals{\textquoteright} recurrent schedules and at the same time in accounting for the possibility that individuals may break the routine during periods of variable duration. In this article we present Ditras (DIary-based TRAjectory Simulator), a framework to simulate the spatio-temporal patterns of human mobility. Ditras operates in two steps: the generation of a mobility diary and the translation of the mobility diary into a mobility trajectory. We propose a data-driven algorithm which constructs a diary generator from real data, capturing the tendency of individuals to follow or break their routine. We also propose a trajectory generator based on the concept of preferential exploration and preferential return. We instantiate Ditras with the proposed diary and trajectory generators and compare the resulting algorithm with real data and synthetic data produced by other generative algorithms, built by instantiating Ditras with several combinations of diary and trajectory generators. We show that the proposed algorithm reproduces the statistical properties of real trajectories in the most accurate way, making a step forward the understanding of the origin of the spatio-temporal patterns of human mobility.},
	issn = {1573-756X},
	doi = {10.1007/s10618-017-0548-4},
	url = {https://doi.org/10.1007/s10618-017-0548-4},
	author = {Luca Pappalardo and Filippo Simini}
}
@article {1037,
	title = {Discovering and Understanding City Events with Big Data: The Case of Rome},
	journal = {Information},
	volume = {8},
	number = {3},
	year = {2017},
	month = {06/2017},
	pages = {74},
	abstract = {The increasing availability of large amounts of data and digital footprints has given rise
to ambitious research challenges in many fields, which spans from medical research, financial and
commercial world, to people and environmental monitoring. Whereas traditional data sources and
census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge
providing useful and hidden information to analysts and decision makers. With this paper, we focus
on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study
and evaluate the impact of these events over the typical city dynamics. We present an analytical
process able to discover, understand and characterize city events from Call Detail Record, designing
a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users.
The methodology provides an useful tool for city mobility manager to manage the events and taking
future decisions on specific classes of users, i.e., residents, commuters and tourists.},
	doi = {10.3390/info8030074},
	url = {https://doi.org/10.3390/info8030074},
	author = {Barbara Furletti and Roberto Trasarti and Paolo Cintia and Lorenzo Gabrielli}
}
@conference {1390,
	title = {Dynamic community analysis in decentralized online social networks},
	booktitle = {European Conference on Parallel Processing},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	author = {Guidi, Barbara and Michienzi, Andrea and Giulio Rossetti}
}
@article {971,
	title = {Efficiently Clustering Very Large Attributed Graphs},
	journal = {arXiv preprint arXiv:1703.08590},
	year = {2017},
	abstract = {Attributed graphs model real networks by enriching their nodes with attributes accounting for properties. Several techniques have been proposed for partitioning these graphs into clusters that are homogeneous with respect to both semantic attributes and to the structure of the graph. However, time and space complexities of state of the art algorithms limit their scalability to medium-sized graphs. We propose SToC (for Semantic-Topological Clustering), a fast and scalable algorithm for partitioning large attributed graphs. The approach is robust, being compatible both with categorical and with quantitative attributes, and it is tailorable, allowing the user to weight the semantic and topological components. Further, the approach does not require the user to guess in advance the number of clusters. SToC relies on well known approximation techniques such as bottom-k sketches, traditional graph-theoretic concepts, and a new perspective on the composition of heterogeneous distance measures. Experimental results demonstrate its ability to efficiently compute high-quality partitions of large scale attributed graphs.},
	doi = {10.1145/3110025.3110030},
	author = {Alessandro Baroni and Conte, Alessio and Patrignani, Maurizio and Salvatore Ruggieri}
}
@article {1052,
	title = {An empirical verification of a-priori learning models on mailing archives in the context of online learning activities of participants in free\libre open source software (FLOSS) communities},
	journal = {Education and Information Technologies},
	volume = {22},
	number = {6},
	year = {2017},
	pages = {3207{\textendash}3229},
	abstract = {Free\Libre Open Source Software (FLOSS) environments are increasingly dubbed as learning environments where practical software engineering skills can be acquired. Numerous studies have extensively investigated how knowledge is acquired in these environments through a collaborative learning model that define a learning process. Such a learning process, identified either as a result of surveys or by means of questionnaires, can be depicted through a series of graphical representations indicating the steps FLOSS community members go through as they acquire and exchange skills. These representations are referred to as a-priori learning models. They are Petri net-like workflow nets (WF-net) that provide a visual representation of the learning process as it is expected to occur. These models are representations of a learning framework or paradigm in FLOSS communities. As such, the credibility of any models is estimated through a process of model verification and validation. Therefore in this paper, we analyze these models in comparison with the real behavior captured in FLOSS repositories by means of conformance verification in process mining. The purpose of our study is twofold. Firstly, the results of our analysis provide insights on the possible discrepancies that are observed between the initial theoretical representations of learning processes and the real behavior captured in FLOSS event logs, constructed from mailing archives. Secondly, this comparison helps foster the understanding on how learning actually takes place in FLOSS environments based on empirical evidence directly from the data.},
	doi = {10.1007/s10639-017-9573-6},
	url = {https://link.springer.com/article/10.1007/s10639-017-9573-6},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@conference {970,
	title = {Enumerating Distinct Decision Trees},
	booktitle = {International Conference on Machine Learning},
	year = {2017},
	abstract = {The search space for the feature selection problem in decision tree learning is the lattice of subsets of the available features. We provide an exact enumeration procedure of the subsets that lead to all and only the distinct decision trees. The procedure can be adopted to prune the search space of complete and heuristics search methods in wrapper models for feature selection. Based on this, we design a computational optimization of the sequential backward elimination heuristics with a performance improvement of up to 100X.},
	url = {http://proceedings.mlr.press/v70/ruggieri17a.html},
	author = {Salvatore Ruggieri}
}
@conference {1127,
	title = {On the Equivalence Between Community Discovery and Clustering},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2017},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	author = {Riccardo Guidotti and Michele Coscia}
}
@inbook {850,
	title = {Experimental Assessment of the Emergence of Awareness and Its Influence on Behavioral Changes: The Everyaware Lesson},
	booktitle = {Participatory Sensing, Opinions and Collective Awareness},
	year = {2017},
	pages = {337{\textendash}362},
	publisher = {Springer},
	organization = {Springer},
	abstract = {The emergence of awareness is deeply connected to the process of learning. In fact, by learning that high sound levels may harm one{\textquoteright}s health, that noise levels that we estimate as innocuous may be dangerous, that there exist an alternative path we can walk to go to work and minimize our exposure to air pollution, etc., citizens will be able to understand the environment around them and act consequently to go toward a more sustainable world.},
	doi = {10.1007/978-3-319-25658-0_16},
	url = {http://link.springer.com/chapter/10.1007/978-3-319-25658-0_16},
	author = {Pietro Gravino and Alina Sirbu and Becker, Martin and Vito D P Servedio and Vittorio Loreto}
}
@booklet {993,
	title = {Fast Estimation of Privacy Risk in Human Mobility Data},
	year = {2017},
	abstract = {Mobility data are an important proxy to understand the patterns of human movements, develop analytical services and design models for simulation and prediction of human dynamics. Unfortunately mobility data are also very sensitive, since they may contain personal information about the individuals involved. Existing frameworks for privacy risk assessment enable the data providers to quantify and mitigate privacy risks, but they suffer two main limitations: (i) they have a high computational complexity; (ii) the privacy risk must be re-computed for each new set of individuals, geographic areas or time windows. In this paper we explore a fast and flexible solution to estimate privacy risk in human mobility data, using predictive models to capture the relation between an individual{\textquoteright}s mobility patterns and her privacy risk. We show the effectiveness of our approach by experimentation on a real-world GPS dataset and provide a comparison with traditional methods. },
	isbn = {978-3-319-66283-1},
	doi = {10.1007/978-3-319-66284-8_35 },
	author = {Roberto Pellungrini and Luca Pappalardo and Francesca Pratesi and Anna Monreale}
}
@article {1018,
	title = {Forecasting success via early adoptions analysis: A data-driven study},
	journal = {PloS one},
	volume = {12},
	number = {12},
	year = {2017},
	pages = {e0189096},
	abstract = {Innovations 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{\textquoteright}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.},
	author = {Giulio Rossetti and Letizia Milli and Fosca Giannotti and Dino Pedreschi}
}
@conference {1129,
	title = {The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2017},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Nowadays 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 {\textquotedblleft}fractal{\textquotedblright} 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{\textquoteright} 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.

},
	doi = {https://doi.org/10.1007/978-3-319-76111-4_19},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-76111-4_19},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@article {896,
	title = {HyWare: a HYbrid Workflow lAnguage for Research E-infrastructures},
	journal = {D-Lib Magazine},
	volume = {23},
	number = {1/2},
	year = {2017},
	abstract = {Research e-infrastructures are "systems of systems", patchworks of tools, services and data sources, evolving over time to address the needs of the scientific process. Accordingly, in such environments, researchers implement their scientific processes by means of workflows made of a variety of actions, including for example usage of web services, download and execution of shared software libraries or tools, or local and manual manipulation of data. Although scientists may benefit from sharing their scientific process, the heterogeneity underpinning e-infrastructures hinders their ability to represent, share and eventually reproduce such workflows. This work presents HyWare, a language for representing scientific process in highly-heterogeneous e-infrastructures in terms of so-called hybrid workflows. HyWare lays in between "business process modeling languages", which offer a formal and high-level description of a reasoning, protocol, or procedure, and "workflow execution languages", which enable the fully automated execution of a sequence of computational steps via dedicated engines.},
	doi = {10.1045/january2017-candela},
	url = {http://dx.doi.org/10.1045/january2017-candela},
	author = {Leonardo Candela and Paolo Manghi and Fosca Giannotti and Valerio Grossi and Roberto Trasarti}
}
@article {959,
	title = {ICON Loop Carpooling Show Case},
	journal = {Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach},
	volume = {10101},
	year = {2017},
	pages = {310},
	abstract = {In 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.},
	url = {https://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf$\#$page=314},
	author = {Mirco Nanni and Lars Kotthoff and Riccardo Guidotti and Barry O{\textquoteright}Sullivan and Dino Pedreschi}
}
@article {955,
	title = {The Inductive Constraint Programming Loop},
	journal = {IEEE Intelligent Systems},
	year = {2017},
	abstract = {Constraint 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.},
	doi = {10.1109/MIS.2017.265115706},
	author = {Bessiere, Christian and De Raedt, Luc and Tias Guns and Lars Kotthoff and Mirco Nanni and Siegfried Nijssen and Barry O{\textquoteright}Sullivan and Paparrizou, Anastasia and Dino Pedreschi and Simonis, Helmut}
}
@article {958,
	title = {The Inductive Constraint Programming Loop},
	journal = {Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach},
	volume = {10101},
	year = {2017},
	pages = {303},
	abstract = {Constraint 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.},
	url = {https://link.springer.com/content/pdf/10.1007/978-3-319-50137-6.pdf$\#$page=307},
	author = {Mirco Nanni and Siegfried Nijssen and Barry O{\textquoteright}Sullivan and Paparrizou, Anastasia and Dino Pedreschi and Simonis, Helmut}
}
@conference {1019,
	title = {Information diffusion in complex networks: The active/passive conundrum},
	booktitle = {International Workshop on Complex Networks and their Applications},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Ideas, 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.},
	doi = {10.1007/978-3-319-72150-7_25},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-72150-7_25},
	author = {Letizia Milli and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@inbook {851,
	title = {Large Scale Engagement Through Web-Gaming and Social Computations},
	booktitle = {Participatory Sensing, Opinions and Collective Awareness},
	year = {2017},
	pages = {237{\textendash}254},
	publisher = {Springer},
	organization = {Springer},
	abstract = {In the last few years the Web has progressively acquired the status of an infrastructure for social computation that allows researchers to coordinate the cognitive abilities of human agents, so to steer the collective user activity towards predefined goals. This general trend is also triggering the adoption of web-games as an alternative laboratory to run experiments in the social sciences and whenever the contribution of human beings can be effectively used for research purposes. Web-games introduce a playful aspect in scientific experiments with the result of increasing participation of people and of keeping their attention steady in time. The aim of this chapter is to suggest a general purpose web-based platform scheme for web-gaming and social computation. This platform will simplify the realization of web-games and will act as a repository of different scientific experiments, thus realizing a sort of showcase that stimulates users{\textquoteright} curiosity and helps researchers in recruiting volunteers. A platform built by following these criteria has been developed within the EveryAware project, the Experimental Tribe (XTribe) platform, which is operational and ready to be used. Finally, a sample web-game hosted by the XTribe platform will be presented with the aim of reporting the results, in terms of participation and motivation, of two different player recruiting strategies.},
	doi = {10.1007/978-3-319-25658-0_12},
	url = {http://link.springer.com/chapter/10.1007/978-3-319-25658-0_12},
	author = {Vito D P Servedio and Saverio Caminiti and Pietro Gravino and Vittorio Loreto and Alina Sirbu and Francesca Tria}
}
@conference {1024,
	title = {Market Basket Prediction using User-Centric Temporal Annotated Recurring Sequences},
	booktitle = {2017 IEEE International Conference on Data Mining (ICDM)},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Nowadays, 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{\textquoteright}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{\textquoteright}s stocks and recommend the set of most necessary items. A deep experimentation shows that TARS can explain the customers{\textquoteright} purchase behavior, and that TBP outperforms the state-of-the-art competitors.},
	author = {Riccardo Guidotti and Giulio Rossetti and Luca Pappalardo and Fosca Giannotti and Dino Pedreschi}
}
@conference {1030,
	title = {Movement Behaviour Recognition for Water Activities},
	booktitle = {Personal Analytics and Privacy. An Individual and Collective Perspective - First International Workshop, {PAP} 2017, Held in Conjunction with {ECML} {PKDD} 2017, Skopje, Macedonia, September 18, 2017, Revised Selected Papers},
	year = {2017},
	doi = {10.1007/978-3-319-71970-2_7},
	url = {https://doi.org/10.1007/978-3-319-71970-2_7},
	author = {Mirco Nanni and Roberto Trasarti and Fosca Giannotti}
}
@article {821,
	title = {MyWay: Location prediction via mobility profiling},
	journal = {Information Systems},
	volume = {64},
	year = {2017},
	month = {03/2017},
	pages = {350{\textendash}367},
	abstract = {Forecasting the future positions of mobile users is a valuable task allowing us to operate efficiently a myriad of different applications which need this type of information. We propose MyWay, a prediction system which exploits the individual systematic behaviors modeled by mobility profiles to predict human movements. MyWay provides three strategies: the individual strategy uses only the user individual mobility profile, the collective strategy takes advantage of all users individual systematic behaviors, and the hybrid strategy that is a combination of the previous two. A key point is that MyWay only requires the sharing of individual mobility profiles, a concise representation of the user׳s movements, instead of raw trajectory data revealing the detailed movement of the users. We evaluate the prediction performances of our proposal by a deep experimentation on large real-world data. The results highlight that the synergy between the individual and collective knowledge is the key for a better prediction and allow the system to outperform the state-of-art methods.},
	author = {Roberto Trasarti and Riccardo Guidotti and Anna Monreale and Fosca Giannotti}
}
@article {1020,
	title = {NDlib: a python library to model and analyze diffusion processes over complex networks},
	journal = {International Journal of Data Science and Analytics},
	year = {2017},
	pages = {1{\textendash}19},
	abstract = {Nowadays 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.},
	author = {Giulio Rossetti and Letizia Milli and S Rinzivillo and Alina Sirbu and Dino Pedreschi and Fosca Giannotti}
}
@conference {1022,
	title = {NDlib: Studying Network Diffusion Dynamics},
	booktitle = {IEEE International Conference on Data Science and Advanced Analytics, DSA},
	year = {2017},
	address = {Tokyo},
	abstract = {Nowadays 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.},
	doi = {https://doi.org/10.1109/DSAA.2017.6},
	url = {https://ieeexplore.ieee.org/abstract/document/8259774},
	author = {Giulio Rossetti and Letizia Milli and S Rinzivillo and Alina Sirbu and Dino Pedreschi and Fosca Giannotti}
}
@article {956,
	title = {Never drive alone: Boosting carpooling with network analysis},
	journal = {Information Systems},
	volume = {64},
	year = {2017},
	pages = {237{\textendash}257},
	abstract = {Carpooling, 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.},
	doi = {10.1016/j.is.2016.03.006},
	author = {Riccardo Guidotti and Mirco Nanni and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@article {957,
	title = {Next Basket Prediction using Recurring Sequential Patterns},
	journal = {arXiv preprint arXiv:1702.07158},
	year = {2017},
	abstract = {Nowadays, 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{\textquoteright}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{\textquoteright}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.},
	url = {https://arxiv.org/abs/1702.07158},
	author = {Riccardo Guidotti and Giulio Rossetti and Luca Pappalardo and Fosca Giannotti and Dino Pedreschi}
}
@article {1025,
	title = {Node-centric Community Discovery: From static to dynamic social network analysis},
	journal = {Online Social Networks and Media},
	volume = {3},
	year = {2017},
	pages = {32{\textendash}48},
	abstract = {Nowadays, 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.},
	doi = {https://doi.org/10.1016/j.osnem.2017.10.003},
	url = {https://www.sciencedirect.com/science/article/abs/pii/S2468696417301052},
	author = {Giulio Rossetti and Dino Pedreschi and Fosca Giannotti}
}
@inbook {849,
	title = {Opinion dynamics: models, extensions and external effects},
	booktitle = {Participatory Sensing, Opinions and Collective Awareness},
	year = {2017},
	pages = {363{\textendash}401},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Recently, social phenomena have received a lot of attention not only from social scientists, but also from physicists, mathematicians and computer scientists, in the emerging interdisciplinary field of complex system science. Opinion dynamics is one of the processes studied, since opinions are the drivers of human behaviour, and play a crucial role in many global challenges that our complex world and societies are facing: global financial crises, global pandemics, growth of cities, urbanisation and migration patterns, and last but not least important, climate change and environmental sustainability and protection. Opinion formation is a complex process affected by the interplay of different elements, including the individual predisposition, the influence of positive and negative peer interaction (social networks playing a crucial role in this respect), the information each individual is exposed to, and many others. Several models inspired from those in use in physics have been developed to encompass many of these elements, and to allow for the identification of the mechanisms involved in the opinion formation process and the understanding of their role, with the practical aim of simulating opinion formation and spreading under various conditions. These modelling schemes range from binary simple models such as the voter model, to multi-dimensional continuous approaches. Here, we provide a review of recent methods, focusing on models employing both peer interaction and external information, and emphasising the role that less studied mechanisms, such as disagreement, has in driving the opinion dynamics. Due to the important role that external information (mainly in the form of mass media broadcast) can have in enhancing awareness of social issues, a special emphasis will be devoted to study different forms it can take, investigating their effectiveness in driving the opinion formation at the population level. The review shows that, although a large number of approaches exist, some mechanisms such as the effect of multiple external information sources could largely benefit from further studies. Additionally, model validation with real data, which are starting to become available, is still largely lacking and should in our opinion be the main ambition of future investigations.},
	doi = {10.1007/978-3-319-25658-0_17},
	url = {http://link.springer.com/chapter/10.1007/978-3-319-25658-0_17},
	author = {Alina Sirbu and Vittorio Loreto and Vito D P Servedio and Francesca Tria}
}
@conference {1049,
	title = {Privacy Preserving Multidimensional Profiling},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Recently, 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.},
	doi = {10.1007/978-3-319-76111-4_15},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-76111-4_15},
	author = {Francesca Pratesi and Anna Monreale and Fosca Giannotti and Dino Pedreschi}
}
@article {1012,
	title = {Quantifying the relation between performance and success in soccer},
	journal = {Advances in Complex Systems},
	year = {2017},
	pages = {1750014},
	abstract = {The availability of massive data about sports activities offers nowadays the opportunity to quantify the relation between performance and success. In this study, we analyze more than 6000 games and 10 million events in six European leagues and investigate this relation in soccer competitions. We discover that a team{\textquoteright}s position in a competition{\textquoteright}s final ranking is significantly related to its typical performance, as described by a set of technical features extracted from the soccer data. Moreover, we find that, while victory and defeats can be explained by the team{\textquoteright}s performance during a game, it is difficult to detect draws by using a machine learning approach. We then simulate the outcomes of an entire season of each league only relying on technical data and exploiting a machine learning model trained on data from past seasons. The simulation produces a team ranking which is similar to the actual ranking, suggesting that a complex systems{\textquoteright} view on soccer has the potential of revealing hidden patterns regarding the relation between performance and success.},
	doi = {10.1142/S021952591750014X},
	url = {http://www.worldscientific.com/doi/abs/10.1142/S021952591750014X},
	author = {Luca Pappalardo and Paolo Cintia}
}
@conference {1038,
	title = {Recognizing Residents and Tourists with Retail Data Using Shopping Profiles},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	abstract = {The huge quantity of personal data stored by service providers registering customers daily life enables the analysis of individual fingerprints characterizing the customers{\textquoteright} behavioral profiles. We propose
a framework for recognizing residents, tourists and occasional shoppers
among the customers of a retail market chain. We employ our recognition framework on a real massive dataset containing the shopping transactions of more than one million of customers, and we identify representative temporal shopping profiles for residents, tourists and occasional
customers. Our experiments show that even though residents are about
33\% of the customers they are responsible for more than 90\% of the expenditure. We statistically validate the number of residents and tourists
with national official statistics enabling in this way the adoption of our
recognition framework for the development of novel services and analysis.},
	doi = {10.1007/978-3-319-76111-4_35},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-76111-4_35},
	author = {Riccardo Guidotti and Lorenzo Gabrielli}
}
@article {1029,
	title = {Scalable and flexible clustering solutions for mobile phone-based population indicators},
	journal = {I. J. Data Science and Analytics},
	volume = {4},
	number = {4},
	year = {2017},
	pages = {285{\textendash}299},
	doi = {10.1007/s41060-017-0065-y},
	url = {https://doi.org/10.1007/s41060-017-0065-y},
	author = {Alessandro Lulli and Lorenzo Gabrielli and Patrizio Dazzi and Matteo Dell{\textquoteright}Amico and Pietro Michiardi and Mirco Nanni and Laura Ricci}
}
@article {998,
	title = {Segregation discovery in a social network of companies},
	journal = {Journal of Intelligent Information Systems},
	year = {2017},
	month = {Sep},
	abstract = {We introduce a framework for the data-driven analysis of social segregation of minority groups, and challenge it on a complex scenario. The framework builds on quantitative measures of segregation, called segregation indexes, proposed in the social science literature. The segregation discovery problem is introduced, which consists of searching sub-groups of population and minorities for which a segregation index is above a minimum threshold. A search algorithm is devised that solves the segregation problem by computing a multi-dimensional data cube that can be explored by the analyst. The machinery underlying the search algorithm relies on frequent itemset mining concepts and tools. The framework is challenged on a cases study in the context of company networks. We analyse segregation on the grounds of sex and age for directors in the boards of the Italian companies. The network includes 2.15M companies and 3.63M directors.},
	issn = {1573-7675},
	doi = {10.1007/s10844-017-0485-0},
	url = {https://doi.org/10.1007/s10844-017-0485-0},
	author = {Alessandro Baroni and Salvatore Ruggieri}
}
@conference {1050,
	title = {Sentiment Spreading: An Epidemic Model for Lexicon-Based Sentiment Analysis on Twitter},
	booktitle = {Conference of the Italian Association for Artificial Intelligence},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	abstract = {While 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.},
	doi = {10.1007/978-3-319-70169-1_9},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-70169-1_9},
	author = {Pollacci, Laura and Alina Sirbu and Fosca Giannotti and Dino Pedreschi and Claudio Lucchese and Muntean, Cristina Ioana}
}
@article {966,
	title = {Survey on using constraints in data mining},
	journal = {Data Mining and Knowledge Discovery},
	volume = {31},
	number = {2},
	year = {2017},
	pages = {424{\textendash}464},
	abstract = {This paper provides an overview of the current state-of-the-art on using constraints in knowledge discovery and data mining. The use of constraints in a data mining task requires specific definition and satisfaction tools during knowledge extraction. This survey proposes three groups of studies based on classification, clustering and pattern mining, whether the constraints are on the data, the models or the measures, respectively. We consider the distinctions between hard and soft constraint satisfaction, and between the knowledge extraction phases where constraints are considered. In addition to discussing how constraints can be used in data mining, we show how constraint-based languages can be used throughout the data mining process.},
	doi = {10.1007/s10618-016-0480-z},
	author = {Valerio Grossi and Andrea Romei and Franco Turini}
}
@conference {1031,
	title = {There{\textquoteright}s A Path For Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas},
	booktitle = {4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017)},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Tokyo},
	author = {Riccardo Guidotti and Roberto Trasarti and Mirco Nanni and Fosca Giannotti and Dino Pedreschi}
}
@article {954,
	title = {Tiles: an online algorithm for community discovery in dynamic social networks},
	journal = {Machine Learning},
	volume = {106},
	number = {8},
	year = {2017},
	pages = {1213{\textendash}1241},
	abstract = {Community 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.},
	doi = {10.1007/s10994-016-5582-8},
	url = {https://link.springer.com/article/10.1007/s10994-016-5582-8},
	author = {Giulio Rossetti and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti}
}
@booklet {962,
	title = {Advances in Network Science: 12th International Conference and School, NetSci-X 2016, Wroclaw, Poland, January 11-13, 2016, Proceedings},
	year = {2016},
	abstract = {This 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.},
	doi = {10.1007/978-3-319-28361-6},
	author = {Wierzbicki, Adam and Brandes, Ulrik and Schweitzer, Frank and Dino Pedreschi}
}
@article {961,
	title = {An analytical framework to nowcast well-being using mobile phone data},
	journal = {International Journal of Data Science and Analytics},
	volume = {2},
	number = {1-2},
	year = {2016},
	pages = {75{\textendash}92},
	abstract = {An 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{\textquoteright} 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 {\textquotedblleft}nowcast{\textquotedblright} the well-being and the socio-economic development of a territory.},
	doi = {10.1007/s41060-016-0013-2},
	author = {Luca Pappalardo and Maarten Vanhoof and Lorenzo Gabrielli and Zbigniew Smoreda and Dino Pedreschi and Fosca Giannotti}
}
@proceedings {874,
	title = {{\textquotedblleft}Are we playing like Music-Stars?{\textquotedblright} Placing Emerging Artists on the Italian Music Scene},
	year = {2016},
	month = {09/2016},
	address = {Riva del Garda},
	abstract = {The Italian emerging bands chase success on the footprint of popular artists by playing rhythmic danceable and happy songs. Our finding comes out from a deep study of the Italian music scene and how the new generation ofmusicians relate with the tradition of their country. By analyzing Spotify data we investigated the peculiarity of regional mu- sic and we placed emerging bands within the musical movements defined by already successful artists. The approach proposed and the results ob- tained are a first attempt to outline some rules suggesting how to reach the success in the musical Italian scene.},
	author = {Pollacci, Laura and Riccardo Guidotti and Giulio Rossetti}
}
@conference {882,
	title = {Audio Ergo Sum},
	booktitle = {Federation of International Conferences on Software Technologies: Applications and Foundations},
	year = {2016},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Nobody can state {\textquotedblleft}Rock is my favorite genre{\textquotedblright} or {\textquotedblleft}David Bowie is my favorite artist{\textquotedblright}. 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.},
	doi = {10.1007/978-3-319-50230-4_5},
	author = {Riccardo Guidotti and Giulio Rossetti and Dino Pedreschi}
}
@conference {834,
	title = {Big Data and Public Administration: a case study for Tuscany Airports},
	booktitle = {SEBD - Italian 	Symposium on  Advanced Database Systems },
	year = {2016},
	month = {06/2016},
	publisher = {Matematicamente.it},
	organization = {Matematicamente.it},
	address = {Ugento, Lecce (Italy)},
	abstract = {In the last decade, the fast development of Information and Communication Technologies led to the wide diffusion of sensors able to track various aspects of human activity, as well as the storage and computational capabilities needed to record and analyze them. The so-called Big Data promise to improve the effectiveness of businesses, the quality of urban life, as well as many other fields, including the functioning of public administrations. Yet, translating the wealth of potential information hidden in Big Data to consumable intelligence seems to be still a difficult task, with a limited basis of success stories. This paper reports a project activity centered on a public administration  - IRPET, the Regional Institute for Economic Planning of Tuscany (Italy). The paper deals, among other topics, with human mobility and public transportation at a regional scale, summarizing the open questions posed by the Public Administration (PA), the envisioned role that Big Data might have in answering them, the actual challenges that emerged in trying to implement them, and finally the results we obtained, the limitations that emerged and the lessons learned.},
	isbn = {9788896354889},
	url = {http://sebd2016.unisalento.it/grid/SEBD2016-proceedings.pdf},
	author = {Barbara Furletti and Daniele Fadda and Leonardo Piccini and Mirco Nanni and Patrizia Lattarulo}
}
@article {852,
	title = {Big Data Research in Italy: A Perspective},
	journal = {Engineering},
	volume = {2},
	number = {2},
	year = {2016},
	month = {06/2016},
	pages = {163},
	abstract = {The 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.},
	issn = {print: 2095-8099 / online: 2096-0026},
	doi = {10.1016/J.ENG.2016.02.011},
	url = {http://engineering.org.cn/EN/abstract/article_12288.shtml},
	author = {Sonia Bergamaschi and Emanuele Carlini and Michelangelo Ceci and Barbara Furletti and Fosca Giannotti and Donato Malerba and Mario Mezzanzanica and Anna Monreale and Gabriella Pasi and Dino Pedreschi and Raffaele Perego and Salvatore Ruggieri}
}
@article {972,
	title = {Causal Discrimination Discovery Through Propensity Score Analysis},
	journal = {arXiv preprint arXiv:1608.03735},
	year = {2016},
	abstract = {Social discrimination is considered illegal and unethical in the modern world. Such discrimination is often implicit in observed decisions{\textquoteright} datasets, and anti-discrimination organizations seek to discover cases of discrimination and to understand the reasons behind them. Previous work in this direction adopted simple observational data analysis; however, this can produce biased results due to the effect of confounding variables. In this paper, we propose a causal discrimination discovery and understanding approach based on propensity score analysis. The propensity score is an effective statistical tool for filtering out the effect of confounding variables. We employ propensity score weighting to balance the distribution of individuals from protected and unprotected groups w.r.t. the confounding variables. For each individual in the dataset, we quantify its causal discrimination or favoritism with a neighborhood-based measure calculated on the balanced distributions. Subsequently, the causal discrimination/favoritism patterns are understood by learning a regression tree. Our approach avoids common pitfalls in observational data analysis and make its results legally admissible. We demonstrate the results of our approach on two discrimination datasets.},
	url = {https://arxiv.org/abs/1608.03735},
	author = {Qureshi, Bilal and Kamiran, Faisal and Karim, Asim and Salvatore Ruggieri}
}
@conference {968,
	title = {Classification Rule Mining Supported by Ontology for Discrimination Discovery},
	booktitle = {Data Mining Workshops (ICDMW), 2016 IEEE 16th International Conference on},
	year = {2016},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Discrimination discovery from data consists of designing data mining methods for the actual discovery of discriminatory situations and practices hidden in a large amount of historical decision records. Approaches based on classification rule mining consider items at a flat concept level, with no exploitation of background knowledge on the hierarchical and inter-relational structure of domains. On the other hand, ontologies are a widespread and ever increasing means for expressing such a knowledge. In this paper, we propose a framework for discrimination discovery from ontologies, where contexts of prima-facie evidence of discrimination are summarized in the form of generalized classification rules at different levels of abstraction. Throughout the paper, we adopt a motivating and intriguing case study based on discriminatory tariffs applied by the U. S. Harmonized Tariff Schedules on imported goods.},
	doi = {10.1109/ICDMW.2016.0128},
	author = {Luong, Binh Thanh and Salvatore Ruggieri and Franco Turini}
}
@booklet {963,
	title = {Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach.},
	year = {2016},
	abstract = {A 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 {\textquotedblleft}Inductive Constraint Programming{\textquotedblright} 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. },
	doi = {10.1007/978-3-319-50137-6},
	author = {Bessiere, Christian and De Raedt, Luc and Lars Kotthoff and Siegfried Nijssen and Barry O{\textquoteright}Sullivan and Dino Pedreschi}
}
@inbook {965,
	title = {Data Mining and Constraints: An Overview},
	booktitle = {Data Mining and Constraint Programming},
	year = {2016},
	pages = {25{\textendash}48},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {This 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.},
	doi = {10.1007/978-3-319-50137-6_2},
	author = {Valerio Grossi and Dino Pedreschi and Franco Turini}
}
@article {875,
	title = {Driving Profiles Computation and Monitoring for Car Insurance CRM},
	journal = {Journal ACM Transactions on Intelligent Systems and Technology (TIST)},
	volume = {8},
	number = {1},
	year = {2016},
	pages = {14:1{\textendash}14:26},
	abstract = {Customer 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.},
	doi = {10.1145/2912148},
	url = {http://doi.acm.org/10.1145/2912148},
	author = {Mirco Nanni and Roberto Trasarti and Anna Monreale and Valerio Grossi and Dino Pedreschi}
}
@inbook {817,
	title = {Going Beyond GDP to Nowcast Well-Being Using Retail Market Data},
	booktitle = {Advances in Network Science},
	year = {2016},
	pages = {29{\textendash}42},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {One 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.},
	doi = {10.1007/978-3-319-28361-6_3},
	author = {Riccardo Guidotti and Michele Coscia and Dino Pedreschi and Diego Pennacchioli}
}
@article {960,
	title = {Homophilic network decomposition: a community-centric analysis of online social services},
	journal = {Social Network Analysis and Mining},
	volume = {6},
	number = {1},
	year = {2016},
	pages = {103},
	abstract = {In 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{\textemdash}Skype, LastFM and Google+{\textemdash}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+.},
	doi = {10.1007/s1327},
	author = {Giulio Rossetti and Luca Pappalardo and Riivo Kikas and Dino Pedreschi and Fosca Giannotti and Marlon Dumas}
}
@conference {969,
	title = {A KDD process for discrimination discovery},
	booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
	year = {2016},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {The acceptance of analytical methods for discrimination discovery by practitioners and legal scholars can be only achieved if the data mining and machine learning communities will be able to provide case studies, methodological refinements, and the consolidation of a KDD process. We summarize here an approach along these directions.},
	doi = {10.1007/978-3-319-46131-1_28},
	author = {Salvatore Ruggieri and Franco Turini}
}
@conference {822,
	title = {A novel approach to evaluate community detection algorithms on ground truth},
	booktitle = {7th Workshop on Complex Networks},
	year = {2016},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Dijon, France},
	abstract = {Evaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating the adherence of a community partition to the ground truth: our methodology provides more information than the state-of-the-art ones and is fast to compute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.},
	doi = {10.1007/978-3-319-30569-1_10},
	url = {http://www.giuliorossetti.net/about/wp-content/uploads/2015/12/Complenet16.pdf},
	author = {Giulio Rossetti and Luca Pappalardo and S Rinzivillo}
}
@inbook {877,
	title = {Partition-Based Clustering Using Constraint Optimization},
	booktitle = {Data Mining and Constraint Programming - Foundations of a Cross-Disciplinary Approach},
	year = {2016},
	pages = {282{\textendash}299},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {Partition-based clustering is the task of partitioning a dataset in a number of groups of examples, such that examples in each group are similar to each other. Many criteria for what constitutes a good clustering have been identified in the literature; furthermore, the use of additional constraints to find more useful clusterings has been proposed. In this chapter, it will be shown that most of these clustering tasks can be formalized using optimization criteria and constraints. We demonstrate how a range of clustering tasks can be modelled in generic constraint programming languages with these constraints and optimization criteria. Using the constraint-based modeling approach we also relate the DBSCAN method for density-based clustering to the label propagation technique for community discovery.},
	doi = {10.1007/978-3-319-50137-6_11},
	url = {http://dx.doi.org/10.1007/978-3-319-50137-6_11},
	author = {Valerio Grossi and Tias Guns and Anna Monreale and Mirco Nanni and Siegfried Nijssen}
}
@proceedings {806,
	title = {Power Consumption Modeling and Prediction in a Hybrid CPU-GPU-MIC Supercomputer},
	volume = {LNCS 9833},
	year = {2016},
	publisher = {Springer LNCS},
	address = {Grenoble, France},
	abstract = {Power consumption is a major obstacle for High Performance Computing (HPC) systems in their quest towards the holy grail of ExaFLOP performance. Significant advances in power efficiency have to be made before this goal can be attained and accurate modeling is an essential step towards power efficiency by optimizing system operating parameters to match dynamic energy needs. In this paper we present a study of power consumption by jobs in Eurora, a hybrid CPU-GPU-MIC system installed at the largest Italian data center. Using data from a dedicated monitoring framework, we build a data-driven model of power consumption for each user in the system and use it to predict the power requirements of future jobs. We are able to achieve good prediction results for over 80 \% of the users in the system. For the remaining users, we identify possible reasons why prediction performance is not as good. Possible applications for our predictive modeling results include scheduling optimization, power-aware billing and system-scale power modeling. All the scripts used for the study have been made available on GitHub.},
	doi = {10.1007/978-3-319-43659-3_9},
	url = {http://arxiv.org/abs/1601.05961},
	author = {Alina Sirbu and Ozalp Babaoglu}
}
@conference {864,
	title = {Predicting System-level Power for a Hybrid Supercomputer},
	booktitle = {2016 International Conference on High Performance Computing Simulation (HPCS)},
	year = {2016},
	month = {07/2016},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Innsbruck, Austria},
	abstract = {For current High Performance Computing systems to scale towards the holy grail of ExaFLOP performance, their power consumption has to be reduced by at least one order of magnitude. This goal can be achieved only through a combination of hardware and software advances. Being able to model and accurately predict the power consumption of large computational systems is necessary for software-level innovations such as proactive and power-aware scheduling, resource allocation and fault tolerance techniques. In this paper we present a 2-layer model of power consumption for a hybrid supercomputer (which held the top spot of the Green500 list on July 2013) that combines CPU, GPU and MIC technologies to achieve higher energy efficiency. Our model takes as input workload information - the number and location of resources that are used by each job at a certain time - and calculates the resulting system-level power consumption. When jobs are submitted to the system, the workload configuration can be foreseen based on the scheduler policies, and our model can then be applied to predict the ensuing system-level power consumption. Additionally, alternative workload configurations can be evaluated from a power perspective and more efficient ones can be selected. Applications of the model include not only power-aware scheduling but also prediction of anomalous behavior.},
	doi = {10.1109/HPCSim.2016.7568420},
	url = {http://ieeexplore.ieee.org/document/7568420/},
	author = {Alina Sirbu and Ozalp Babaoglu}
}
@conference {876,
	title = {Privacy-Preserving Outsourcing of Data Mining},
	booktitle = {40th IEEE Annual Computer Software and Applications Conference, {COMPSAC} Workshops 2016, Atlanta, GA, USA, June 10-14, 2016},
	year = {2016},
	publisher = {IEEE Computer Society},
	organization = {IEEE Computer Society},
	address = { Atlanta, GA, USA},
	abstract = {Data mining is gaining momentum in society due to the ever increasing availability of large amounts of data, easily gathered by a variety of collection technologies and stored via computer systems. Due to the limited computational resources of data owners and the developments in cloud computing, there has been considerable recent interest in the paradigm of data mining-as-a-service (DMaaS). In this paradigm, a company (data owner) lacking in expertise or computational resources outsources its mining needs to a third party service provider (server). Given the fact that the server may not be fully trusted, one of the main concerns of the DMaaS paradigm is the protection of data privacy. In this paper, we provide an overview of a variety of techniques and approaches that address the privacy issues of the DMaaS paradigm.},
	doi = {10.1109/COMPSAC.2016.169},
	url = {http://dx.doi.org/10.1109/COMPSAC.2016.169},
	author = {Anna Monreale and Hui Wendy Wang}
}
@conference {989,
	title = {Privacy-Preserving Outsourcing of Pattern Mining of Event-Log Data-A Use-Case from Process Industry},
	booktitle = {Cloud Computing Technology and Science (CloudCom), 2016 IEEE International Conference on},
	year = {2016},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {With the advent of cloud computing and its model for IT services based on the Internet and big data centers, the interest of industries into XaaS ("Anything as a Service") paradigm is increasing. Business intelligence and knowledge discovery services are typical services that companies tend to externalize on the cloud, due to their data intensive nature and the algorithms complexity. What is appealing for a company is to rely on external expertise and infrastructure to compute the analytical results and models which are required by the business analysts for understanding the business phenomena under observation. Although it is advantageous to achieve sophisticated analysis there exist several serious privacy issues in this paradigm. In this paper we investigate through an industrial use-case the application of a framework for privacypreserving outsourcing of pattern mining on event-log data. Moreover, we present and discuss some ideas about possible extensions.},
	doi = {10.1109/CloudCom.2016.0095},
	author = {Marrella, Alessandro and Anna Monreale and Kloepper, Benjamin and Krueger, Martin W}
}
@book {879,
	title = {Realising the European open science cloud},
	year = {2016},
	abstract = {The European Open Science Cloud (EOSC) aims to accelerate and support the current transition to more effective Open Science and Open Innovation in the Digital Single Market. It should enable trusted access to services, systems and the re-use of shared scientific data across disciplinary, social and geographical borders. This report approaches the EOSC as a federated environment for scientific data sharing and re-use, based on existing and emerging elements in the Member States, with light-weight international guidance and governance, and a large degree of freedom regarding practical implementation.},
	isbn = {978-92-79-61762-1},
	doi = {10.2777/940154},
	url = {http://dx.doi.org/10.2777/940154},
	author = {Ayris, Paul and Berthou, Jean-Yves and Bruce, Rachel and Lindstaedt, Stefanie and Anna Monreale and Mons, Barend and Murayama, Yasuhiro and S{\"o}derg{\r a}rd, Caj and Tochtermann, Klaus and Wilkinson, Ross}
}
@conference {1177,
	title = {SPARQL Queries over Source Code},
	booktitle = {2016 IEEE Tenth International Conference on Semantic Computing (ICSC)},
	year = {2016},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Mattia Setzu and Atzori, Maurizio}
}
@article {897,
	title = {Special Issue on Mobile Traffic Analytics},
	journal = {Computer Communications},
	volume = {95},
	year = {2016},
	pages = {1{\textendash}2},
	abstract = {This Special Issue of Computer Communications is dedicated to mobile traffic data analysis. This is an emerging field of research that stems from the increasing pervasiveness in our lives of always-connected mobile devices. These devices continuously collect, generate, receive or communicate data; in doing so, they leave trails of digital crumbs that can be followed, recorded and analysed in many and varied ways, and for a number of different purposes.
From a data collection perspective, applications running on smartphones allow tracking user activities with extreme accuracy, in terms of mobility, context, and service usage. Yet, having individuals informedly install and run software that monitors their actions is not obvious; finding adequate incentives is equivalently complex. The other option is gathering mobile traffic data in the mobile network. This is an increasingly common practice for telecommunication operators: the collection of minimum information required for billing is giving way to in-depth inspection and recording of mobile service usages in space and time, and of traffic flows at the network edge and core. In this case, data access remains the major impediment, due to privacy and industrial secrecy reasons.

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

As a result, we witness today a rapid growth of the literature that proposes or exploits mobile traffic analytics. Included in this Special Issue are eight papers that cover a significant portion of the different research topics in this area, ranging from data collection to the characterization of land use and mobile service consumption, from the inference and prediction of user mobility to the detection of malicious traffic. These papers were selected from 30 high-quality submissions after at least two rounds of reviews by experts and guest editors. The original submissions were received from five continents and a variety of countries, including Austria, Argentina, Belgium, Brazil, Chile, China, France, Germany, Italy, South Korea, Luxembourg, Pakistan, Saudi Arabia, Spain, Sweden, Tunisia, Turkey, USA. The accepted papers reflect this geographical heterogeneity, and are authored by researchers based in Europe, North and South America.},
	doi = {10.1016/j.comcom.2016.10.009},
	url = {http://dx.doi.org/10.1016/j.comcom.2016.10.009},
	author = {Marco Fiore and Zubair Shafiq and Zbigniew Smoreda and Razvan Stanica and Roberto Trasarti}
}
@article {866,
	title = {A supervised approach for intra-/inter-community interaction prediction in dynamic social networks},
	journal = {Social Network Analysis and Mining},
	volume = {6},
	number = {1},
	year = {2016},
	month = {09/2016},
	pages = {86},
	abstract = {Due 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.},
	issn = {1869-5469},
	doi = {10.1007/s13278-016-0397-y},
	url = {http://dx.doi.org/10.1007/s13278-016-0397-y},
	author = {Giulio Rossetti and Riccardo Guidotti and Ioanna Miliou and Dino Pedreschi and Fosca Giannotti}
}
@article {846,
	title = {Towards operator-less data centers through data-driven, predictive, proactive autonomics},
	journal = {Cluster Computing},
	year = {2016},
	month = {04/2016},
	pages = {1{\textendash}14},
	abstract = {Continued reliance on human operators for managing data centers is a major impediment for them from ever reaching extreme dimensions. Large computer systems in general, and data centers in particular, will ultimately be managed using predictive computational and executable models obtained through data-science tools, and at that point, the intervention of humans will be limited to setting high-level goals and policies rather than performing low-level operations. Data-driven autonomics, where management and control are based on holistic predictive models that are built and updated using live data, opens one possible path towards limiting the role of operators in data centers. In this paper, we present a data-science study of a public Google dataset collected in a 12K-node cluster with the goal of building and evaluating predictive models for node failures. Our results support the practicality of a data-driven approach by showing the effectiveness of predictive models based on data found in typical data center logs. We use BigQuery, the big data SQL platform from the Google Cloud suite, to process massive amounts of data and generate a rich feature set characterizing node state over time. We describe how an ensemble classifier can be built out of many Random Forest classifiers each trained on these features, to predict if nodes will fail in a future 24-h window. Our evaluation reveals that if we limit false positive rates to 5 \%, we can achieve true positive rates between 27 and 88 \% with precision varying between 50 and 72 \%. This level of performance allows us to recover large fraction of jobs{\textquoteright} executions (by redirecting them to other nodes when a failure of the present node is predicted) that would otherwise have been wasted due to failures. We discuss the feasibility of including our predictive model as the central component of a data-driven autonomic manager and operating it on-line with live data streams (rather than off-line on data logs). All of the scripts used for BigQuery and classification analyses are publicly available on GitHub.},
	doi = {DOI:10.1007/s10586-016-0564-y},
	url = {http://link.springer.com/article/10.1007/s10586-016-0564-y},
	author = {Alina Sirbu and Ozalp Babaoglu}
}
@inbook {964,
	title = {Understanding human mobility with big data},
	booktitle = {Solving Large Scale Learning Tasks. Challenges and Algorithms},
	year = {2016},
	pages = {208{\textendash}220},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {The 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.},
	doi = {10.1007/978-3-319-41706-6_10},
	author = {Fosca Giannotti and Lorenzo Gabrielli and Dino Pedreschi and S Rinzivillo}
}
@article {867,
	title = {Unveiling mobility complexity through complex network analysis},
	journal = {Social Network Analysis and Mining},
	volume = {6},
	number = {1},
	year = {2016},
	pages = {59},
	abstract = {The 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.},
	doi = {10.1007/s13278-016-0369-2},
	author = {Riccardo Guidotti and Anna Monreale and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@conference {857,
	title = {Unveiling Political Opinion Structures with a Web-experiment},
	booktitle = {Proceedings of the 1st International Conference on Complex Information Systems},
	year = {2016},
	abstract = {The dynamics of political votes has been widely studied, both for its practical interest and as a paradigm of the dynamics of mass opinions and collective phenomena, where theoretical predictions can be easily tested. However, the vote outcome is often influenced by many factors beyond the bare opinion on the candidate, and in most cases it is bound to a single preference. The voter perception of the political space is still to be elucidated. We here propose a web experiment (laPENSOcos{\textquoteleft}{\i}) where we explicitly investigate participants{\textquoteright} opinions on political entities (parties, coalitions, individual candidates) of the Italian political scene. As a main result, we show that the political perception follows a Weber-Fechner-like law, i.e., when ranking political entities according to the user expressed preferences, the perceived distance of the user from a given entity scales as the logarithm of this rank.},
	isbn = {978-989-758-181-6},
	doi = {10.5220/0005906300390047},
	url = {http://www.scitepress.org/DigitalLibrary/Link.aspx?doi=10.5220/0005906300390047},
	author = {Pietro Gravino and Saverio Caminiti and Alina Sirbu and Francesca Tria and Vito D P Servedio and Vittorio Loreto}
}
@inbook {818,
	title = {Where Is My Next Friend? Recommending Enjoyable Profiles in Location Based Services},
	booktitle = {Complex Networks VII},
	year = {2016},
	pages = {65{\textendash}78},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {How many of your friends, with whom you enjoy spending some time, live close by? How many people are at your reach, with whom you could have a nice conversation? We introduce a measure of enjoyability that may be the basis for a new class of location-based services aimed at maximizing the likelihood that two persons, or a group of people, would enjoy spending time together. Our enjoyability takes into account both topic similarity between two users and the users{\textquoteright} tendency to connect to people with similar or dissimilar interest. We computed the enjoyability on two datasets of geo-located tweets, and we reasoned on the applicability of the obtained results for producing friend recommendations. We aim at suggesting couples of users which are not friends yet, but which are frequently co-located and maximize our enjoyability measure. By taking into account the spatial dimension, we show how 50 \% of users may find at least one enjoyable person within 10 km of their two most visited locations. Our results are encouraging, and open the way for a new class of recommender systems based on enjoyability.},
	doi = {10.1007/978-3-319-30569-1_5},
	author = {Riccardo Guidotti and Michele Berlingerio}
}
@conference {763,
	title = {Behavioral Entropy and Profitability in Retail},
	booktitle = {IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA{\textquoteright}2015)},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris},
	abstract = {Human 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{\textquoteright}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.},
	author = {Riccardo Guidotti and Michele Coscia and Dino Pedreschi and Diego Pennacchioli}
}
@article {801,
	title = {A Big Data Analyzer for Large Trace Logs},
	journal = {Computing},
	year = {2015},
	doi = {10.1007/s00607-015-0480-7},
	url = {http://link.springer.com/article/10.1007/s00607-015-0480-7},
	author = {Balliu, Alkida and Olivetti, Dennis and Ozalp Babaoglu and Marzolla, Moreno and Alina Sirbu}
}
@conference {756,
	title = {City users{\textquoteright} classification with mobile phone data},
	booktitle = {IEEE Big Data},
	year = {2015},
	month = {11/2015},
	address = {Santa Clara (CA) - USA},
	abstract = {Nowadays mobile phone data are an actual proxy for studying the users{\textquoteright} 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.},
	author = {Lorenzo Gabrielli and Barbara Furletti and Roberto Trasarti and Fosca Giannotti and Dino Pedreschi}
}
@conference {878,
	title = {Clustering Formulation Using Constraint Optimization},
	booktitle = {Software Engineering and Formal Methods - {SEFM} 2015 Collocated Workshops: ATSE, HOFM, MoKMaSD, and VERY*SCART, York, UK, September 7-8, 2015, Revised Selected Papers},
	year = {2015},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	abstract = {The 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.},
	doi = {10.1007/978-3-662-49224-6_9},
	url = {http://dx.doi.org/10.1007/978-3-662-49224-6_9},
	author = {Valerio Grossi and Anna Monreale and Mirco Nanni and Dino Pedreschi and Franco Turini}
}
@conference {774,
	title = {ComeWithMe: An Activity-Oriented Carpooling Approach},
	booktitle = {2015 {IEEE} 18th International Conference on Intelligent Transportation Systems},
	year = {2015},
	month = {09/2015},
	publisher = {Institute of Electrical {\&} Electronics Engineers ({IEEE})},
	organization = {Institute of Electrical {\&} Electronics Engineers ({IEEE})},
	abstract = {The interest in carpooling is increasing due to the need to reduce traffic and noise pollution. Most of the available approaches and systems are route oriented, where driver and passengers are matched when the destination location is the same. ComeWithMe offers a new perspective: the destination is the intended activity instead of a location. This novel matching method is aimed to boost the possibilities of rides if passenger reaches a different location maintaining the activity. We conducted experiments using a real data set of trajectories and our results showed that the proposed matching algorithm improved the traditional carpooling approach in more than 80\%.},
	doi = {10.1109/itsc.2015.414},
	url = {http://dx.doi.org/10.1109/itsc.2015.414},
	author = {Vinicius Monteiro de Lira and Val{\'e}ria Ces{\'a}rio Times and Chiara Renso and S Rinzivillo}
}
@conference {819,
	title = {Community-centric analysis of user engagement in Skype social network},
	booktitle = {International conference on Advances in Social Network Analysis and Mining},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris, France},
	isbn = {978-1-4503-3854-7},
	doi = {10.1145/2808797.2809384},
	url = {http://dl.acm.org/citation.cfm?doid=2808797.2809384},
	author = {Giulio Rossetti and Luca Pappalardo and Riivo Kikas and Dino Pedreschi and Fosca Giannotti and Marlon Dumas}
}
@article {804,
	title = {Data Integration for Microarrays: Enhanced Inference for Gene Regulatory Networks},
	journal = {Microarrays},
	volume = {4},
	number = {2},
	year = {2015},
	pages = {255{\textendash}269},
	doi = {10.3390/microarrays4020255},
	url = {http://www.mdpi.com/2076-3905/4/2/255},
	author = {Alina Sirbu and Martin Crane and Heather J Ruskin}
}
@conference {689,
	title = {Detecting and understanding big events in big cities},
	booktitle = {NetMob},
	year = {2015},
	month = {04/2015},
	address = {Boston},
	abstract = {Recent studies have shown the great potential of big data such as mobile phone location data to model human behavior. Big data allow to analyze people presence in a territory in a fast and effective way with respect to the classical surveys (diaries or questionnaires). One of the drawbacks of these collection systems is incompleteness of the users{\textquoteright} traces; people are localized only when they are using their phones. In this work we define a data mining method for identifying people presence and understanding the impact of big events in big cities. We exploit the ability of the Sociometer for classifying mobile phone users in mobility categories through their presence profile. The experiment in cooperation with Orange Telecom has been conduced in Paris during the event F^ete de la Musique using a
privacy preserving protocol.},
	url = {http://www.netmob.org/assets/img/netmob15_book_of_abstracts_posters.pdf},
	author = {Barbara Furletti and Lorenzo Gabrielli and Roberto Trasarti and Zbigniew Smoreda and Maarten Vanhoof and Cezary Ziemlicki}
}
@article {759,
	title = {Discrimination- and privacy-aware patterns},
	journal = {Data Min. Knowl. Discov.},
	volume = {29},
	number = {6},
	year = {2015},
	pages = {1733{\textendash}1782},
	abstract = {Data 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. },
	doi = {10.1007/s10618-014-0393-7},
	url = {http://dx.doi.org/10.1007/s10618-014-0393-7},
	author = {Sara Hajian and Josep Domingo-Ferrer and Anna Monreale and Dino Pedreschi and Fosca Giannotti}
}
@article {805,
	title = {Egalitarianism in the rank aggregation problem: a new dimension for democracy},
	journal = {Quality \& Quantity},
	year = {2015},
	pages = {1{\textendash}16},
	doi = {10.1007/s11135-015-0197-x},
	url = {http://link.springer.com/article/10.1007/s11135-015-0197-x},
	author = {Contucci, Pierluigi and Panizzi, Emanuele and Ricci-Tersenghi, Federico and Alina Sirbu}
}
@booklet {974,
	title = {An exploration of learning processes as process maps in FLOSS repositories},
	year = {2015},
	abstract = {Evidence suggests that Free/Libre Open Source Software (FLOSS) environ-ments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while mak-ing use of the tools available in these environments. A number of studies docu-ment the existence of learning processes in FLOSS through the analysis of sur-veys and questionnaires filled by FLOSS project participants. At the same time, the interest in understanding the dynamics of the FLOSS phenomenon, its popu-larity and success resulted in the development of tools and techniques for extract-ing and analyzing data from different FLOSS data sources. This new field is called Mining Software Repositories (MSR). In spite of these efforts, there is limited work aiming to provide empirical evidence of learning processes directly from FLOSS repositories. In this paper, we seek to trigger such an initiative by proposing an approach based on Process Mining to trace learning behaviors from FLOSS participants{\textquoteright} trails of activities, as recorded in FLOSS repositories, and visualize them as pro-cess maps. Process maps provide a pictorial representation of real behavior as it is recorded in FLOSS data. Our aim is to provide critical evidence that boosts the understanding of learning behavior in FLOSS communities by analyzing the rel-evant repositories. In order to accomplish this, we propose an effective approach that comprises first the mining of FLOSS repositories in order to generate Event logs, and then the generation of process maps, equipped with relevant statistical data interpreting and indicating the value of process discovery from these reposi-tories.},
	url = {http://eprints.adm.unipi.it/id/eprint/2344},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@conference {760,
	title = {Find Your Way Back: Mobility Profile Mining with Constraints},
	booktitle = {Principles and Practice of Constraint Programming},
	year = {2015},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cork},
	abstract = {Mobility profile mining is a data mining task that can be formulated as clustering over movement trajectory data. The main challenge is to separate the signal from the noise, i.e. one-off trips. We show that standard data mining approaches suffer the important drawback that they cannot take the symmetry of non-noise trajectories into account. That is, if a trajectory has a symmetric equivalent that covers the same trip in the reverse direction, it should become more likely that neither of them is labelled as noise. We present a constraint model that takes this knowledge into account to produce better clusters. We show the efficacy of our approach on real-world data that was previously processed using standard data mining techniques.},
	author = {Lars Kotthoff and Mirco Nanni and Riccardo Guidotti and Barry O{\textquoteright}Sullivan}
}
@proceedings {770,
	title = {The harsh rule of the goals: data-driven performance indicators for football teams},
	year = {2015},
	abstract = {{\textemdash}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{\textquoteright} view on football data has the potential of
revealing hidden patterns and behavior of superior quality.},
	url = {https://www.researchgate.net/profile/Luca_Pappalardo/publication/281318318_The_harsh_rule_of_the_goals_data-driven_performance_indicators_for_football_teams/links/561668e308ae37cfe4090a5d.pdf},
	author = {Paolo Cintia and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti and Marco Malvaldi}
}
@conference {802,
	title = {A Holistic Approach to Log Data Analysis in High-Performance Computing Systems: The Case of IBM Blue Gene/Q},
	booktitle = {Euro-Par 2015: parallel Processing Workshops, LNCS 9523},
	year = {2015},
	publisher = {Springer},
	organization = {Springer},
	doi = {10.1007/978-3-319-27308-2_51},
	url = {http://link.springer.com/chapter/10.1007\%2F978-3-319-27308-2_51},
	author = {Alina Sirbu and Ozalp Babaoglu}
}
@conference {816,
	title = {Interaction Prediction in Dynamic Networks exploiting Community Discovery},
	booktitle = {International conference on Advances in Social Network Analysis and Mining, ASONAM 2015},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris, France},
	abstract = {Due 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.},
	isbn = {978-1-4503-3854-7},
	doi = {0.1145/2808797.2809401},
	url = {http://dl.acm.org/citation.cfm?doid=2808797.2809401},
	author = {Giulio Rossetti and Riccardo Guidotti and Diego Pennacchioli and Dino Pedreschi and Fosca Giannotti}
}
@article {980,
	title = {Introduction to the special issue on Artificial Intelligence for Society and Economy},
	journal = {Intelligenza Artificiale},
	volume = {9},
	number = {1},
	year = {2015},
	pages = {23{\textendash}23},
	doi = {10.3233/IA-150074},
	author = {Salvatore Ruggieri}
}
@proceedings {825,
	title = {ItEM: A Vector Space Model to Bootstrap an Italian Emotive Lexicon},
	volume = {II},
	year = {2015},
	abstract = {In  recent  years  computational  linguistics  has  seen  a  rising  interest  in subjectivity,  opinions,  feelings and  emotions.  Even  though  great attention  has been given to polarity recognition, the research in emotion detection has had to rely on small emotion resources. In this paper,  we  present  a  methodology  to  build emotive   lexicons   by  jointly   exploiting vector  space  models  and  human  annotation,  and  we  provide  the  first  results  of the  evaluation  with  a  crowdsourcing  experiment.},
	isbn = {978-88-99200-62-6},
	author = {Lucia Passaro and Pollacci, Laura and Lenci, Alessandro}
}
@conference {973,
	title = {The layered structure of company share networks},
	booktitle = {Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE International Conference on},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {We present a framework for the analysis of corporate governance problems using network science and graph algorithms on ownership networks. In such networks, nodes model companies/shareholders and edges model shares owned. Inspired by the widespread pyramidal organization of corporate groups of companies, we model ownership networks as layered graphs, and exploit the layered structure to design feasible and efficient solutions to three key problems of corporate governance. The first one is the long-standing problem of computing direct and indirect ownership (integrated ownership problem). The other two problems are introduced here: computing direct and indirect dividends (dividend problem), and computing the group of companies controlled by a parent shareholder (corporate group problem). We conduct an extensive empirical analysis of the Italian ownership network, which, with its 3.9M nodes, is 30{\texttimes} the largest network studied so far.},
	doi = {10.1109/DSAA.2015.7344809},
	author = {Andrea Romei and Salvatore Ruggieri and Franco Turini}
}
@conference {761,
	title = {Managing travels with PETRA: The Rome use case},
	booktitle = {2015 31st IEEE International Conference on Data Engineering Workshops (ICDEW)},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {The aim of the PETRA project is to provide the basis for a city-wide transportation system that supports policies catering for both individual preferences of users and city-wide travel patterns. The PETRA platform will be initially deployed in the partner city of Rome, and later in Venice, and Tel-Aviv.},
	author = {Botea, Adi and Braghin, Stefano and Lopes, Nuno and Riccardo Guidotti and Francesca Pratesi}
}
@conference {975,
	title = {Mining learning processes from FLOSS mailing archives},
	booktitle = {Conference on e-Business, e-Services and e-Society},
	year = {2015},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Evidence suggests that Free/Libre Open Source Software (FLOSS) environments provide unlimited learning opportunities. Community members engage in a number of activities both during their interaction with their peers and while making use of these environments. As FLOSS repositories store data about participants{\textquoteright} interaction and activities, we analyze participants{\textquoteright} interaction and knowledge exchange in emails to trace learning activities that occur in distinct phases of the learning process. We make use of semantic search in SQL to retrieve data and build corresponding event logs which are then fed to a process mining tool in order to produce visual workflow nets. We view these nets as representative of the traces of learning activities in FLOSS as well as their relevant flow of occurrence. Additional statistical details are provided to contextualize and describe these models.},
	doi = {10.1007/978-3-319-25013-7_23},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@conference {764,
	title = {Mobility Mining for Journey Planning in Rome},
	booktitle = {Machine Learning and Knowledge Discovery in Databases},
	year = {2015},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {We present recent results on integrating private car GPS routines obtained by a Data Mining module. into the PETRA (PErsonal TRansport Advisor) platform. The routines are used as additional {\textquotedblleft}bus lines{\textquotedblright}, available to provide a ride to travelers. We present the effects of querying the planner with and without the routines, which show how Data Mining may help Smarter Cities applications.},
	author = {Michele Berlingerio and Bicer, Veli and Botea, Adi and Braghin, Stefano and Lopes, Nuno and Riccardo Guidotti and Francesca Pratesi}
}
@article {794,
	title = {Participatory Patterns in an International Air Quality Monitoring Initiative.},
	journal = {PLoS One},
	volume = {10},
	year = {2015},
	month = {2015},
	pages = {e0136763},
	abstract = {<p>The issue of sustainability is at the top of the political and societal agenda, being considered of extreme importance and urgency. Human individual action impacts the environment both locally (e.g., local air/water quality, noise disturbance) and globally (e.g., climate change, resource use). Urban environments represent a crucial example, with an increasing realization that the most effective way of producing a change is involving the citizens themselves in monitoring campaigns (a citizen science bottom-up approach). This is possible by developing novel technologies and IT infrastructures enabling large citizen participation. Here, in the wider framework of one of the first such projects, we show results from an international competition where citizens were involved in mobile air pollution monitoring using low cost sensing devices, combined with a web-based game to monitor perceived levels of pollution. Measures of shift in perceptions over the course of the campaign are provided, together with insights into participatory patterns emerging from this study. Interesting effects related to inertia and to direct involvement in measurement activities rather than indirect information exposure are also highlighted, indicating that direct involvement can enhance learning and environmental awareness. In the future, this could result in better adoption of policies towards decreasing pollution.</p>},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0136763},
	author = {Alina Sirbu and Becker, Martin and Saverio Caminiti and De Baets, Bernard and Elen, Bart and Francis, Louise and Pietro Gravino and Hotho, Andreas and Ingarra, Stefano and Vittorio Loreto and Molino, Andrea and Mueller, Juergen and Peters, Jan and Ricchiuti, Ferdinando and Saracino, Fabio and Vito D P Servedio and Stumme, Gerd and Theunis, Jan and Francesca Tria and Van den Bossche, Joris}
}
@article {752,
	title = {Product assortment and customer mobility},
	journal = {EPJ Data Science},
	volume = {4},
	number = {1},
	year = {2015},
	month = {10-2015},
	pages = {1{\textendash}18},
	abstract = {Customers mobility is dependent on the sophistication of their needs: sophisticated customers need to travel more to fulfill their needs. In this paper, we provide more detailed evidence of this phenomenon, providing an empirical validation of the Central Place Theory. For each customer, we detect what is her favorite shop, where she purchases most products. We can study the relationship between the favorite shop and the closest one, by recording the influence of the shop{\textquoteright}s size and the customer{\textquoteright}s sophistication in the discordance cases, i.e. the cases in which the favorite shop is not the closest one. We show that larger shops are able to retain most of their closest customers and they are able to catch large portions of customers from smaller shops around them. We connect this observation with the shop{\textquoteright}s larger sophistication, and not with its other characteristics, as the phenomenon is especially noticeable when customers want to satisfy their sophisticated needs. This is a confirmation of the recent extensions of the Central Place Theory, where the original assumptions of homogeneity in customer purchase power and needs are challenged. Different types of shops have also different survival logics. The largest shops get closed if they are unable to catch customers from the smaller shops, while medium size shops get closed if they cannot retain their closest customers. All analysis are performed on a large real-world dataset recording all purchases from millions of customers across the west coast of Italy.},
	doi = {10.1140/epjds/s13688-015-0051-3},
	url = {http://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-015-0051-3},
	author = {Michele Coscia and Diego Pennacchioli and Fosca Giannotti}
}
@conference {820,
	title = {Quantification in Social Networks},
	booktitle = {International Conference on Data Science and Advanced Analytics (IEEE DSAA{\textquoteright}2015)},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Paris, France},
	abstract = {In 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. },
	doi = {10.1109/DSAA.2015.7344845},
	url = {http://www.giuliorossetti.net/about/wp-content/uploads/2015/12/main_DSAA.pdf},
	author = {Letizia Milli and Anna Monreale and Giulio Rossetti and Dino Pedreschi and Fosca Giannotti and Fabrizio Sebastiani}
}
@article {723,
	title = {Returners and explorers dichotomy in human mobility},
	journal = {Nat Commun},
	volume = {6},
	year = {2015},
	month = {09},
	abstract = {The 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.},
	url = {http://dx.doi.org/10.1038/ncomms9166},
	author = {Luca Pappalardo and Filippo Simini and S Rinzivillo and Dino Pedreschi and Fosca Giannotti and Barabasi, Albert-Laszlo}
}
@article {990,
	title = {A risk model for privacy in trajectory data},
	journal = {Journal of Trust Management},
	volume = {2},
	number = {1},
	year = {2015},
	pages = {9},
	abstract = {Time 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.},
	doi = {10.1186/s40493-015-0020-6},
	author = {Anirban Basu and Anna Monreale and Roberto Trasarti and Juan Camilo Corena and Fosca Giannotti and Dino Pedreschi and Shinsaku Kiyomoto and Yutaka Miyake and Tadashi Yanagihara}
}
@conference {981,
	title = {Segregation Discovery in a Social Network of Companies},
	booktitle = {International Symposium on Intelligent Data Analysis},
	year = {2015},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {We introduce a framework for a data-driven analysis of segregation of minority groups in social networks, and challenge it on a complex scenario. The framework builds on quantitative measures of segregation, called segregation indexes, proposed in the social science literature. The segregation discovery problem consists of searching sub-graphs and sub-groups for which a reference segregation index is above a minimum threshold. A search algorithm is devised that solves the segregation problem. The framework is challenged on the analysis of segregation of social groups in the boards of directors of the real and large network of Italian companies connected through shared directors.},
	doi = {10.1007/978-3-319-24465-5_4},
	author = {Alessandro Baroni and Salvatore Ruggieri}
}
@article {724,
	title = {Small Area Model-Based Estimators Using Big Data Sources},
	journal = {Journal of Official Statistics},
	volume = {31},
	number = {2},
	year = {2015},
	pages = {263{\textendash}281},
	author = {Stefano Marchetti and Caterina Giusti and Monica Pratesi and Nicola Salvati and Fosca Giannotti and Dino Pedreschi and S Rinzivillo and Luca Pappalardo and Lorenzo Gabrielli}
}
@conference {823,
	title = {Social or green? A data-driven approach for more enjoyable carpooling},
	booktitle = {Intelligent Transportation Systems (ITSC), 2015 IEEE 18th International Conference on},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Riccardo Guidotti and Sassi, Andrea and Michele Berlingerio and Pascale, Alessandra and Ghaddar, Bissan}
}
@conference {898,
	title = {{TOSCA:} two-steps clustering algorithm for personal locations detection},
	booktitle = {Proceedings of the 23rd {SIGSPATIAL} International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA, November 3-6, 2015},
	year = {2015},
	doi = {10.1145/2820783.2820818},
	url = {http://doi.acm.org/10.1145/2820783.2820818},
	author = {Riccardo Guidotti and Roberto Trasarti and Mirco Nanni}
}
@inbook {824,
	title = {Towards a Boosted Route Planner Using Individual Mobility Models},
	booktitle = {Software Engineering and Formal Methods},
	year = {2015},
	pages = {108{\textendash}123},
	publisher = {Springer Berlin Heidelberg},
	organization = {Springer Berlin Heidelberg},
	author = {Riccardo Guidotti and Paolo Cintia}
}
@conference {803,
	title = {Towards Data-Driven Autonomics in Data Centers},
	booktitle = {IEEE International Conference on Cloud and Autonomic Computing},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	doi = {DOI:10.1109/ICCAC.2015.19},
	url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7312140\&filter\%3DAND\%28p_IS_Number\%3A7312127\%29},
	author = {Alina Sirbu and Ozalp Babaoglu}
}
@conference {899,
	title = {Towards user-centric data management: individual mobility analytics for collective services},
	booktitle = {Proceedings of the 4th {ACM} {SIGSPATIAL} International Workshop on Mobile Geographic Information Systems, MobiGIS 2015, Bellevue, WA, USA, November 3-6, 2015},
	year = {2015},
	doi = {10.1145/2834126.2834132},
	url = {http://doi.acm.org/10.1145/2834126.2834132},
	author = {Riccardo Guidotti and Roberto Trasarti and Mirco Nanni and Fosca Giannotti}
}
@inbook {777,
	title = {Use of Mobile Phone Data to Estimate Visitors Mobility Flows},
	booktitle = {Software Engineering and Formal Methods},
	volume = {8938},
	number = {Lecture Notes in Computer Science},
	year = {2015},
	pages = {214-226},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {Big Data originating from the digital breadcrumbs of human activities, sensed as by-product of the technologies that we use for our daily activities, allows us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data {\textquotedblleft}proxies{\textquotedblright}, such as the mobile calls data for mobility. In this paper we investigate to what extent data coming from mobile operators could be a support in producing reliable and timely estimates of intra-city mobility flows. The idea is to define an estimation method based on calling data to characterize the mobility habits of visitors at the level of a single municipality.},
	issn = {978-3-319-15200-4},
	doi = {10.1007/978-3-319-15201-1_14},
	url = {http://link.springer.com/chapter/10.1007\%2F978-3-319-15201-1_14},
	author = {Lorenzo Gabrielli and Barbara Furletti and Fosca Giannotti and Mirco Nanni and S Rinzivillo}
}
@conference {978,
	title = {An abstract state machine (ASM) representation of learning process in FLOSS communities},
	booktitle = {International Conference on Software Engineering and Formal Methods},
	year = {2014},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Free/Libre Open Source Software (FLOSS) communities as collaborative environments enable the occurrence of learning between participants in these groups. With the increasing interest research on understanding the mechanisms and processes through which learning occurs in FLOSS, there is an imperative to describe these processes. One successful way of doing this is through specification methods. In this paper, we describe the adoption of Abstract States Machines (ASMs) as a specification methodology for the description of learning processes in FLOSS. The goal of this endeavor is to represent the many possible steps and/or activities FLOSS participants go through during interactions that can be categorized as learning processes. Through ASMs, we express learning phases as states while activities that take place before moving from one state to another are expressed as transitions.},
	doi = {10.1007/978-3-319-15201-1_15},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@article {564,
	title = {Anonymity preserving sequential pattern mining},
	journal = {Artif. Intell. Law},
	volume = {22},
	number = {2},
	year = {2014},
	pages = {141{\textendash}173},
	abstract = {The 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.},
	doi = {10.1007/s10506-014-9154-6},
	url = {http://dx.doi.org/10.1007/s10506-014-9154-6},
	author = {Anna Monreale and Dino Pedreschi and Ruggero G. Pensa and Fabio Pinelli}
}
@conference {983,
	title = {Anti-discrimination analysis using privacy attack strategies},
	booktitle = {Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
	year = {2014},
	publisher = {Springer, Berlin, Heidelberg},
	organization = {Springer, Berlin, Heidelberg},
	abstract = {Social discrimination discovery from data is an important task to identify illegal and unethical discriminatory patterns towards protected-by-law groups, e.g., ethnic minorities. We deploy privacy attack strategies as tools for discrimination discovery under hard assumptions which have rarely tackled in the literature: indirect discrimination discovery, privacy-aware discrimination discovery, and discrimination data recovery. The intuition comes from the intriguing parallel between the role of the anti-discrimination authority in the three scenarios above and the role of an attacker in private data publishing. We design strategies and algorithms inspired/based on Fr{\`e}chet bounds attacks, attribute inference attacks, and minimality attacks to the purpose of unveiling hidden discriminatory practices. Experimental results show that they can be effective tools in the hands of anti-discrimination authorities.},
	doi = {10.1007/978-3-662-44851-9_44},
	author = {Salvatore Ruggieri and Sara Hajian and Kamiran, Faisal and Zhang, Xiangliang}
}
@conference {807,
	title = {BiDAl: Big Data Analyzer for Cluster Traces},
	booktitle = {Informatika (BigSys workshop)},
	year = {2014},
	publisher = {GI-Edition Lecture Notes in Informatics},
	organization = {GI-Edition Lecture Notes in Informatics},
	url = {http://arxiv.org/abs/1410.1309},
	author = {Balliu, Alkida and Olivetti, Dennis and Ozalp Babaoglu and Marzolla, Moreno and Alina Sirbu}
}
@conference {574,
	title = {Big data analytics for smart mobility: a case study},
	booktitle = {EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD)},
	year = {2014},
	month = {03/2014},
	address = {Athens, Greece},
	url = {http://ceur-ws.org/Vol-1133/paper-57.pdf},
	author = {Barbara Furletti and Roberto Trasarti and Lorenzo Gabrielli and Mirco Nanni and Dino Pedreschi}
}
@conference {637,
	title = {CF-inspired Privacy-Preserving Prediction of Next Location in the Cloud},
	booktitle = {Cloud Computing Technology and Science (CloudCom), 2014 IEEE 6th International Conference on},
	year = {2014},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {Mobility 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. },
	doi = {10.1109/CloudCom.2014.114},
	url = {http://dx.doi.org/10.1109/CloudCom.2014.114},
	author = {Anirban Basu and Juan Camilo Corena and Anna Monreale and Dino Pedreschi and Fosca Giannotti and Shinsaku Kiyomoto and Vaidya, Jaideep and Yutaka Miyake}
}
@proceedings {793,
	title = {The CoLing Lab system for Sentiment Polarity Classification of tweets},
	volume = {II},
	year = {2014},
	author = {Lucia Passaro and Lebani, Gianluca E and Pollacci, Laura and Chersoni, Emmanuele and Lenci, Alessandro}
}
@article {988,
	title = {On the complexity of quantified linear systems},
	journal = {Theoretical Computer Science},
	volume = {518},
	year = {2014},
	pages = {128{\textendash}134},
	abstract = {In this paper, we explore the computational complexity of the conjunctive fragment of the first-order theory of linear arithmetic. Quantified propositional formulas of linear inequalities with (k-1) quantifier alternations are log-space complete in ΣkP or ΠkP depending on the initial quantifier. We show that when we restrict ourselves to quantified conjunctions of linear inequalities, i.e., quantified linear systems, the complexity classes collapse to polynomial time. In other words, the presence of universal quantifiers does not alter the complexity of the linear programming problem, which is known to be in P. Our result reinforces the importance of sentence formats from the perspective of computational complexity.},
	doi = {10.1016/j.tcs.2013.08.001},
	author = {Salvatore Ruggieri and Eirinakis, Pavlos and Subramani, K and Wojciechowski, Piotr}
}
@article {987,
	title = {Decision tree building on multi-core using FastFlow},
	journal = {Concurrency and Computation: Practice and Experience},
	volume = {26},
	number = {3},
	year = {2014},
	pages = {800{\textendash}820},
	abstract = {The whole computer hardware industry embraced the multi-core. The extreme optimisation of sequential algorithms is then no longer sufficient to squeeze the real machine power, which can be only exploited via thread-level parallelism. Decision tree algorithms exhibit natural concurrency that makes them suitable to be parallelised. This paper presents an in-depth study of the parallelisation of an implementation of the C4.5 algorithm for multi-core architectures. We characterise elapsed time lower bounds for the forms of parallelisations adopted and achieve close to optimal performance. Our implementation is based on the FastFlow parallel programming environment, and it requires minimal changes to the original sequential code. Copyright {\textcopyright} 2013 John Wiley \& Sons, Ltd.},
	doi = {10.1002/cpe.3063},
	author = {Aldinucci, Marco and Salvatore Ruggieri and Torquati, Massimo}
}
@article {Trasarti2014,
	title = {Discovering urban and country dynamics from mobile phone data with spatial correlation patterns},
	journal = {Telecommunications Policy},
	year = {2014},
	pages = {-},
	abstract = {Abstract Mobile communication technologies pervade our society and existing wireless networks are able to sense the movement of people, generating large volumes of data related to human activities, such as mobile phone call records. At the present, this kind of data is collected and stored by telecom operators infrastructures mainly for billing reasons, yet it represents a major source of information in the study of human mobility. In this paper, we propose an analytical process aimed at extracting interconnections between different areas of the city that emerge from highly correlated temporal variations of population local densities. To accomplish this objective, we propose a process based on two analytical tools: (i) a method to estimate the presence of people in different geographical areas; and (ii) a method to extract time- and space-constrained sequential patterns capable to capture correlations among geographical areas in terms of significant co-variations of the estimated presence. The methods are presented and combined in order to deal with two real scenarios of different spatial scale: the Paris Region and the whole France.},
	keywords = {Urban dynamics},
	issn = {0308-5961},
	doi = {http://dx.doi.org/10.1016/j.telpol.2013.12.002},
	url = {http://www.sciencedirect.com/science/article/pii/S0308596113002012},
	author = {Roberto Trasarti and Ana-Maria Olteanu-Raimond and Mirco Nanni and Thomas Couronn{\'e} and Barbara Furletti and Fosca Giannotti and Zbigniew Smoreda and Cezary Ziemlicki}
}
@inbook {808,
	title = {EGIA{\textendash}Evolutionary Optimisation of Gene Regulatory Networks, an Integrative Approach},
	booktitle = {Complex Networks V},
	year = {2014},
	pages = {217{\textendash}229},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	doi = {10.1007/978-3-319-05401-8_21},
	url = {http://link.springer.com/chapter/10.1007/978-3-319-05401-8_21},
	author = {Alina Sirbu and Martin Crane and Heather J Ruskin}
}
@conference {566,
	title = {Fair pattern discovery},
	booktitle = {Symposium on Applied Computing, {SAC} 2014, Gyeongju, Republic of Korea - March 24 - 28, 2014},
	year = {2014},
	pages = {113{\textendash}120},
	abstract = {Data 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.},
	doi = {10.1145/2554850.2555043},
	url = {http://doi.acm.org/10.1145/2554850.2555043},
	author = {Sara Hajian and Anna Monreale and Dino Pedreschi and Josep Domingo-Ferrer and Fosca Giannotti}
}
@article {986,
	title = {Introduction to special issue on computational methods for enforcing privacy and fairness in the knowledge society},
	journal = {Artificial Intelligence and Law},
	volume = {22},
	number = {2},
	year = {2014},
	pages = {109{\textendash}111},
	doi = {10.1007/s10506-014-9153-7},
	author = {Sergio Mascetti and Ricci, Annarita and Salvatore Ruggieri}
}
@conference {ideas/LiraRRTT14,
	title = {Investigating semantic regularity of human mobility lifestyle},
	booktitle = {18th International Database Engineering {\&} Applications Symposium, {IDEAS} 2014, Porto, Portugal, July 7-9, 2014},
	year = {2014},
	pages = {314{\textendash}317},
	publisher = {ACM},
	organization = {ACM},
	address = {Porto, Portugal},
	doi = {10.1145/2628194.2628226},
	url = {http://doi.acm.org/10.1145/2628194.2628226},
	author = {Vinicius Monteiro de Lira and S Rinzivillo and Chiara Renso and Val{\'e}ria Ces{\'a}rio Times and Patr{\'{\i}}cia C. A. R. Tedesco}
}
@conference {icwe/LiraRTR14,
	title = {{MAPMOLTY:} {A} Web Tool for Discovering Place Loyalty Based on Mobile Crowdsource Data},
	booktitle = {Web Engineering, 14th International Conference, {ICWE} 2014, Toulouse, France, July 1-4, 2014. Proceedings},
	year = {2014},
	pages = {528{\textendash}531},
	doi = {10.1007/978-3-319-08245-5_43},
	url = {http://dx.doi.org/10.1007/978-3-319-08245-5_43},
	author = {Vinicius Monteiro de Lira and S Rinzivillo and Val{\'e}ria Ces{\'a}rio Times and Chiara Renso}
}
@conference {727,
	title = {Mining efficient training patterns of non-professional cyclists},
	booktitle = {22nd Italian Symposium on Advanced Database Systems, {SEBD} 2014, Sorrento Coast, Italy, June 16-18, 2014.},
	year = {2014},
	author = {Paolo Cintia and Luca Pappalardo and Dino Pedreschi}
}
@inbook {575,
	title = {Mobility Profiling},
	booktitle = {Data Science and Simulation in Transportation Research},
	year = {2014},
	pages = {1-29},
	publisher = {IGI Global},
	organization = {IGI Global},
	chapter = {1},
	abstract = {The ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network. },
	doi = {10.4018/978-1-4666-4920-0.ch001},
	author = {Mirco Nanni and Roberto Trasarti and Paolo Cintia and Barbara Furletti and Chiara Renso and Lorenzo Gabrielli and S Rinzivillo and Fosca Giannotti}
}
@article {982,
	title = {A multidisciplinary survey on discrimination analysis},
	journal = {The Knowledge Engineering Review},
	volume = {29},
	number = {5},
	year = {2014},
	pages = {582{\textendash}638},
	abstract = {The collection and analysis of observational and experimental data represent the main tools for assessing the presence, the extent, the nature, and the trend of discrimination phenomena. Data analysis techniques have been proposed in the last 50 years in the economic, legal, statistical, and, recently, in the data mining literature. This is not surprising, since discrimination analysis is a multidisciplinary problem, involving sociological causes, legal argumentations, economic models, statistical techniques, and computational issues. The objective of this survey is to provide a guidance and a glue for researchers and anti-discrimination data analysts on concepts, problems, application areas, datasets, methods, and approaches from a multidisciplinary perspective. We organize the approaches according to their method of data collection as observational, quasi-experimental, and experimental studies. A fourth line of recently blooming research on knowledge discovery based methods is also covered. Observational methods are further categorized on the basis of their application context: labor economics, social profiling, consumer markets, and others.},
	doi = {10.1017/S0269888913000039},
	author = {Andrea Romei and Salvatore Ruggieri}
}
@conference {977,
	title = {Ontolifloss: Ontology for learning processes in FLOSS communities},
	booktitle = {International Conference on Software Engineering and Formal Methods},
	year = {2014},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Free/Libre Open Source Software (FLOSS) communities are considered an example of commons-based peer-production models where groups of participants work together to achieve projects of common purpose. In these settings, many occurring activities can be documented and have established them as learning environments. As knowledge exchange is proved to occur in FLOSS, the dynamic and free nature of participation poses a great challenge in understanding activities pertaining to Learning Processes.

In this paper we raise this question and propose an ontology (called OntoLiFLOSS) in order to define terms and concepts that can explain learning activities taking place in these communities. The objective of this endeavor is to define in the simplest possible way a common definition of concepts and activities that can guide the identification of learning processes taking place among FLOSS members in any of the standard repositories such as mailing list, SVN, bug trackers and even discussion forums.},
	doi = {10.1007/978-3-319-15201-1_11},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@conference {827,
	title = {Overlap versus partition: marketing classification and customer profiling in complex networks of products},
	booktitle = {Data engineering workshops (ICDEW), 2014 IEEE 30th international conference on},
	year = {2014},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {In 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.},
	doi = {10.1109/ICDEW.2014.6818312},
	url = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6818312},
	author = {Diego Pennacchioli and Michele Coscia and Dino Pedreschi}
}
@conference {623,
	title = {The patterns of musical influence on the Last.Fm social network},
	booktitle = {22nd Italian Symposium on Advanced Database Systems, {SEBD} 2014, Sorrento Coast, Italy, June 16-18, 2014.},
	year = {2014},
	author = {Diego Pennacchioli and Giulio Rossetti and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti and Michele Coscia}
}
@conference {565,
	title = {A Privacy Risk Model for Trajectory Data},
	booktitle = {Trust Management {VIII} - 8th {IFIP} {WG} 11.11 International Conference, {IFIPTM} 2014, Singapore, July 7-10, 2014. Proceedings},
	year = {2014},
	pages = {125{\textendash}140},
	abstract = {Time 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.},
	doi = {10.1007/978-3-662-43813-8_9},
	url = {http://dx.doi.org/10.1007/978-3-662-43813-8_9},
	author = {Anirban Basu and Anna Monreale and Juan Camilo Corena and Fosca Giannotti and Dino Pedreschi and Shinsaku Kiyomoto and Yutaka Miyake and Tadashi Yanagihara and Roberto Trasarti}
}
@article {EPJ14,
	title = {Privacy-by-Design in Big Data Analytics and Social Mining},
	journal = {EPJ Data Science},
	volume = {10},
	year = {2014},
	note = {2014:10},
	abstract = {Privacy 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.},
	doi = {10.1140/epjds/s13688-014-0010-4},
	author = {Anna Monreale and S Rinzivillo and Francesca Pratesi and Fosca Giannotti and Dino Pedreschi}
}
@conference {976,
	title = {Process mining event logs from FLOSS data: state of the art and perspectives},
	booktitle = {International Conference on Software Engineering and Formal Methods},
	year = {2014},
	publisher = {Springer, Cham},
	organization = {Springer, Cham},
	abstract = {Free/Libre Open Source Software (FLOSS) is a phenomenon that has undoubtedly triggered extensive research endeavors. At the heart of these initiatives is the ability to mine data from FLOSS repositories with the hope of revealing empirical evidence to answer existing questions on the FLOSS development process. In spite of the success produced with existing mining techniques, emerging questions about FLOSS data require alternative and more appropriate ways to explore and analyse such data.

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

},
	doi = {10.1007/978-3-319-15201-1_12},
	author = {Mukala, Patrick and Cerone, Antonio and Franco Turini}
}
@conference {725,
	title = {The purpose of motion: Learning activities from Individual Mobility Networks},
	booktitle = {International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014},
	year = {2014},
	doi = {10.1109/DSAA.2014.7058090},
	url = {http://dx.doi.org/10.1109/DSAA.2014.7058090},
	author = {S Rinzivillo and Lorenzo Gabrielli and Mirco Nanni and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti}
}
@article {985,
	title = {On quantified linear implications},
	journal = {Annals of Mathematics and Artificial Intelligence},
	volume = {71},
	number = {4},
	year = {2014},
	pages = {301{\textendash}325},
	abstract = {A Quantified Linear Implication (QLI) is an inclusion query over two polyhedral sets, with a quantifier string that specifies which variables are existentially quantified and which are universally quantified. Equivalently, it can be viewed as a quantified implication of two systems of linear inequalities. In this paper, we provide a 2-person game semantics for the QLI problem, which allows us to explore the computational complexities of several of its classes. More specifically, we prove that the decision problem for QLIs with an arbitrary number of quantifier alternations is PSPACE-hard. Furthermore, we explore the computational complexities of several classes of 0, 1, and 2-quantifier alternation QLIs. We observed that some classes are decidable in polynomial time, some are NP-complete, some are coNP-hard and some are  ΠP2Π2P -hard. We also establish the hardness of QLIs with 2 or more quantifier alternations with respect to the first quantifier in the quantifier string and the number of quantifier alternations. All the proofs that we provide for polynomially solvable problems are constructive, i.e., polynomial-time decision algorithms are devised that utilize well-known procedures. QLIs can be utilized as powerful modelling tools for real-life applications. Such applications include reactive systems, real-time schedulers, and static program analyzers.},
	doi = {10.1007/s10472-013-9332-3},
	author = {Eirinakis, Pavlos and Salvatore Ruggieri and Subramani, K and Wojciechowski, Piotr}
}
@article {828,
	title = {The retail market as a complex system},
	journal = {EPJ Data Science},
	volume = {3},
	number = {1},
	year = {2014},
	pages = {1{\textendash}27},
	abstract = {Aim 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{\textquoteright}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{\textquoteright} volumes of sales with the customers{\textquoteright} 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.},
	doi = {10.1140/epjds/s13688-014-0033-x},
	url = {http://link.springer.com/article/10.1140/epjds/s13688-014-0033-x},
	author = {Diego Pennacchioli and Michele Coscia and S Rinzivillo and Fosca Giannotti and Dino Pedreschi}
}
@inbook {636,
	title = {Retrieving Points of Interest from Human Systematic Movements},
	booktitle = {Software Engineering and Formal Methods},
	year = {2014},
	pages = {294{\textendash}308},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {Human 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{\textquoteright} 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.},
	doi = {10.1007/978-3-319-15201-1_19},
	author = {Riccardo Guidotti and Anna Monreale and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@article {622,
	title = {Uncovering Hierarchical and Overlapping Communities with a Local-First Approach},
	journal = {{TKDD}},
	volume = {9},
	number = {1},
	year = {2014},
	pages = {6},
	abstract = {Community discovery in complex networks is the task of organizing a network{\textquoteright}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.},
	doi = {10.1145/2629511},
	url = {http://doi.acm.org/10.1145/2629511},
	author = {Michele Coscia and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@conference {573,
	title = {Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach},
	booktitle = {47th SIS Scientific Meeting of the Italian Statistica Society},
	year = {2014},
	month = {06/2014},
	address = {Cagliari },
	abstract = {The 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 {\textquotedblleft}proxies{\textquotedblright}, as the mobile
calls data for mobility. In this paper we investigate to what extent such {\textquotedblright}big data{\textquotedblright},
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 {\textquotedblleft}Commssione di studio
avente il compito di orientare le scelte dellIstat sul tema dei Big Data {\textquotedblright}. In an on-
going project at ISTAT, called {\textquotedblleft}Persons and Places{\textquotedblright} {\textendash} based on an integration of
administrative data sources, it has been produced a first release of Origin Destina-
tion matrix {\textendash} at municipality level {\textendash} 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) {\textendash}
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)},
	isbn = {978-88-8467-874-4},
	url = {http://www.sis2014.it/proceedings/allpapers/3026.pdf},
	author = {Barbara Furletti and Lorenzo Gabrielli and Fosca Giannotti and Letizia Milli and Mirco Nanni and Dino Pedreschi}
}
@conference {MokMasd2014,
	title = {Use of mobile phone data to estimate visitors mobility flows},
	booktitle = {Proceedings of MoKMaSD},
	year = {2014},
	abstract = {Big Data originating from the digital breadcrumbs of human activities,
sensed as by-product of the technologies that we use for our daily activities, allows
us to observe the individual and collective behavior of people at an unprecedented
detail. Many dimensions of our social life have big data {\textquotedblleft}proxies{\textquotedblright}, such as the mo-
bile calls data for mobility. In this paper we investigate to what extent data coming
from mobile operators could be a support in producing reliable and timely estimates
of intra-city mobility flows. The idea is to define an estimation method based on
calling data to characterize the mobility habits of visitors at the level of a single
municipality},
	url = {http://www.di.unipi.it/mokmasd/symposium-2014/preproceedings/GabrielliEtAl-mokmasd2014.pdf},
	author = {Lorenzo Gabrielli and Barbara Furletti and Fosca Giannotti and Mirco Nanni and S Rinzivillo}
}
@article {984,
	title = {Using t-closeness anonymity to control for non-discrimination.},
	journal = {Trans. Data Privacy},
	volume = {7},
	number = {2},
	year = {2014},
	pages = {99{\textendash}129},
	abstract = {We investigate the relation between t-closeness, a well-known model of data anonymization
against attribute disclosure, and α-protection, a model of the social discrimination hidden in
data. We show that t-closeness implies bdf (t)-protection, for a bound function bdf () depending on
the discrimination measure f() at hand. This allows us to adapt inference control methods, such
as the Mondrian multidimensional generalization technique and the Sabre bucketization and redistribution
framework, to the purpose of non-discrimination data protection. The parallel between
the two analytical models raises intriguing issues on the interplay between data anonymization and
non-discrimination research in data protection.},
	url = {http://dl.acm.org/citation.cfm?id=2870623},
	author = {Salvatore Ruggieri}
}
@proceedings {528,
	title = {Analysis of GSM Calls Data for Understanding User Mobility Behavior},
	year = {2013},
	address = {Santa Clara, California},
	author = {Barbara Furletti and Lorenzo Gabrielli and Chiara Renso and S Rinzivillo}
}
@article {542,
	title = {Assessing the Attractiveness of Places with Movement Data },
	journal = {Journal of Information and Data Management},
	volume = {4},
	number = {2},
	year = {2013},
	month = {2013},
	author = {Andr{\'e} Salvaro Furtado and Renato Fileto and Chiara Renso}
}
@conference {536,
	title = {Average Speed Estimation For Road Networks Based On GPS Raw Trajectories},
	booktitle = {ICEIS Conference},
	year = {2013},
	author = {Ivanildo Barbosa and Marco A. Casanova and Chiara Renso and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do}
}
@article {795,
	title = {Awareness and learning in participatory noise sensing.},
	journal = {PLoS One},
	volume = {8},
	year = {2013},
	month = {2013},
	pages = {e81638},
	abstract = {<p>The development of ICT infrastructures has facilitated the emergence of new paradigms for looking at society and the environment over the last few years. Participatory environmental sensing, i.e. directly involving citizens in environmental monitoring, is one example, which is hoped to encourage learning and enhance awareness of environmental issues. In this paper, an analysis of the behaviour of individuals involved in noise sensing is presented. Citizens have been involved in noise measuring activities through the WideNoise smartphone application. This application has been designed to record both objective (noise samples) and subjective (opinions, feelings) data. The application has been open to be used freely by anyone and has been widely employed worldwide. In addition, several test cases have been organised in European countries. Based on the information submitted by users, an analysis of emerging awareness and learning is performed. The data show that changes in the way the environment is perceived after repeated usage of the application do appear. Specifically, users learn how to recognise different noise levels they are exposed to. Additionally, the subjective data collected indicate an increased user involvement in time and a categorisation effect between pleasant and less pleasant environments.</p>},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0081638},
	author = {Becker, Martin and Saverio Caminiti and Fiorella, Donato and Francis, Louise and Pietro Gravino and Haklay, Mordechai Muki and Hotho, Andreas and Vittorio Loreto and Mueller, Juergen and Ricchiuti, Ferdinando and Vito D P Servedio and Alina Sirbu and Francesca Tria}
}
@conference {538,
	title = {Baquara: A Holistic Ontological Framework for Movement Analysis with Linked Data},
	booktitle = {Entity Relationship Conference - ER 2013},
	year = {2013},
	address = {Hong Kong},
	author = {Renato Fileto and Marcelo Krger and Nikos Pelekis and Yannis Theodoridis and Chiara Renso}
}
@article {810,
	title = {Cohesion, consensus and extreme information in opinion dynamics},
	journal = {Advances in Complex Systems},
	volume = {16},
	number = {06},
	year = {2013},
	pages = {1350035},
	doi = {10.1142/S0219525913500355},
	url = {http://www.worldscientific.com/doi/abs/10.1142/S0219525913500355},
	author = {Alina Sirbu and Vittorio Loreto and Vito D P Servedio and Francesca Tria}
}
@conference {731,
	title = {Comparing General Mobility and Mobility by Car},
	booktitle = {Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI CBIC), 2013 BRICS Congress on},
	year = {2013},
	month = {Sept},
	doi = {10.1109/BRICS-CCI-CBIC.2013.116},
	author = {Luca Pappalardo and Filippo Simini and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@article {531,
	title = {CONSTAnT - A Conceptual Data Model for Semantic Trajectories of Moving Objects },
	journal = {Transaction in GIS},
	year = {2013},
	author = {Vania Bogorny and Chiara Renso and Artur Ribeiro de Aquino and Fernando de Lucca Siqueira and Luis Otavio Alvares}
}
@conference {631,
	title = {Data Anonymity Meets Non-discrimination},
	booktitle = {Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on},
	year = {2013},
	publisher = {IEEE},
	organization = {IEEE},
	author = {Salvatore Ruggieri}
}
@inbook {634,
	title = {The discovery of discrimination},
	booktitle = {Discrimination and privacy in the information society},
	year = {2013},
	pages = {91{\textendash}108},
	publisher = {Springer},
	organization = {Springer},
	author = {Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@article {632,
	title = {Discrimination discovery in scientific project evaluation: A case study},
	journal = {Expert Systems with Applications},
	volume = {40},
	number = {15},
	year = {2013},
	pages = {6064{\textendash}6079},
	author = {Andrea Romei and Salvatore Ruggieri and Franco Turini}
}
@conference {508,
	title = {Efficient GPU-based skyline computation},
	booktitle = {DAMON@SIGMOD 2013},
	year = {2013},
	month = {2013},
	author = {Kenneth S. Boeg and Ira Assent, and Matteo Magnani}
}
@conference {729,
	title = {"Engine Matters": {A} First Large Scale Data Driven Study on Cyclists{\textquoteright} Performance},
	booktitle = {13th {IEEE} International Conference on Data Mining Workshops, {ICDM} Workshops, TX, USA, December 7-10, 2013},
	year = {2013},
	doi = {10.1109/ICDMW.2013.41},
	url = {http://dx.doi.org/10.1109/ICDMW.2013.41},
	author = {Paolo Cintia and Luca Pappalardo and Dino Pedreschi}
}
@article {567,
	title = {Evolving networks: Eras and turning points},
	journal = {Intell. Data Anal.},
	volume = {17},
	number = {1},
	year = {2013},
	pages = {27{\textendash}48},
	abstract = {Within 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.},
	doi = {10.3233/IDA-120566},
	url = {http://dx.doi.org/10.3233/IDA-120566},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@proceedings {529,
	title = {Explaining the PRoduct Range Effect in Purchase Data},
	year = {2013},
	author = {Diego Pennacchioli and Michele Coscia and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@conference {684,
	title = {A Gravity Model for Speed Estimation over Road Network},
	booktitle = {2013 {IEEE} 14th International Conference on Mobile Data Management, Milan, Italy, June 3-6, 2013 - Volume 2},
	year = {2013},
	doi = {10.1109/MDM.2013.83},
	url = {http://dx.doi.org/10.1109/MDM.2013.83},
	author = {Paolo Cintia and Roberto Trasarti and Jos{\'e} Ant{\^o}nio Fernandes de Mac{\^e}do and Livia Almada and Camila Fereira}
}
@article {681,
	title = {How you move reveals who you are: understanding human behavior by analyzing trajectory data},
	journal = {Knowl. Inf. Syst.},
	volume = {37},
	number = {2},
	year = {2013},
	pages = {331{\textendash}362},
	doi = {10.1007/s10115-012-0511-z},
	url = {http://dx.doi.org/10.1007/s10115-012-0511-z},
	author = {Chiara Renso and Miriam Baglioni and Jos{\'e} Ant{\^o}nio Fernandes de Mac{\^e}do and Roberto Trasarti and Monica Wachowicz}
}
@conference {537,
	title = {Inferring human activities from GPS tracks UrbComp},
	booktitle = {Workshop at KDD 2013},
	year = {2013},
	address = {Chicago USA},
	author = {Paolo Cintia and Barbara Furletti and Chiara Renso}
}
@conference {633,
	title = {Learning from polyhedral sets},
	booktitle = {Proceedings of the Twenty-Third international joint conference on Artificial Intelligence},
	year = {2013},
	publisher = {AAAI Press},
	organization = {AAAI Press},
	author = {Salvatore Ruggieri}
}
@conference {504,
	title = {Measuring tie strength in multidimensional networks},
	booktitle = {SEDB 2013},
	year = {2013},
	month = {2013},
	author = {Giulio Rossetti and Luca Pappalardo and Dino Pedreschi}
}
@mastersthesis {643,
	title = {Mobility Ranking - Human Mobility Analysis Using Ranking Measures},
	year = {2013},
	author = {Riccardo Guidotti}
}
@conference {539,
	title = {Mob-Warehouse: A semantic approach for mobility analysis with a Trajectory Data Ware- house},
	booktitle = {SecoGIS 2013 - International Workshop on Semantic Aspects of GIS, Joint to ER conference 2013},
	year = {2013},
	address = {Hong Kong},
	author = {Ricardo Wagner and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Alessandra Raffaet{\`a} and Chiara Renso and Alessandro Roncato and Roberto Trasarti}
}
@conference {704,
	title = {MP4-A Project: Mobility Planning For Africa},
	booktitle = {In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013)},
	year = {2013},
	address = {Cambridge, USA},
	abstract = {This project aims to create a tool that uses mobile phone transaction (trajectory) data that will be able to address transportation related challenges, thus allowing promotion and facilitation of sustainable urban mobility planning in Third World countries. The proposed tool is a transport demand model for Ivory Coast, with emphasis on its major urbanization Abidjan. The consortium will bring together available data from the internet, and integrate these with the mobility data obtained from the mobile phones in order to build the best possible transport model. A transport model allows an understanding of current and future infrastructure requirements in Ivory Coast. As such, this project will provide the first proof of concept. In this context, long-term analysis of individual call traces will be performed to reconstruct systematic movements, and to infer an origin-destination matrix. A similar process will be performed using the locations of caller and recipient of phone calls, enabling the comparison of socio-economic ties vs. mobility. The emerging links between different areas will be used to build an effective map to optimize regional border definitions and road infrastructure from a mobility perspective. Finally, we will try to build specialized origin-destination matrices for specific categories of population. Such categories will be inferred from data through analysis of calling behaviours, and will also be used to characterize the population of different cities. The project also includes a study of data compliance with distributions of standard measures observed in literature, including distribution of calls, call durations and call network features.},
	url = {http://perso.uclouvain.be/vincent.blondel/netmob/2013/D4D-book.pdf},
	author = {Mirco Nanni and Roberto Trasarti and Barbara Furletti and Lorenzo Gabrielli and Peter Van Der Mede and Joost De Bruijn and Erik de Romph and Gerard Bruil}
}
@conference {505,
	title = {On multidimensional network measures},
	booktitle = {SEDB 2013},
	year = {2013},
	month = {2013},
	abstract = {Networks, i.e., sets of interconnected entities, are ubiquitous,
spanning disciplines as diverse as sociology, biology and computer science.
The recent availability of large amounts of network data has thus
provided a unique opportunity to develop models and analysis tools applicable
to a wide range of scenarios. However, real-world phenomena are
often more complex than existing graph data models. One relevant example
concerns the numerous types of social relationships (or edges) that
can be present between individuals in a social network. In this short paper
we present a unified model and a set of measures recently developed
to represent and analyze network data with multiple types of edges.},
	url = {https://www.researchgate.net/publication/256194479_On_multidimensional_network_measures},
	author = {Matteo Magnani and Anna Monreale and Giulio Rossetti and Fosca Giannotti}
}
@article {789,
	title = {Opinion dynamics with disagreement and modulated information},
	journal = {Journal of Statistical Physics},
	year = {2013},
	pages = {1{\textendash}20},
	doi = {10.1007/s10955-013-0724-x},
	url = {http://link.springer.com/article/10.1007/s10955-013-0724-x},
	author = {Alina Sirbu and Vittorio Loreto and Vito D P Servedio and Francesca Tria}
}
@conference {535,
	title = {Pisa Tourism fluxes Observatory: deriving mobility indicators from GSM call habits},
	booktitle = {NetMob Conference 2013},
	year = {2013},
	author = {Barbara Furletti and Lorenzo Gabrielli and Chiara Renso and S Rinzivillo}
}
@conference {615,
	title = {Privacy-Aware Distributed Mobility Data Analytics},
	booktitle = {SEBD},
	year = {2013},
	address = {Roccella Jonica},
	abstract = {We 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.
},
	author = {Francesca Pratesi and Anna Monreale and Hui Wendy Wang and S Rinzivillo and Dino Pedreschi and Gennady Andrienko and Natalia Andrienko}
}
@inbook {571,
	title = {Privacy-Preserving Distributed Movement Data Aggregation},
	booktitle = {Geographic Information Science at the Heart of Europe},
	series = {Lecture Notes in Geoinformation and Cartography},
	year = {2013},
	pages = {225-245},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {We 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{\textquoteright}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.},
	isbn = {978-3-319-00614-7},
	doi = {10.1007/978-3-319-00615-4_13},
	url = {http://dx.doi.org/10.1007/978-3-319-00615-4_13},
	author = {Anna Monreale and Hui Wendy Wang and Francesca Pratesi and S Rinzivillo and Dino Pedreschi and Gennady Andrienko and Natalia Andrienko},
	editor = {Vandenbroucke, Danny and Bucher, B{\'e}n{\'e}dicte and Crompvoets, Joep}
}
@article {478,
	title = {Privacy-Preserving Mining of Association Rules From Outsourced Transaction Databases},
	journal = { IEEE Systems Journal},
	year = {2013},
	abstract = {Spurred 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{\textquoteright}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.},
	doi = {10.1109/JSYST.2012.2221854},
	author = {Fosca Giannotti and L.V.S. Lakshmanan and Anna Monreale and Dino Pedreschi and Hui Wendy Wang}
}
@conference {533,
	title = {A Proactive Ap- plication to Monitor Truck Fleets},
	booktitle = {Mobile Data Management Conference, 2013},
	year = {2013},
	author = {Fabio Da Costa Albuquerque and Marco A. Casanova and Marcelo Tilio M. de Carvalho and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Chiara Renso}
}
@conference {563,
	title = {Quantification Trees},
	booktitle = {2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA, December 7-10, 2013},
	year = {2013},
	pages = {528{\textendash}536},
	abstract = {In 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.},
	doi = {10.1109/ICDM.2013.122},
	url = {http://dx.doi.org/10.1109/ICDM.2013.122},
	author = {Letizia Milli and Anna Monreale and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi and Fabrizio Sebastiani}
}
@article {526,
	title = {Scalable Analysis of Movement Data for Extracting and Exploring Significant Places},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {19},
	number = {7},
	year = {2013},
	chapter = {49},
	author = {Gennady Andrienko and Natalia Andrienko and C. Hunter and S Rinzivillo and Stefan Wrobel}
}
@article {525,
	title = {Semantic Trajectories Modeling and Analysis},
	journal = {ACM Computing Surveys},
	volume = {45},
	number = {4},
	year = {2013},
	month = {August 2013},
	author = {Christine Parent and Stefano Spaccapietra and Chiara Renso and Gennady Andrienko and Natalia Andrienko and Vania Bogorny and Damiani M L, and Gkoulalas-Divanis A, and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Nikos Pelekis}
}
@article {527,
	title = {Spatial and Temporal Evaluation of Network-based Analysis of Human Mobility},
	journal = {Social Network Analysis and Mining},
	volume = {to appear},
	year = {2013},
	author = {Michele Coscia and S Rinzivillo and Fosca Giannotti and Dino Pedreschi}
}
@inbook {543,
	title = {Spatio and Spatio-temporal Reasoning and Decision Support Tools},
	booktitle = {Entry at Encyclopedia of Social Network Analysis and Mining},
	year = {2013},
	edition = {springer},
	author = {Monica Wachowicz and Chiara Renso}
}
@conference {503,
	title = {Spatio temporal keyword-queries in Social Networs},
	year = {2013},
	author = {Vittoria Cozza and Antonio Messina and Danilo Montesi and Luca Arietta and Matteo Magnani}
}
@article {979,
	title = {Spatio-Temporal Data},
	journal = {Spatio-Temporal Databases: Flexible Querying and Reasoning},
	year = {2013},
	pages = {75},
	author = {Mirco Nanni and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {541,
	title = {A Study on Parameter Estimation for a Mining Flock Algorithm },
	booktitle = {Mining Complex Patterns Workshop, ECML PKDD 2013},
	year = {2013},
	author = {Rebecca Ong and Mirco Nanni and Chiara Renso and Monica Wachowicz and Dino Pedreschi}
}
@conference {534,
	title = {Tailoring Moving Patterns to Contexts},
	booktitle = {AGILE Conference},
	year = {2013},
	address = {Leuven, Belgium, 2013},
	author = {Monica Wachowicz and Rebecca Ong and Chiara Renso}
}
@conference {624,
	title = {The Three Dimensions of Social Prominence},
	booktitle = {Social Informatics - 5th International Conference, SocInfo 2013, Kyoto, Japan, November 25-27, 2013, Proceedings},
	year = {2013},
	doi = {10.1007/978-3-319-03260-3_28},
	url = {http://dx.doi.org/10.1007/978-3-319-03260-3_28},
	author = {Diego Pennacchioli and Giulio Rossetti and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti and Michele Coscia}
}
@article {682,
	title = {Towards mega-modeling: a walk through data analysis experiences},
	journal = {{SIGMOD} Record},
	volume = {42},
	number = {3},
	year = {2013},
	pages = {19{\textendash}27},
	doi = {10.1145/2536669.2536673},
	url = {http://doi.acm.org/10.1145/2536669.2536673},
	author = {Stefano Ceri and Themis Palpanas and Emanuele Della Valle and Dino Pedreschi and Johann-Christoph Freytag and Roberto Trasarti}
}
@conference {683,
	title = {Transportation Planning Based on {GSM} Traces: {A} Case Study on Ivory Coast},
	booktitle = {Citizen in Sensor Networks - Second International Workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, Revised Selected Papers},
	year = {2013},
	doi = {10.1007/978-3-319-04178-0_2},
	url = {http://dx.doi.org/10.1007/978-3-319-04178-0_2},
	author = {Mirco Nanni and Roberto Trasarti and Barbara Furletti and Lorenzo Gabrielli and Peter Van Der Mede and Joost De Bruijn and Erik de Romph and Gerard Bruil}
}
@article {732,
	title = {{Understanding the patterns of car travel}},
	journal = {The European Physical Journal Special Topics},
	volume = {215},
	number = {1},
	year = {2013},
	pages = {61{\textendash}73},
	abstract = {{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.}},
	doi = {10.1140/epjst\%252fe2013-01715-5},
	url = {http://dx.doi.org/10.1140/epjst\%252fe2013-01715-5},
	author = {Luca Pappalardo and S Rinzivillo and Qu, Zehui and Dino Pedreschi and Fosca Giannotti}
}
@conference {daytag2013,
	title = {Where Have You Been Today? Annotating Trajectories with DayTag},
	booktitle = {International Conference on Spatial and Spatio-temporal Databases (SSTD)},
	year = {2013},
	pages = {467-471},
	doi = {http://dx.doi.org/10.1007/978-3-642-40235-7_30},
	author = {S Rinzivillo and Fernando de Lucca Siqueira and Lorenzo Gabrielli and Chiara Renso and Vania Bogorny}
}
@conference {540,
	title = {Where Shall We Go Today? Planning Touristic Tours with TripBuilder},
	booktitle = {International Conference CIKM 2013},
	year = {2013},
	address = {San Francisco, USA},
	author = {Igo Brilhante and Franco Maria Nardini and Raffaele Perego and Chiara Renso and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do}
}
@conference {809,
	title = {XTribe: a web-based social computation platform},
	booktitle = {Cloud and Green Computing (CGC), 2013 Third International Conference on},
	year = {2013},
	publisher = {IEEE},
	organization = {IEEE},
	doi = {10.1109/CGC.2013.69},
	url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=\&arnumber=6686061\&url=http\%3A\%2F\%2Fieeexplore.ieee.org\%2Fxpls\%2Fabs_all.jsp\%3Farnumber\%3D6686061},
	author = {Saverio Caminiti and Cicali, Claudio and Pietro Gravino and Vittorio Loreto and Vito D P Servedio and Alina Sirbu and Francesca Tria}
}
@conference {502,
	title = {You Know Because I Know{\textquotedblright}: a Multidimensional Network Approach to Human Resources Problem},
	booktitle = {ASONAM 2013},
	year = {2013},
	author = {Michele Coscia and Giulio Rossetti and Diego Pennacchioli and Damiano Ceccarelli and Fosca Giannotti}
}
@conference {687,
	title = {An Agent-Based Model to Evaluate Carpooling at Large Manufacturing Plants},
	booktitle = {Proceedings of the 3rd International Conference on Ambient Systems, Networks and Technologies {(ANT} 2012), the 9th International Conference on Mobile Web Information Systems (MobiWIS-2012), Niagara Falls, Ontario, Canada, August 27-29, 2012},
	year = {2012},
	doi = {10.1016/j.procs.2012.08.001},
	url = {http://dx.doi.org/10.1016/j.procs.2012.08.001},
	author = {Tom Bellemans and Sebastian Bothe and Sungjin Cho and Fosca Giannotti and Davy Janssens and Luk Knapen and Christine K{\"o}rner and Michael May and Mirco Nanni and Dino Pedreschi and Hendrik Stange and Roberto Trasarti and Ansar-Ul-Haque Yasar and Geert Wets}
}
@article {488,
	title = {Analisi di Mobilita{\textquoteright} con dati eterogenei},
	year = {2012},
	institution = {ISTI - CNR},
	address = {Pisa},
	author = {Barbara Furletti and Roberto Trasarti and Lorenzo Gabrielli and S Rinzivillo and Luca Pappalardo and Fosca Giannotti}
}
@conference {480,
	title = {Anonymity: a Comparison between the Legal and Computer Science Perspectives.},
	booktitle = {The 5rd International Conference on Computers, Privacy, and Data Protection: {\textquotedblleft}European Data Protection: Coming of Age{\textquotedblright}},
	year = {2012},
	month = {2012},
	abstract = {Privacy preservation has emerged as a major challenge in ICT. One possible solution for enforcing privacy is to guarantee anonymity. Indeed, according to international regulations, no restriction is applied to the handling of anonymous data. Consequently, in the past years the notion of anonymity has been extensively studied by two different communities: Law researchers and professionals that propose definitions of privacy regulations, and Computer Scientists attempting to provide technical solutions for enforcing the legal requirements.

In this contribution we address the problem with an interdisciplinary approach, in the aim to encourage the reciprocal understanding and collaboration between researchers in the two areas. To achieve this, we compare the different notions of anonymity provided in the European data protection Law with the formal models proposed in Computer Science. This analysis allows us to identify the main similarities and differences between the two points of view, hence highlighting the need for a joint research effort.},
	doi = {10.1007/978-94-007-5170-5_4},
	author = {S Mascetti and Anna Monreale and A Ricci and A. Gerino}
}
@conference {479,
	title = {AUDIO: An Integrity Auditing Framework of Outlier-Mining-as-a-Service Systems.},
	booktitle = {Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2012 },
	year = {2012},
	month = {2012},
	abstract = {Spurred 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{\textquoteright}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.},
	doi = {10.1007/978-3-642-33486-3_1},
	author = {R.Liu and Hui Wendy Wang and Anna Monreale and Dino Pedreschi and Fosca Giannotti and W Guo}
}
@conference {570,
	title = {Classifying Trust/Distrust Relationships in Online Social Networks},
	booktitle = {2012 International Conference on Privacy, Security, Risk and Trust, {PASSAT} 2012, and 2012 International Confernece on Social Computing, SocialCom 2012, Amsterdam, Netherlands, September 3-5, 2012},
	year = {2012},
	pages = {552{\textendash}557},
	abstract = {Online social networks are increasingly being used as places where communities gather to exchange information, form opinions, collaborate in response to events. An aspect of this information exchange is how to determine if a source of social information can be trusted or not. Data mining literature addresses this problem. However, if usually employs social balance theories, by looking at small structures in complex networks known as triangles. This has proven effective in some cases, but it under performs in the lack of context information about the relation and in more complex interactive structures. In this paper we address the problem of creating a framework for the trust inference, able to infer the trust/distrust relationships in those relational environments that cannot be described by using the classical social balance theory. We do so by decomposing a trust network in its ego network components and mining on this ego network set the trust relationships, extending a well known graph mining algorithm. We test our framework on three public datasets describing trust relationships in the real world (from the social media Epinions, Slash dot and Wikipedia) and confronting our results with the trust inference state of the art, showing better performances where the social balance theory fails.},
	doi = {10.1109/SocialCom-PASSAT.2012.115},
	url = {http://dx.doi.org/10.1109/SocialCom-PASSAT.2012.115},
	author = {Giacomo Bachi and Michele Coscia and Anna Monreale and Fosca Giannotti}
}
@proceedings {545,
	title = {ComeTogether: Discovering Communities of Places in Mobility Data},
	year = {2012},
	month = {2012},
	pages = { 268-273},
	author = {Igo Brilhante and Michele Berlingerio and Roberto Trasarti and Chiara Renso and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Marco A. Casanova}
}
@article {462,
	title = {Data Science for Simulating the Era of Electric Vehicles},
	journal = {KI - K{\"u}nstliche Intelligenz},
	year = {2012},
	doi = {10.1007/s13218-012-0183-6},
	author = {Davy Janssens and Fosca Giannotti and Mirco Nanni and Dino Pedreschi and S Rinzivillo}
}
@conference {625,
	title = {DEMON: a local-first discovery method for overlapping communities},
	booktitle = {The 18th {ACM} {SIGKDD} International Conference on Knowledge Discovery and Data Mining, {KDD} {\textquoteright}12, Beijing, China, August 12-16, 2012},
	year = {2012},
	doi = {10.1145/2339530.2339630},
	url = {http://doi.acm.org/10.1145/2339530.2339630},
	author = {Michele Coscia and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@conference {507,
	title = {DEMON: a Local-First Discovery Method for Overlapping Communities},
	booktitle = {KDD 2012},
	year = {2012},
	month = {2012},
	author = {Michele Coscia and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi}
}
@article {455,
	title = {Discovering the Geographical Borders of Human Mobility},
	journal = {KI - K{\"u}nstliche Intelligenz},
	year = {2012},
	chapter = {1},
	abstract = {The 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.},
	issn = {0933-1875},
	doi = {10.1007/s13218-012-0181-8},
	url = {https://link.springer.com/article/10.1007\%2Fs13218-012-0181-8},
	author = {S Rinzivillo and Simone Mainardi and Fabio Pezzoni and Michele Coscia and Fosca Giannotti and Dino Pedreschi}
}
@conference {626,
	title = {"How Well Do We Know Each Other?" Detecting Tie Strength in Multidimensional Social Networks},
	booktitle = {International Conference on Advances in Social Networks Analysis and Mining, {ASONAM} 2012, Istanbul, Turkey, 26-29 August 2012},
	year = {2012},
	doi = {10.1109/ASONAM.2012.180},
	url = {http://doi.ieeecomputersociety.org/10.1109/ASONAM.2012.180},
	author = {Luca Pappalardo and Giulio Rossetti and Dino Pedreschi}
}
@conference {487,
	title = {Identifying users profiles from mobile calls habits},
	booktitle = {ACM SIGKDD International Workshop on Urban Computing},
	year = {2012},
	publisher = {ACM New York, NY, USA {\textcopyright}2012},
	organization = {ACM New York, NY, USA {\textcopyright}2012},
	address = {Beijing, China},
	abstract = {The huge quantity of positioning data registered by our mobile phones stimulates several research questions, mainly originating from the combination of this huge quantity of data with the extreme heterogeneity of the tracked user and the low granularity of the data. We propose a methodology to partition the users tracked by GSM phone calls into profiles like resident, commuters, in transit and tourists. The methodology analyses the phone calls with a combination of top-down and bottom up techniques where the top-down phase is based on a sequence of queries that identify some behaviors. The bottom-up is a machine learning phase to find groups of similar call behavior, thus refining the previous step. The integration of the two steps results in the partitioning of mobile traces into these four user categories that can be deeper analyzed, for example to understand the tourist movements in city or the traffic effects of commuters. An experiment on the identification of user profiles on a real dataset collecting call records from one month in the city of Pisa illustrates the methodology.},
	isbn = {978-1-4503-1542-5},
	doi = {10.1145/2346496.2346500},
	url = {http://delivery.acm.org/10.1145/2350000/2346500/p17-furletti.pdf?ip=146.48.83.121\&acc=ACTIVE\%20SERVICE\&CFID=166768290\&CFTOKEN=58719386\&__acm__=1357648050_e23771c2f6bd8feb96bd66b39294175d},
	author = {Barbara Furletti and Lorenzo Gabrielli and Chiara Renso and S Rinzivillo}
}
@conference {686,
	title = {Individual Mobility Profiles: Methods and Application on Vehicle Sharing},
	booktitle = {Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings},
	year = {2012},
	url = {http://sebd2012.dei.unipd.it/documents/188475/32d00b8a-8ead-4d97-923f-bd2f2cf6ddcb},
	author = {Roberto Trasarti and Fabio Pinelli and Mirco Nanni and Fosca Giannotti}
}
@conference {569,
	title = {Injecting Discrimination and Privacy Awareness Into Pattern Discovery},
	booktitle = {12th {IEEE} International Conference on Data Mining Workshops, {ICDM} Workshops, Brussels, Belgium, December 10, 2012},
	year = {2012},
	pages = {360{\textendash}369},
	abstract = {Data 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.},
	doi = {10.1109/ICDMW.2012.51},
	url = {http://dx.doi.org/10.1109/ICDMW.2012.51},
	author = {Sara Hajian and Anna Monreale and Dino Pedreschi and Josep Domingo-Ferrer and Fosca Giannotti}
}
@article {798,
	title = {Integrating heterogeneous gene expression data for gene regulatory network modelling.},
	journal = {Theory Biosci},
	volume = {131},
	year = {2012},
	month = {2012 Jun},
	pages = {95-102},
	abstract = {<p>Gene regulatory networks (GRNs) are complex biological systems that have a large impact on protein levels, so that discovering network interactions is a major objective of systems biology. Quantitative GRN models have been inferred, to date, from time series measurements of gene expression, but at small scale, and with limited application to real data. Time series experiments are typically short (number of time points of the order of ten), whereas regulatory networks can be very large (containing hundreds of genes). This creates an under-determination problem, which negatively influences the results of any inferential algorithm. Presented here is an integrative approach to model inference, which has not been previously discussed to the authors{\textquoteright} knowledge. Multiple heterogeneous expression time series are used to infer the same model, and results are shown to be more robust to noise and parameter perturbation. Additionally, a wavelet analysis shows that these models display limited noise over-fitting within the individual datasets.</p>},
	issn = {1611-7530},
	doi = {10.1007/s12064-011-0133-0},
	author = {Alina Sirbu and Heather J Ruskin and Martin Crane}
}
@article {475,
	title = {Knowledge Discovery in Ontologies},
	journal = {Intelligent Data Analysis},
	volume = {16},
	year = {2012},
	chapter = {513},
	issn = {1571-4128},
	doi = {10.3233/IDA-2012-0536},
	url = {http://iospress.metapress.com/content/765h53w41286p578/fulltext.pdf},
	author = {Barbara Furletti and Franco Turini}
}
@conference {544,
	title = {M-Attract: Assessing Places Attractiveness by using Moving Objects Trajectories Data},
	booktitle = {GEOINFO 2012 Brazilian Conference on Geographical Information Systems},
	year = {2012},
	month = {2012},
	author = {Andr{\'e} Salvaro Furtado and Renato Fileto and Chiara Renso}
}
@conference {685,
	title = {Mega-modeling for Big Data Analytics},
	booktitle = {Conceptual Modeling - 31st International Conference {ER} 2012, Florence, Italy, October 15-18, 2012. Proceedings},
	year = {2012},
	doi = {10.1007/978-3-642-34002-4_1},
	url = {http://dx.doi.org/10.1007/978-3-642-34002-4_1},
	author = {Stefano Ceri and Emanuele Della Valle and Dino Pedreschi and Roberto Trasarti}
}
@article {482,
	title = {Multidimensional networks: foundations of structural analysis},
	journal = {World Wide Web},
	volume = { Volume 15 / 2012},
	year = {2012},
	month = {10/2012},
	abstract = {Complex 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. },
	doi = {10.1007/s11280-012-0190-4},
	url = {http://www.springerlink.com/content/f774289854430410/abstract/},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@proceedings {461,
	title = {Optimal Spatial Resolution for the Analysis of Human Mobility},
	year = {2012},
	address = {Instanbul, Turkey},
	author = {Michele Coscia and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@article {796,
	title = {RNA-Seq vs dual- and single-channel microarray data: sensitivity analysis for differential expression and clustering.},
	journal = {PLoS One},
	volume = {7},
	year = {2012},
	month = {2012},
	pages = {e50986},
	abstract = {<p>With the fast development of high-throughput sequencing technologies, a new generation of genome-wide gene expression measurements is under way. This is based on mRNA sequencing (RNA-seq), which complements the already mature technology of microarrays, and is expected to overcome some of the latter{\textquoteright}s disadvantages. These RNA-seq data pose new challenges, however, as strengths and weaknesses have yet to be fully identified. Ideally, Next (or Second) Generation Sequencing measures can be integrated for more comprehensive gene expression investigation to facilitate analysis of whole regulatory networks. At present, however, the nature of these data is not very well understood. In this paper we study three alternative gene expression time series datasets for the Drosophila melanogaster embryo development, in order to compare three measurement techniques: RNA-seq, single-channel and dual-channel microarrays. The aim is to study the state of the art for the three technologies, with a view of assessing overlapping features, data compatibility and integration potential, in the context of time series measurements. This involves using established tools for each of the three different technologies, and technical and biological replicates (for RNA-seq and microarrays, respectively), due to the limited availability of biological RNA-seq replicates for time series data. The approach consists of a sensitivity analysis for differential expression and clustering. In general, the RNA-seq dataset displayed highest sensitivity to differential expression. The single-channel data performed similarly for the differentially expressed genes common to gene sets considered. Cluster analysis was used to identify different features of the gene space for the three datasets, with higher similarities found for the RNA-seq and single-channel microarray dataset.</p>},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0050986},
	author = {Alina Sirbu and Kerr, Gr{\'a}inne and Martin Crane and Heather J Ruskin}
}
@article {601,
	title = {Smart cities of the future},
	journal = {European Physical Journal-Special Topics},
	volume = {214},
	number = {1},
	year = {2012},
	pages = {481},
	abstract = {Here we sketch the rudiments of what constitutes a smart city which we define as a city in which ICT is merged with traditional infrastructures, coordinated and integrated using new digital technologies. We first sketch our vision defining seven goals which concern: developing a new understanding of urban problems; effective and feasible ways to coordinate urban technologies; models and methods for using urban data across spatial and temporal scales; developing new technologies for communication and dissemination; developing new forms of urban governance and organisation; defining critical problems relating to cities, transport, and energy; and identifying risk, uncertainty, and hazards in the smart city. To this, we add six research challenges: to relate the infrastructure of smart cities to their operational functioning and planning through management, control and optimisation; to explore the notion of the city as a laboratory for innovation; to provide portfolios of urban simulation which inform future designs; to develop technologies that ensure equity, fairness and realise a better quality of city life; to develop technologies that ensure informed participation and create shared knowledge for democratic city governance; and to ensure greater and more effective mobility and access to opportunities for urban populations. We begin by defining the state of the art, explaining the science of smart cities. We define six scenarios based on new cities badging themselves as smart, older cities regenerating themselves as smart, the development of science parks, tech cities, and technopoles focused on high technologies, the development of urban services using contemporary ICT, the use of ICT to develop new urban intelligence functions, and the development of online and mobile forms of participation. Seven project areas are then proposed: Integrated Databases for the Smart City, Sensing, Networking and the Impact of New Social Media, Modelling Network Performance, Mobility and Travel Behaviour, Modelling Urban Land Use, Transport and Economic Interactions, Modelling Urban Transactional Activities in Labour and Housing Markets, Decision Support as Urban Intelligence, Participatory Governance and Planning Structures for the Smart City. Finally we anticipate the paradigm shifts that will occur in this research and define a series of key demonstrators which we believe are important to progressing a science of smart cities.},
	doi = {10.1140/epjst/e2012-01703-3},
	author = {Batty, Michael and Axhausen, Kay W and Fosca Giannotti and Pozdnoukhov, Alexei and Bazzani, Armando and Monica Wachowicz and Ouzounis, Georgios and Portugali, Yuval}
}
@inbook {476,
	title = {What else can be extracted from ontologies? Influence Rules},
	booktitle = {Software and Data Technologies},
	series = {Communications in Computer and Information Science},
	year = {2012},
	publisher = {Springer},
	organization = {Springer},
	author = {Franco Turini and Barbara Furletti}
}
@article {1273,
	title = {Wine and Food Tourism First European Conference},
	journal = {Edizioni ETS Pisa},
	year = {2012},
	author = {Romano, Maria Francesca and Michela Natilli}
}
@article {814,
	title = {Wisdom of crowds for robust gene network inference},
	journal = {Nature Methods},
	volume = {9},
	number = {8},
	year = {2012},
	pages = {796-804},
	doi = {10.1038/nmeth.2016},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-84870305264\&partnerID=40\&md5=04a686572bdefff60157bf68c95df7ea},
	author = {Daniel Marbach and J.C. Costello and Robert K{\"u}ffner and N.M. Vega and R.J. Prill and D.M. Camacho and K.R. Allison and Manolis Kellis and J.J. Collins and Aderhold, A. and Gustavo Stolovitzky and Bonneau, R. and Chen, Y. and Cordero, F. and Martin Crane and Dondelinger, F. and Drton, M. and Esposito, R. and Foygel, R. and De La Fuente, A. and Gertheiss, J. and Geurts, P. and Greenfield, A. and Grzegorczyk, M. and Haury, A.-C. and Holmes, B. and Hothorn, T. and Husmeier, D. and Huynh-Thu, V.A. and Irrthum, A. and Karlebach, G. and Lebre, S. and De Leo, V. and Madar, A. and Mani, S. and Mordelet, F. and Ostrer, H. and Ouyang, Z. and Pandya, R. and Petri, T. and Pinna, A. and Poultney, C.S. and Rezny, S. and Heather J Ruskin and Saeys, Y. and Shamir, R. and Alina Sirbu and Song, M. and Soranzo, N. and Statnikov, A. and N.M. Vega and Vera-Licona, P. and Vert, J.-P. and Visconti, A. and Haizhou Wang and Wehenkel, L. and Windhager, L. and Zhang, Y. and Zimmer, R.}
}
@article {797,
	title = {Wisdom of crowds for robust gene network inference.},
	journal = {Nat Methods},
	volume = {9},
	year = {2012},
	month = {2012 Aug},
	pages = {796-804},
	abstract = {<p>Reconstructing gene regulatory networks from high-throughput data is a long-standing challenge. Through the Dialogue on Reverse Engineering Assessment and Methods (DREAM) project, we performed a comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data. We characterize the performance, data requirements and inherent biases of different inference approaches, and we provide guidelines for algorithm application and development. We observed that no single inference method performs optimally across all data sets. In contrast, integration of predictions from multiple inference methods shows robust and high performance across diverse data sets. We thereby constructed high-confidence networks for E. coli and S. aureus, each comprising ~1,700 transcriptional interactions at a precision of ~50\%. We experimentally tested 53 previously unobserved regulatory interactions in E. coli, of which 23 (43\%) were supported. Our results establish community-based methods as a powerful and robust tool for the inference of transcriptional gene regulatory networks.</p>},
	issn = {1548-7105},
	doi = {10.1038/nmeth.2016},
	author = {Daniel Marbach and J.C. Costello and Robert K{\"u}ffner and N.M. Vega and R.J. Prill and D.M. Camacho and K.R. Allison and Manolis Kellis and J.J. Collins and Gustavo Stolovitzky}
}
@article {sam2011,
	title = {A classification for community discovery methods in complex networks},
	journal = {Statistical Analysis and Data Mining},
	volume = {4},
	number = {5},
	year = {2011},
	pages = {512-546},
	author = {Michele Coscia and Fosca Giannotti and Dino Pedreschi}
}
@article {MonrealeTPRB11,
	title = {C-safety: a framework for the anonymization of semantic trajectories},
	journal = {Transactions on Data Privacy},
	volume = {4},
	number = {2},
	year = {2011},
	pages = {73-101},
	abstract = {The 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{\textquoteright}s inference succeeds is much lower than the theoretical upper bound
established.},
	url = {http://dl.acm.org/citation.cfm?id=2019319\&CFID=803961971\&CFTOKEN=35994039},
	author = {Anna Monreale and Roberto Trasarti and Dino Pedreschi and Chiara Renso and Vania Bogorny}
}
@book {1275,
	title = {Dinamiche di impoverimento. Meccanismi, traiettorie ed effetti in un contesto locale},
	year = {2011},
	publisher = {Carocci Editore},
	organization = {Carocci Editore},
	author = {Tomei, Gabriele and Michela Natilli}
}
@conference {asonam22011,
	title = {Finding and Characterizing Communities in Multidimensional Networks},
	booktitle = {ASONAM},
	year = {2011},
	pages = {490-494},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {cikm2011,
	title = {Finding redundant and complementary communities in multidimensional networks},
	booktitle = {CIKM},
	year = {2011},
	pages = {2181-2184},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {asonam12011,
	title = {Foundations of Multidimensional Network Analysis},
	booktitle = {ASONAM},
	year = {2011},
	pages = {485-489},
	abstract = {Complex 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.},
	doi = {10.1109/ASONAM.2011.103},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@proceedings {358,
	title = {From Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data},
	year = {2011},
	author = {Gennady Andrienko and Natalia Andrienko and Cristophe Hurter and S Rinzivillo and Stefan Wrobel}
}
@conference {1277,
	title = {The impact of wine and food tourism in Italy: an analysis of official statistical data at province level},
	booktitle = {First European Conference on Wine and Food Tourism},
	year = {2011},
	author = {Michela Natilli and Romano, Maria Francesca}
}
@conference {1276,
	title = {The language of tourists in a wine and food blog},
	booktitle = {First European Conference on Wine and Food Tourism},
	year = {2011},
	author = {Pavone, Pasquale and Michela Natilli and Romano, Maria Francesca}
}
@conference {627,
	title = {Link Prediction su Reti Multidimensionali},
	booktitle = {Sistemi Evoluti per Basi di Dati - {SEBD} 2011, Proceedings of the Nineteenth Italian Symposium on Advanced Database Systems, Maratea, Italy, June 26-29, 2011},
	year = {2011},
	author = {Giulio Rossetti and Michele Berlingerio and Fosca Giannotti}
}
@article {1274,
	title = {Measuring the effectiveness of homeopathic care through objective and shared indicators},
	journal = {Homeopathy},
	volume = {100},
	number = {04},
	year = {2011},
	pages = {212{\textendash}219},
	author = {Leone, Laura and Marchitiello, Maria and Michela Natilli and Romano, Maria Francesca}
}
@conference {474,
	title = {Mining Influence Rules out of Ontologies},
	booktitle = {International Conference on Software and Data Technologies (ICSOFT)},
	year = {2011},
	month = {2011},
	address = {Siviglia, Spagna},
	author = {Barbara Furletti and Franco Turini}
}
@conference {TrasartiPNG11,
	title = {Mining mobility user profiles for car pooling},
	booktitle = {KDD},
	year = {2011},
	pages = {1190-1198},
	author = {Roberto Trasarti and Fabio Pinelli and Mirco Nanni and Fosca Giannotti}
}
@conference {481,
	title = {Privacy-preserving data mining from outsourced databases.},
	booktitle = { the 3rd International Conference on Computers, Privacy, and Data Protection: An element of choice },
	year = {2011},
	month = {2011},
	abstract = {Spurred 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.},
	doi = {10.1007/978-94-007-0641-5_19},
	author = {Fosca Giannotti and L.V.S. Lakshmanan and Anna Monreale and Dino Pedreschi and Hui Wendy Wang}
}
@article {jocs2011,
	title = {The pursuit of hubbiness: Analysis of hubs in large multidimensional networks},
	journal = {J. Comput. Science},
	volume = {2},
	number = {3},
	year = {2011},
	pages = {223-237},
	abstract = {Hubs 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{\textendash}word query log network, outlier detection in a social network, and temporal analysis of behaviors in a co-authorship network.},
	doi = {10.1016/j.jocs.2011.05.009},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@article {TrasartiGNPR11,
	title = {A Query Language for Mobility Data Mining},
	journal = {IJDWM},
	volume = {7},
	number = {1},
	year = {2011},
	pages = {24-45},
	author = {Roberto Trasarti and Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Chiara Renso}
}
@conference {446,
	title = {Scalable Link Prediction on Multidimensional Networks},
	booktitle = {ICDM Workshops},
	year = {2011},
	pages = {979-986},
	address = {Vancouver},
	author = {Giulio Rossetti and Michele Berlingerio and Fosca Giannotti}
}
@inbook {811,
	title = {Stages of Gene Regulatory Network Inference: the Evolutionary Algorithm Role},
	booktitle = {Evolutionary Algorithms},
	year = {2011},
	publisher = {InTech},
	organization = {InTech},
	doi = {DOI: 10.5772/15182},
	url = {http://www.intechopen.com/articles/show/title/stages-of-gene-regulatory-network-inference-the-evolutionary-algorithm-role},
	author = {Alina Sirbu and Heather J Ruskin and Martin Crane}
}
@article {1278,
	title = {Stiramenti identitari. Strategie di integrazione degli strannieri nella provincia di Massa Carrara tra appartenenza etnica ed esperienza transnazionale},
	year = {2011},
	author = {Tomei, Gabriele and Paletti, F and Michela Natilli}
}
@conference {OngPTNRRG11,
	title = {Traffic Jams Detection Using Flock Mining},
	booktitle = {ECML/PKDD (3)},
	year = {2011},
	pages = {650-653},
	author = {Rebecca Ong and Fabio Pinelli and Roberto Trasarti and Mirco Nanni and Chiara Renso and S Rinzivillo and Fosca Giannotti}
}
@article {vlbdjMatlas,
	title = {Unveiling the complexity of human mobility by querying and mining massive trajectory data},
	journal = {VLDB J.},
	volume = {20},
	number = {5},
	year = {2011},
	pages = {695-719},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@conference {RinzivilloR11,
	title = {Who/Where Are My New Customers?},
	booktitle = {ISMIS Industrial Session},
	year = {2011},
	pages = {307-317},
	author = {S Rinzivillo and Salvatore Ruggieri}
}
@conference {NanniTRGP10,
	title = {Advanced knowledge discovery on movement data with the GeoPKDD system},
	booktitle = {EDBT},
	year = {2010},
	pages = {693-696},
	author = {Mirco Nanni and Roberto Trasarti and Chiara Renso and Fosca Giannotti and Dino Pedreschi}
}
@conference {NanniTRGP10,
	title = {Advanced knowledge discovery on movement data with the GeoPKDD system},
	booktitle = {EDBT},
	year = {2010},
	pages = {693-696},
	author = {Mirco Nanni and Roberto Trasarti and Chiara Renso and Fosca Giannotti and Dino Pedreschi}
}
@conference {pakdd2010,
	title = {As Time Goes by: Discovering Eras in Evolving Social Networks},
	booktitle = {PAKDD (1)},
	year = {2010},
	pages = {81-90},
	abstract = {Within 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.},
	doi = {10.1007/978-3-642-13657-3_11},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@article {800,
	title = {Comparison of evolutionary algorithms in gene regulatory network model inference.},
	journal = {BMC Bioinformatics},
	volume = {11},
	year = {2010},
	month = {2010},
	pages = {59},
	abstract = {<p><b>BACKGROUND: </b>The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient.</p><p><b>RESULTS: </b>This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared.</p><p><b>CONCLUSIONS: </b>Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.</p>},
	issn = {1471-2105},
	doi = {10.1186/1471-2105-11-59},
	author = {Alina Sirbu and Heather J Ruskin and Martin Crane}
}
@article {799,
	title = {Cross-platform microarray data normalisation for regulatory network inference.},
	journal = {PLoS One},
	volume = {5},
	year = {2010},
	month = {2010},
	pages = {e13822},
	abstract = {<p><b>BACKGROUND: </b>Inferring Gene Regulatory Networks (GRNs) from time course microarray data suffers from the dimensionality problem created by the short length of available time series compared to the large number of genes in the network. To overcome this, data integration from diverse sources is mandatory. Microarray data from different sources and platforms are publicly available, but integration is not straightforward, due to platform and experimental differences.</p><p><b>METHODS: </b>We analyse here different normalisation approaches for microarray data integration, in the context of reverse engineering of GRN quantitative models. We introduce two preprocessing approaches based on existing normalisation techniques and provide a comprehensive comparison of normalised datasets.</p><p><b>CONCLUSIONS: </b>Results identify a method based on a combination of Loess normalisation and iterative K-means as best for time series normalisation for this problem.</p>},
	issn = {1932-6203},
	doi = {10.1371/journal.pone.0013822},
	author = {Alina Sirbu and Heather J Ruskin and Martin Crane}
}
@conference {sebd10,
	title = {Discovering Eras in Evolving Social Networks (Extended Abstract)},
	booktitle = {SEBD},
	year = {2010},
	pages = {78-85},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@conference {TrasartiRPNM10,
	title = {Exploring Real Mobility Data with M-Atlas},
	booktitle = {ECML/PKDD (3)},
	year = {2010},
	pages = {624-627},
	abstract = {Research 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.},
	doi = {10.1007/978-3-642-15939-8_48},
	author = {Roberto Trasarti and S Rinzivillo and Fabio Pinelli and Mirco Nanni and Anna Monreale and Chiara Renso and Dino Pedreschi and Fosca Giannotti}
}
@proceedings {337,
	title = {A Generalisation-based Approach to Anonymising Movement Data},
	year = {2010},
	abstract = {The 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. },
	issn = {978-989-20-1953-6},
	url = {http://agile2010.dsi.uminho.pt/pen/ShortPapers_PDF\%5C122_DOC.pdf},
	author = {Gennady Andrienko and Natalia Andrienko and Fosca Giannotti and Anna Monreale and Dino Pedreschi and S Rinzivillo}
}
@article {473,
	title = {Improving the Business Plan Evaluation Process: the Role of Intangibles},
	journal = {Quality Technology \& Quantitative Management},
	volume = {7},
	number = {1},
	year = {2010},
	month = {2010},
	chapter = {35},
	issn = {1684-3703},
	url = {http://web.it.nctu.edu.tw/~qtqm/upcomingpapers/2010V7N1/2010V7N1_F3.pdf},
	author = {Barbara Furletti and Franco Turini and Andrea Bellandi and Miriam Baglioni and Chiara Pratesi}
}
@conference {MonrealePTG10,
	title = {Location Prediction through Trajectory Pattern Mining (Extended Abstract)},
	booktitle = {SEBD},
	year = {2010},
	pages = {134-141},
	author = {Anna Monreale and Fabio Pinelli and Roberto Trasarti and Fosca Giannotti}
}
@conference {GiannottiNPPR10,
	title = {Mobility data mining: discovering movement patterns from trajectory data},
	booktitle = {Computational Transportation Science},
	year = {2010},
	pages = {7-10},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@article {572,
	title = {Movement Data Anonymity through Generalization},
	journal = {Transactions on Data Privacy},
	volume = {3},
	number = {2},
	year = {2010},
	pages = {91{\textendash}121},
	abstract = {Wireless 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.},
	url = {http://www.tdp.cat/issues/abs.a045a10.php},
	author = {Anna Monreale and Gennady Andrienko and Natalia Andrienko and Fosca Giannotti and Dino Pedreschi and S Rinzivillo and Stefan Wrobel}
}
@conference {MonrealeTRPB10,
	title = {Preserving privacy in semantic-rich trajectories of human mobility},
	booktitle = {SPRINGL},
	year = {2010},
	pages = {47-54},
	abstract = {The 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.},
	doi = {10.1145/1868470.1868481},
	author = {Anna Monreale and Roberto Trasarti and Chiara Renso and Dino Pedreschi and Vania Bogorny}
}
@conference {NanniT10,
	title = {Querying and mining trajectories with gaps: a multi-path reconstruction approach (Extended Abstract)},
	booktitle = {SEBD},
	year = {2010},
	pages = {126-133},
	author = {Mirco Nanni and Roberto Trasarti}
}
@article {812,
	title = {Regulatory network modelling: Correlation for structure and parameter optimisation},
	journal = {Proceedings of The IASTED Technology Conferences (International Conference on Computational Bioscience), Cambridge, Massachusetts},
	year = {2010},
	pages = {3473{\textendash}3481},
	doi = {10.2316/P.2010.728-020},
	url = {http://www.actapress.com/Abstract.aspx?paperId=41573},
	author = {Alina Sirbu and Heather J Ruskin and Martin Crane}
}
@inbook {KisilevichMNR10,
	title = {Spatio-temporal clustering},
	booktitle = {Data Mining and Knowledge Discovery Handbook},
	year = {2010},
	pages = {855-874},
	author = {Slava Kisilevich and Florian Mansmann and Mirco Nanni and S Rinzivillo}
}
@conference {Berlm3sn2010,
	title = {Towards Discovery of Eras in Social Networks},
	booktitle = {M3SN 2010 Workshop, in conjunction with ICDE2010},
	year = {2010},
	abstract = {In 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.},
	doi = {10.1109/ICDEW.2010.5452713},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@proceedings {242,
	title = {Anonymous Sequences from Trajectory Data},
	year = {2009},
	edition = {17},
	address = {Camogli, Italy},
	author = {Ruggero G. Pensa and Anna Monreale and Fabio Pinelli and Dino Pedreschi}
}
@article {BonchiGLOPT09,
	title = {A constraint-based querying system for exploratory pattern discovery},
	journal = {Inf. Syst.},
	volume = {34},
	number = {1},
	year = {2009},
	pages = {3-27},
	author = {Francesco Bonchi and Fosca Giannotti and Claudio Lucchese and Salvatore Orlando and Raffaele Perego and Roberto Trasarti}
}
@article {BonchiGLOPT09,
	title = {A constraint-based querying system for exploratory pattern discovery},
	journal = {Inf. Syst.},
	volume = {34},
	number = {1},
	year = {2009},
	pages = {3-27},
	author = {Francesco Bonchi and Fosca Giannotti and Claudio Lucchese and Salvatore Orlando and Raffaele Perego and Roberto Trasarti}
}
@conference {TrasartiBR09,
	title = {DAMSEL: A System for Progressive Querying and Reasoning on Movement Data},
	booktitle = {DEXA Workshops},
	year = {2009},
	pages = {452-456},
	author = {Roberto Trasarti and Miriam Baglioni and Chiara Renso}
}
@article {bookdaniel,
	title = {Developing a Spatial Knowledge Representation for Pedestrian Interactions},
	year = {2009},
	note = {MOVEMENT-AWARE APPLICATIONS FOR SUSTAINABLE MOBILITY: TECHNOLOGIES AND APPROACHES, Monica Wachowicz Editor, IGI Publisher, To appear \subsection{Conferenze e Workshop}},
	author = {Daniel Ornellana, Chiara Renso}
}
@conference {GiannottiPT09,
	title = {Geographic privacy-aware knowledge discovery and delivery},
	booktitle = {EDBT},
	year = {2009},
	pages = {1157-1158},
	author = {Fosca Giannotti and Dino Pedreschi and Yannis Theodoridis}
}
@conference {fet2009,
	title = {GeoPKDD {\textendash} Geographic Privacy-aware Knowledge Discovery},
	booktitle = {The European Future Technologies Conference (FET 2009)},
	year = {2009},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@conference {PedreschiRT09,
	title = {Integrating induction and deduction for finding evidence of discrimination},
	booktitle = {ICAIL},
	year = {2009},
	pages = {157-166},
	author = {Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@conference {NanniT09,
	title = {K-BestMatch Reconstruction and Comparison of Trajectory Data},
	booktitle = {ICDM Workshops},
	year = {2009},
	pages = {610-615},
	author = {Mirco Nanni and Roberto Trasarti}
}
@conference {NanniT09,
	title = {K-BestMatch Reconstruction and Comparison of Trajectory Data},
	booktitle = {ICDM Workshops},
	year = {2009},
	pages = {610-615},
	author = {Mirco Nanni and Roberto Trasarti}
}
@conference {sdmPedreschiRT09,
	title = {Measuring Discrimination in Socially-Sensitive Decision Records},
	booktitle = {SDM},
	year = {2009},
	pages = {581-592},
	author = {Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@inbook {BerlingerioBCGT9,
	title = {Mining Clinical, Immunological, and Genetic Data of Solid Organ Transplantation},
	booktitle = {Biomedical Data and Applications},
	year = {2009},
	pages = {211-236},
	author = {Michele Berlingerio and Francesco Bonchi and Michele Curcio and Fosca Giannotti and Franco Turini}
}
@conference {BerlingerioBBG09,
	title = {Mining Graph Evolution Rules},
	booktitle = {ECML/PKDD 2009},
	year = {2009},
	pages = {115-130},
	address = {Bled, Slovenia},
	author = {Michele Berlingerio and Francesco Bonchi and Bj{\"o}rn Bringmann and Aristides Gionis}
}
@conference {GiannottiNPRT09,
	title = {Mining Mobility Behavior from Trajectory Data},
	booktitle = {CSE (4)},
	year = {2009},
	pages = {948-951},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Chiara Renso and Roberto Trasarti}
}
@conference {sebd09,
	title = {Mining the Information Propagation in a Network},
	booktitle = {SEBD},
	year = {2009},
	pages = {333-340},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {BerlingerioCG09,
	title = {Mining the Information Propagation in a Network},
	booktitle = {SEBD},
	year = {2009},
	pages = {333-340},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {BerlingerioCG09,
	title = {Mining the Temporal Dimension of the Information Propagation},
	booktitle = {IDA},
	year = {2009},
	pages = {237-248},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {ida09,
	title = {Mining the Temporal Dimension of the Information Propagation},
	booktitle = {IDA},
	year = {2009},
	pages = {237-248},
	author = {Michele Berlingerio and Michele Coscia and Fosca Giannotti}
}
@conference {991,
	title = {Movement data anonymity through generalization},
	booktitle = {Proceedings of the 2nd SIGSPATIAL ACM GIS 2009 International Workshop on Security and Privacy in GIS and LBS},
	year = {2009},
	publisher = {ACM},
	organization = {ACM},
	abstract = {In 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.},
	doi = {10.1145/1667502.1667510},
	author = {Gennady Andrienko and Natalia Andrienko and Fosca Giannotti and Anna Monreale and Dino Pedreschi}
}
@conference {TrasartiBG09,
	title = {A new technique for sequential pattern mining under regular expressions},
	booktitle = {SEBD},
	year = {2009},
	pages = {325-332},
	author = {Roberto Trasarti and Francesco Bonchi and Bart Goethals}
}
@mastersthesis {448,
	title = {Ontology Driven Knowledge Discovery},
	year = {2009},
	school = {IMT - Lucca},
	address = {Lucca - Italy},
	author = {Barbara Furletti}
}
@conference {1279,
	title = {Poverty as a Social Condition: a Case Study on a Small Municipality in Tuscany},
	booktitle = {Global Recession: Regional Impacts on Housing, Jobs, Health and Wellbeing},
	year = {2009},
	publisher = {SEAFORD},
	organization = {SEAFORD},
	author = {Tomei, Gabriele and Michela Natilli}
}
@article {book,
	title = {{The Role of a Multi-tier Ontological Framework in Reasoning to Discover Meaningful Patterns of Sustainable Mobility}},
	year = {2009},
	note = {{Geographic Data Mining and Knowledge Discovery, 2nd Edition, to appear}},
	author = {Monica Wachowicz and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Chiara Renso and Arend Ligtenberg}
}
@conference {CosciaGP09,
	title = {Social Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography},
	booktitle = {ASONAM},
	year = {2009},
	pages = {279-283},
	author = {Michele Coscia and Fosca Giannotti and Ruggero G. Pensa}
}
@conference {asonam09,
	title = {Social Network Analysis as Knowledge Discovery Process: A Case Study on Digital Bibliography},
	booktitle = {ASONAM},
	year = {2009},
	pages = {279-283},
	author = {Michele Coscia and Fosca Giannotti and Ruggero G. Pensa}
}
@conference {BerlingerioPNG09,
	title = {Temporal mining for interactive workflow data analysis},
	booktitle = {KDD},
	year = {2009},
	pages = {109-118},
	author = {Michele Berlingerio and Fabio Pinelli and Mirco Nanni and Fosca Giannotti}
}
@conference {BaglioniMRTW09,
	title = {Towards Semantic Interpretation of Movement Behavior},
	booktitle = {AGILE Conf.},
	year = {2009},
	pages = {271-288},
	author = {Miriam Baglioni and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Chiara Renso and Roberto Trasarti and Monica Wachowicz}
}
@conference {BaglioniMRTW09,
	title = {Towards Semantic Interpretation of Movement Behavior},
	booktitle = {AGILE Conf.},
	year = {2009},
	pages = {271-288},
	author = {Miriam Baglioni and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Chiara Renso and Roberto Trasarti and Monica Wachowicz}
}
@conference {DBLP:conf/gis/Gi,
	title = {Trajectory pattern analysis for urban traffic},
	booktitle = {Second International Workshop on Computational Transportation Science},
	year = {2009},
	month = {11/2009},
	pages = {43-47},
	publisher = {ACM},
	organization = {ACM},
	address = {SEATTLE, USA},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli}
}
@conference {AndrienkoARNP09,
	title = {A Visual Analytics Toolkit for Cluster-Based Classification of Mobility Data},
	booktitle = {SSTD},
	year = {2009},
	pages = {432-435},
	author = {Gennady Andrienko and Natalia Andrienko and S Rinzivillo and Mirco Nanni and Dino Pedreschi}
}
@conference {ClusterVAST,
	title = {Visual Cluster Analysis of Large Collections of Trajectories},
	booktitle = {IEEE Visual Analytics Science and Tecnology (VAST 2009)},
	year = {2009},
	publisher = {IEEE Computer Society Press},
	organization = {IEEE Computer Society Press},
	author = {Gennady Andrienko and Natalia Andrienko and S Rinzivillo and Mirco Nanni and Dino Pedreschi and Fosca Giannotti}
}
@proceedings {243,
	title = {WhereNext: a Location Predictor on Trajectory Pattern Mining},
	year = {2009},
	abstract = {The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data.This side eect provides the opportunity for innovative methods that analyse the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. The tree is learned from the Trajectory Patterns that hold a certain area and it may be used as a predictor of the next location of a new trajectory finding the best matching path in the tree. Three dierent best matching methods to classify a new moving object are proposed and their impact on the quality of prediction is studied extensively. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends on the movement of all available objects in a certain area instead of on the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties that have emerged from the data and this allows us to define matching methods that striclty depend on the properties of such movements. In addition, we propose a set of other measures, that evaluate a priori the predictive power of a set of Trajectory Patterns. This measures were tuned on a real life case study. Finally, an exhaustive set of experiments and results on the real dataset are presented.},
	doi = {10.1145/1557019.1557091},
	author = {Anna Monreale and Fabio Pinelli and Roberto Trasarti and Fosca Giannotti}
}
@article {1270,
	title = {Wine tourism in Italy: New profiles, styles of consumption, ways of touring},
	journal = {Turizam: me{\dj}unarodni znanstveno-stru{\v c}ni {\v c}asopis},
	volume = {57},
	number = {4},
	year = {2009},
	pages = {463{\textendash}475},
	author = {Romano, Maria Francesca and Michela Natilli}
}
@article {DBLP:journals/vldb/AtzoriBGP08,
	title = {Anonymity preserving pattern discovery},
	journal = {VLDB J.},
	volume = {17},
	number = {4},
	year = {2008},
	pages = {703-727},
	author = {Maurizio Atzori and Francesco Bonchi and Fosca Giannotti and Dino Pedreschi}
}
@article {DBLP:journals/geoinformatica/RaffaetaCCGMPRST08,
	title = {An Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology},
	journal = {GeoInformatica},
	volume = {12},
	number = {1},
	year = {2008},
	pages = {37-72},
	author = {Alessandra Raffaet{\`a} and T. Ceccarelli and D. Centeno and Fosca Giannotti and A. Massolo and Christine Parent and Chiara Renso and Stefano Spaccapietra and Franco Turini}
}
@article {geoinfo,
	title = {An Application of Advanced Spatio-Temporal Formalisms to Behavioural Ecology},
	year = {2008},
	note = {Geoinformatica, Volume 12, Number 1 / March,},
	author = {T. Ceccarelli and D. Centeno and Fosca Giannotti and A. Massolo and Christine Parent and Alessandra Raffaet{\`a} and Chiara Renso and Stefano Spaccapietra and Franco Turini}
}
@conference {GrossiRR08,
	title = {A Case Study in Sequential Pattern Mining for IT-Operational Risk},
	booktitle = {ECML/PKDD (1)},
	year = {2008},
	pages = {424-439},
	author = {Valerio Grossi and Andrea Romei and Salvatore Ruggieri}
}
@inbook {630,
	title = {Characterising the Next Generation of Mobile Applications Through a Privacy-Aware Geographic Knowledge Discovery Process},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {39-72},
	publisher = {a Knowledge Discovery vision},
	organization = {a Knowledge Discovery vision},
	address = {Mobility, Privacy, and Geography},
	author = {Monica Wachowicz and Arend Ligtenberg and Chiara Renso and Seda F. G{\"u}rses}
}
@conference {ZandaKGSM08,
	title = {Clustering of German municipalities based on mobility characteristics: an overview of results},
	booktitle = {GIS},
	year = {2008},
	pages = {69},
	author = {Andrea Zanda and Christine K{\"o}rner and Fosca Giannotti and Daniel Schulz and Michael May}
}
@conference {DBLP:conf/sebd/O,
	title = {DAEDALUS: A knowledge discovery analysis framework for movement data},
	booktitle = {SEBD},
	year = {2008},
	pages = {191-198},
	author = {Riccardo Ortale and E Ritacco and N. Pelekisy and Roberto Trasarti and Gianni Costa and Fosca Giannotti and Giuseppe Manco and Chiara Renso and Yannis Theodoridis}
}
@conference {RPTCGMRT08,
	title = {The DAEDALUS framework: progressive querying and mining of movement data},
	booktitle = {GIS},
	year = {2008},
	pages = {52},
	author = {Riccardo Ortale and E Ritacco and Nikos Pelekis and Roberto Trasarti and Gianni Costa and Fosca Giannotti and Giuseppe Manco and Chiara Renso and Yannis Theodoridis}
}
@inbook {472,
	title = {Discovering Strategic Behaviour in Multi- Agent Scenarios by Ontology-Driven Mining},
	booktitle = {Advances in Robotics, Automation and Control},
	year = {2008},
	isbn = {978-953-7619-16-9},
	url = {http://www.intechopen.com/books/advances_in_robotics_automation_and_control/discovering_strategic_behaviors_in_multi-agent_scenarios_by_ontology-driven_mining},
	author = {Davide Bacciu and Andrea Bellandi and Barbara Furletti and Valerio Grossi and Andrea Romei}
}
@conference {PedreschiRT08,
	title = {Discrimination-aware data mining},
	booktitle = {KDD},
	year = {2008},
	pages = {560-568},
	author = {Dino Pedreschi and Salvatore Ruggieri and Franco Turini}
}
@conference {470,
	title = {AN EXTENSIBLE AND INTERACTIVE SOFTWARE AGENT FOR MOBILE DEVICES BASED ON GPS DATA},
	booktitle = {IADIS International Conference Applied Computing},
	year = {2008},
	month = {2008},
	isbn = {978-972-8924-56-0},
	url = {http://www.iadisportal.org/digital-library/mdownload/an-extensible-and-interactive-software-agent-for-mobile-devices-based-on-gps-data},
	author = {Barbara Furletti and Francesco Fornasari and Claudio Montanari}
}
@inbook {RTBKKM08,
	title = {Knowledge Discovery from Geographical Data},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {243-265},
	author = {S Rinzivillo and Franco Turini and Vania Bogorny and Christine K{\"o}rner and Bart Kuijpers and Michael May}
}
@proceedings {241,
	title = {Location prediction within the mobility data analysis environment Daedalus},
	year = {2008},
	address = {Dublin, Ireland},
	abstract = {In this paper we propose a method to predict the next location of a moving object based on two recent results in GeoPKDD project: DAEDALUS, a mobility data analysis environment and Trajectory Pattern, a sequential pattern mining algorithm with temporal annotation integrated in DAEDALUS. The first one is a DMQL environment for moving objects, where both data and patterns can be represented. The second one extracts movement patterns as sequences of movements between locations with typical travel times.

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

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

Others proposals in literature base the definition of prediction methods for future location of a moving object on previously extracted frequent patterns. They use the recent history of movements of the object itself and often use time only to order the events. Our work uses the movements of all moving objects in a certain area to learn a classifier built on the mined trajectory patterns, which are intrinsically equipped with temporal information.},
	doi = {10.4108/ICST.MOBIQUITOUS2008.3894},
	author = {Fabio Pinelli and Anna Monreale and Roberto Trasarti and Fosca Giannotti}
}
@inbook {GiannottiP08,
	title = {Mobility, Data Mining and Privacy: A Vision of Convergence},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {1-11},
	author = {Fosca Giannotti and Dino Pedreschi}
}
@book {2008mdmp,
	title = {Mobility, Data Mining and Privacy - Geographic Knowledge Discovery},
	series = {Mobility, Data Mining and Privacy},
	year = {2008},
	publisher = {Springer},
	organization = {Springer},
	isbn = {978-3-540-75176-2},
	author = {Fosca Giannotti and Dino Pedreschi},
	editor = {Fosca Giannotti and Dino Pedreschi}
}
@conference {GiannottiPT08,
	title = {Mobility, Data Mining and Privacy the Experience of the GeoPKDD Project},
	booktitle = {PinKDD},
	year = {2008},
	pages = {25-32},
	author = {Fosca Giannotti and Dino Pedreschi and Franco Turini}
}
@conference {469,
	title = {Ontological Support for Association Rule Mining},
	booktitle = {IASTED International Conference on Artificial Intelligence and Applications (AIA)},
	year = {2008},
	address = {Innsbruck, Austria },
	author = {Barbara Furletti and Andrea Bellandi and Valerio Grossi and Andrea Romei}
}
@conference {BaglioniMRW08,
	title = {An Ontology-Based Approach for the Semantic Modelling and Reasoning on Trajectories},
	booktitle = {ER Workshops},
	year = {2008},
	pages = {344-353},
	author = {Miriam Baglioni and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Chiara Renso and Monica Wachowicz}
}
@conference {BaglioniBFST08,
	title = {Ontology-Based Business Plan Classification},
	booktitle = {EDOC},
	year = {2008},
	pages = {365-371},
	author = {Miriam Baglioni and Andrea Bellandi and Barbara Furletti and Laura Spinsanti and Franco Turini}
}
@conference {471,
	title = {Ontology-Based Business Plan Classification},
	booktitle = {Enterprise Distributed Object Computing Conference (EDOC)},
	year = {2008},
	month = {2008},
	isbn = {978-0-7695-3373-5},
	doi = {http://dx.doi.org/10.1109/EDOC.2008.30},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=4634789},
	author = {Franco Turini and Barbara Furletti and Miriam Baglioni and Laura Spinsanti and Andrea Bellandi}
}
@article {GeoS,
	title = {Ontology-driven Querying of Geographical Databases},
	year = {2008},
	note = {Transactions in GIS Volume 12, Issue s1, Date: December Pages:\subsection{Capitoli Libri}},
	pages = {31{\textendash}44},
	author = {Miriam Baglioni and E. Giovannetti and Maria V Masserotti and Chiara Renso and Laura Spinsanti}
}
@conference {DBLP:conf/esoric,
	title = {Pattern-Preserving k-Anonymization of Sequences and its Application to Mobility Data Mining},
	booktitle = {PiLBA},
	year = {2008},
	abstract = {Sequential 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{\textquoteright} and customers{\textquoteright} behavior. However, this puts the citizen{\textquoteright}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.},
	url = {https://air.unimi.it/retrieve/handle/2434/52786/106397/ProceedingsPiLBA08.pdf$\#$page=44},
	author = {Ruggero G. Pensa and Anna Monreale and Fabio Pinelli and Dino Pedreschi}
}
@inbook {PedreschiBTVAMMS,
	title = {Privacy Protection: Regulations and Technologies, Opportunities and Threats},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {101-119},
	author = {Dino Pedreschi and Francesco Bonchi and Franco Turini and Vassilios S. Verykios and Maurizio Atzori and Bradley Malin and Bart Moelans and Y{\"u}cel Saygin}
}
@inbook {MancoBGKRR08,
	title = {Querying and Reasoning for Spatiotemporal Data Mining},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {335-374},
	author = {Giuseppe Manco and Miriam Baglioni and Fosca Giannotti and Bart Kuijpers and Alessandra Raffaet{\`a} and Chiara Renso}
}
@inbook {Ch12,
	title = {Querying and Reasoning for Spatio-Temporal Data Mining},
	year = {2008},
	note = {, A Springer LNCS Monograph Fosca Giannotti and Dino Pedreschi, Editors, January},
	publisher = {a Knowledge Discovery vision},
	organization = {a Knowledge Discovery vision},
	address = {Mobility, Privacy, and Geography},
	author = {Giuseppe Manco and Miriam Baglioni and Fosca Giannotti and Bart Kuijpers and Alessandra Raffaet{\`a} and Chiara Renso}
}
@inbook {NanniKKMP08,
	title = {Spatiotemporal Data Mining},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {267-296},
	author = {Mirco Nanni and Bart Kuijpers and Christine K{\"o}rner and Michael May and Dino Pedreschi}
}
@conference {DBLP:conf/sebd/BerlingerioGNP08,
	title = {Temporal analysis of process logs: a case study},
	booktitle = {SEBD},
	year = {2008},
	pages = {430-437},
	author = {Michele Berlingerio and Fosca Giannotti and Mirco Nanni and Fabio Pinelli}
}
@conference {RuggieriM08,
	title = {Typing Linear Constraints for Moding CLP() Programs},
	booktitle = {SAS},
	year = {2008},
	pages = {128-143},
	author = {Salvatore Ruggieri and Fr{\'e}d{\'e}ric Mesnard}
}
@article {IV2008,
	title = {Visually driven analysis of movement data by progressive clustering},
	journal = {Information Visualization},
	volume = {7},
	number = {3-4},
	year = {2008},
	pages = {225-239},
	publisher = {Palgrave Macmillan Ltd},
	author = {S Rinzivillo and Dino Pedreschi and Mirco Nanni and Fosca Giannotti and Natalia Andrienko and Gennady Andrienko}
}
@inbook {629,
	title = {Wireless Network Data Sources: Tracking and Synthesizing Trajectories},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {73-100},
	author = {Chiara Renso and Simone Puntoni and E. Frentzos and Andrea Mazzoni and Bart Moelans and Nikos Pelekis and F. Pini}
}
@conference {DBLP:conf/geos/BaglioniMRS07,
	title = {Building Geospatial Ontologies from Geographical Databases},
	booktitle = {GeoS},
	year = {2007},
	pages = {195-209},
	author = {Miriam Baglioni and Maria V Masserotti and Chiara Renso and Laura Spinsanti}
}
@conference {DBLP:conf/icdm/AbulABG07,
	title = {Hiding Sensitive Trajectory Patterns},
	booktitle = {ICDM Workshops},
	year = {2007},
	pages = {693-698},
	author = {Osman Abul and Maurizio Atzori and Francesco Bonchi and Fosca Giannotti}
}
@conference {DBLP:conf/sebd/AbulABG07,
	title = {Hiding Sequences},
	booktitle = {SEBD},
	year = {2007},
	pages = {233-241},
	author = {Osman Abul and Maurizio Atzori and Francesco Bonchi and Fosca Giannotti}
}
@conference {DBLP:conf/icde/AbulABG07,
	title = {Hiding Sequences},
	booktitle = {ICDE Workshops},
	year = {2007},
	pages = {147-156},
	author = {Osman Abul and Maurizio Atzori and Francesco Bonchi and Fosca Giannotti}
}
@article {RT07,
	title = {Knowledge discovery from spatial transactions},
	journal = {Journal of Intelligent Information Systems},
	volume = {28},
	number = {1},
	year = {2007},
	pages = {1-22},
	author = {S Rinzivillo and Franco Turini}
}
@conference {DBLP:conf/bibm/BerlingerioBGT07,
	title = {Mining Clinical Data with a Temporal Dimension: A Case Study},
	booktitle = {BIBM},
	year = {2007},
	pages = {429-436},
	author = {Michele Berlingerio and Francesco Bonchi and Fosca Giannotti and Franco Turini}
}
@conference {468,
	title = {Ontology-Driven Association Rule Extraction: A Case Study},
	booktitle = {International Workshop on Contexts and Ontologies: Representation and Reasoning},
	year = {2007},
	month = {2007},
	address = {Roskilde, Denmark},
	url = {http://ceur-ws.org/Vol-298/paper1.pdf},
	author = {Barbara Furletti and Andrea Bellandi and Valerio Grossi and Andrea Romei}
}
@conference {DBLP:conf/mdm/AtzoriBGPA07,
	title = {Privacy-Aware Knowledge Discovery from Location Data},
	booktitle = {MDM},
	year = {2007},
	pages = {283-287},
	author = {Maurizio Atzori and Francesco Bonchi and Fosca Giannotti and Dino Pedreschi and Osman Abul}
}
@conference {467,
	title = {PUSHING CONSTRAINTS IN ASSOCIATION RULE MINING: AN ONTOLOGY-BASED APPROACH },
	booktitle = { IADIS International Conference WWW/Internet 2007},
	year = {2007},
	month = {2007},
	isbn = {978-972-8924-44-7},
	url = {http://www.iadisportal.org/digital-library/mdownload/pushing-constraints-in-association-rule-mining-an-ontology-based-approach},
	author = {Barbara Furletti and Andrea Bellandi and Andrea Romei and Valerio Grossi}
}
@conference {DBLP:conf/icdm/BerlingerioBGT07,
	title = {Time-Annotated Sequences for Medical Data Mining},
	booktitle = {ICDM Workshops},
	year = {2007},
	pages = {133-138},
	author = {Michele Berlingerio and Francesco Bonchi and Fosca Giannotti and Franco Turini}
}
@conference {DBLP:conf/sebd/BerlingerioBG07,
	title = {Towards Constraint-Based Subgraph Mining},
	booktitle = {SEBD},
	year = {2007},
	pages = {274-281},
	author = {Michele Berlingerio and Francesco Bonchi and Fosca Giannotti}
}
@conference {DBLP:conf/kdd/GiannottiNPP07,
	title = {Trajectory pattern mining},
	booktitle = {KDD},
	year = {2007},
	pages = {330-339},
	author = {Fosca Giannotti and Mirco Nanni and Fabio Pinelli and Dino Pedreschi}
}
@conference {DBLP:conf/icde/BonchiGLOPT06,
	title = {ConQueSt: a Constraint-based Querying System for Exploratory Pattern Discovery},
	booktitle = {ICDE},
	year = {2006},
	pages = {159},
	author = {Francesco Bonchi and Fosca Giannotti and Claudio Lucchese and Salvatore Orlando and Raffaele Perego and Roberto Trasarti}
}
@conference {DBLP:conf/sdm/GiannottiNP06,
	title = {Efficient Mining of Temporally Annotated Sequences},
	booktitle = {SDM},
	year = {2006},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi}
}
@conference {TuriniBFR06,
	title = {Examples of Integration of Induction and Deduction in Knowledge Discovery},
	booktitle = {Reasoning, Action and Interaction in AI Theories and Systems},
	year = {2006},
	pages = {307-326},
	author = {Franco Turini and Miriam Baglioni and Barbara Furletti and S Rinzivillo}
}
@inbook {466,
	title = {Examples of Integration of Induction and Deduction in Knowledge Discovery},
	booktitle = {Reasoning, Action and Interaction in AI Theories and Systems},
	series = {LNAI},
	volume = {4155},
	year = {2006},
	pages = {307-326},
	doi = {10.1007/11829263_17},
	url = {http://www.springerlink.com/content/m400v4507476n18g/fulltext.pdf},
	author = {Franco Turini and Miriam Baglioni and Barbara Furletti and S Rinzivillo}
}
@conference {DBLP:conf/sebd/LuccheseBGOPT06,
	title = {On Interactive Pattern Mining from Relational Databases},
	booktitle = {SEBD},
	year = {2006},
	pages = {329-338},
	author = {Claudio Lucchese and Francesco Bonchi and Fosca Giannotti and Salvatore Orlando and Raffaele Perego and Roberto Trasarti}
}
@conference {DBLP:conf/kdid/BonchiGLOPT06,
	title = {On Interactive Pattern Mining from Relational Databases},
	booktitle = {KDID},
	year = {2006},
	pages = {42-62},
	author = {Francesco Bonchi and Fosca Giannotti and Claudio Lucchese and Salvatore Orlando and Raffaele Perego and Roberto Trasarti}
}
@inbook {flexible,
	title = {Maximum Entropy Reasoning for GIS},
	year = {2006},
	note = {Flexible Databases supporting Imprecision and Uncertainty, Physica Verlag, June},
	author = {H. Hosni and Maria V Masserotti and Chiara Renso}
}
@conference {DBLP:conf/cbms/BerlingerioBCCGS06,
	title = {Mining HLA Patterns Explaining Liver Diseases},
	booktitle = {CBMS},
	year = {2006},
	pages = {702-707},
	author = {Michele Berlingerio and Francesco Bonchi and Silvia Chelazzi and Michele Curcio and Fosca Giannotti and Fabrizio Scatena}
}
@conference {DBLP:conf/sac/GiannottiNPP06,
	title = {Mining sequences with temporal annotations},
	booktitle = {SAC},
	year = {2006},
	pages = {593-597},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli}
}
@article {DBLP:journals/jiis/NanniP06,
	title = {Time-focused clustering of trajectories of moving objects},
	journal = {J. Intell. Inf. Syst.},
	volume = {27},
	number = {3},
	year = {2006},
	pages = {267-289},
	author = {Mirco Nanni and Dino Pedreschi}
}
@conference {465,
	title = {A Tool for Economic Plans analysis based on expert knowledge and data mining techniques},
	booktitle = { IADIS International Conference Applied Computing 2006 },
	year = {2006},
	month = {2006},
	isbn = {972-8924-09-7},
	url = {http://www.iadisportal.org/digital-library/mdownload/a-tool-for-economic-plans-analysis-based-on-expert-knowledge-and-data-mining-techniques},
	author = {Miriam Baglioni and Barbara Furletti and Franco Turini}
}
@conference {DBLP:conf/sac/AtzoriBGP06,
	title = {Towards low-perturbation anonymity preserving pattern discovery},
	booktitle = {SAC},
	year = {2006},
	pages = {588-592},
	author = {Maurizio Atzori and Francesco Bonchi and Fosca Giannotti and Dino Pedreschi}
}
@article {DBLP:journals/csse/AtzoriBGP05,
	title = {Anonymity and data mining},
	journal = {Comput. Syst. Sci. Eng.},
	volume = {20},
	number = {5},
	year = {2005},
	author = {Maurizio Atzori and Francesco Bonchi and Fosca Giannotti and Dino Pedreschi}
}
@conference {DBLP:conf/icdm/AtzoriBGP05,
	title = {Blocking Anonymity Threats Raised by Frequent Itemset Mining},
	booktitle = {ICDM},
	year = {2005},
	pages = {561-564},
	author = {Maurizio Atzori and Francesco Bonchi and Fosca Giannotti and Dino Pedreschi}
}
@conference {1280,
	title = {Comparative indicators of regional poverty and deprivation: Poland versus EU-15 Member States},
	booktitle = {conference Comparative Economic Analysis of Households" Behaviour (CEAHB): Old and New EU Members, Warsaw University},
	year = {2005},
	author = {Betti, Gianni and Mulas, Anna and Michela Natilli and Neri, Laura and Verma, Vijay}
}
@conference {464,
	title = {DrC4.5: Improving C4.5 by means of Prior Knowledge},
	booktitle = {ACM Symposium on Applied Computing},
	year = {2005},
	publisher = {ACM},
	organization = {ACM},
	address = {Santa Fe, New Mexico, USA},
	isbn = {1-58113-964-0},
	doi = {http://dx.doi.org/10.1145/1066677.1066787},
	url = {http://dl.acm.org/ft_gateway.cfm?id=1066787\&ftid=311609\&dwn=1\&CFID=96873366\&CFTOKEN=59233511},
	author = {Miriam Baglioni and Barbara Furletti and Franco Turini}
}
@article {DBLP:journals/kais/BonchiGMP05,
	title = {Efficient breadth-first mining of frequent pattern with monotone constraints},
	journal = {Knowl. Inf. Syst.},
	volume = {8},
	number = {2},
	year = {2005},
	pages = {131-153},
	author = {Francesco Bonchi and Fosca Giannotti and Alessio Mazzanti and Dino Pedreschi}
}
@article {DBLP:journals/expert/BonchiGMP05,
	title = {Exante: A Preprocessing Method for Frequent-Pattern Mining},
	journal = {IEEE Intelligent Systems},
	volume = {20},
	number = {3},
	year = {2005},
	pages = {25-31},
	author = {Francesco Bonchi and Fosca Giannotti and Alessio Mazzanti and Dino Pedreschi}
}
@conference {RinzivilloT05,
	title = {Extracting spatial association rules from spatial transactions},
	booktitle = {ACM GIS},
	year = {2005},
	pages = {79-86},
	author = {S Rinzivillo and Franco Turini}
}
@booklet {1271,
	title = {Indicators of social exclusion and poverty in Europe{\textquoteright}s regions},
	year = {2005},
	author = {Verma, Vijay and Betti, Gianni and Michela Natilli and Lemmi, Achille}
}
@conference {DBLP:conf/gis/GiannottiMPR05,
	title = {Synthetic generation of cellular network positioning data},
	booktitle = {GIS},
	year = {2005},
	pages = {12-20},
	author = {Fosca Giannotti and Andrea Mazzoni and Simone Puntoni and Chiara Renso}
}
@conference {DBLP:conf/gis/GiannottiMPR05,
	title = {Synthetic generation of cellular network positioning data},
	booktitle = {GIS},
	year = {2005},
	pages = {12-20},
	author = {Fosca Giannotti and Andrea Mazzoni and Simone Puntoni and Chiara Renso}
}
@article {DBLP:journals/amai/PedreschiR04,
	title = {Bounded Nondeterminism of Logic Programs},
	journal = {Ann. Math. Artif. Intell.},
	volume = {42},
	number = {4},
	year = {2004},
	pages = {313-343},
	author = {Dino Pedreschi and Salvatore Ruggieri}
}
@conference {DBLP:conf/lopstr/PedreschiRS04,
	title = {Characterisations of Termination in Logic Programming},
	booktitle = {Program Development in Computational Logic},
	year = {2004},
	pages = {376-431},
	author = {Dino Pedreschi and Salvatore Ruggieri and Jan-Georg Smaus}
}
@conference {RinzivilloT04,
	title = {Classification in Geographical Information Systems},
	booktitle = {PKDD},
	year = {2004},
	pages = {374-385},
	author = {S Rinzivillo and Franco Turini}
}
@conference {DBLP:conf/wlp/NanniRRT04,
	title = {Deductive and Inductive Reasoning on Spatio-Temporal Data},
	booktitle = {INAP/WLP},
	year = {2004},
	pages = {98-115},
	author = {Mirco Nanni and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/sebd/NanniRRT04,
	title = {Deductive and Inductive Reasoning on Trajectories},
	booktitle = {SEBD},
	year = {2004},
	pages = {98-105},
	author = {Mirco Nanni and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/dmkd/BacarellaGNP04,
	title = {Discovery of ads web hosts through traffic data analysis},
	booktitle = {DMKD},
	year = {2004},
	pages = {76-81},
	author = {V. Bacarella and Fosca Giannotti and Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/sebd/BoschiGP04,
	title = {Frequent Pattern Queries for Flexible Knowledge Discovery},
	booktitle = {SEBD},
	year = {2004},
	pages = {250-261},
	author = {Francesco Bonchi and Fosca Giannotti and Dino Pedreschi}
}
@article {MRRT03,
	title = {Integrating Knowledge Representation and Reasoning in Geographical},
	year = {2004},
	note = {information systems. {\em International Journal of GIS,Vol 18 (4), June }.},
	author = {Paolo Mancarella and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@article {DBLP:journals/gis/MancarellaRRT04,
	title = {Integrating knowledge representation and reasoning in Geographical Information Systems},
	journal = {International Journal of Geographical Information Science},
	volume = {18},
	number = {4},
	year = {2004},
	pages = {417-447},
	author = {Paolo Mancarella and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/iticse/AlfonsiSPS04,
	title = {IT4PS: information technology for problem solving},
	booktitle = {ITiCSE},
	year = {2004},
	pages = {241},
	author = {C. Alfonsi and Nello Scarabottolo and Dino Pedreschi and Maria Simi}
}
@proceedings {DBLP:conf/pkdd/2004,
	title = {Knowledge Discovery in Databases: PKDD 2004, 8th European Conference on Principles and Practice of Knowledge Discovery in Databases, Pisa, Italy, September 20-24, 2004, Proceedings},
	volume = {3202},
	year = {2004},
	publisher = {Springer},
	isbn = {3-540-23108-0},
	author = {Jean-Fran{\c c}ois Boulicaut and Floriana Esposito and Fosca Giannotti and Dino Pedreschi}
}
@proceedings {DBLP:conf/ecml/2004,
	title = {Machine Learning: ECML 2004, 15th European Conference on Machine Learning, Pisa, Italy, September 20-24, 2004, Proceedings},
	volume = {3201},
	year = {2004},
	publisher = {Springer},
	isbn = {3-540-23105-6},
	author = {Jean-Fran{\c c}ois Boulicaut and Floriana Esposito and Fosca Giannotti and Dino Pedreschi}
}
@article {NRRT04,
	title = {\newblock{A Declarative Framework for Reasoning on Spatio-temporal Data}},
	year = {2004},
	note = {\newblock{Book chapter in Spatio-temporal databases, flexible querying and reasoning, R. de Caluwe, G. de Tr{\`e}, G. Bordogna editors, Physica Verlag }.},
	author = {Mirco Nanni and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/dagstuhl/BonchiG04,
	title = {Pushing Constraints to Detect Local Patterns},
	booktitle = {Local Pattern Detection},
	year = {2004},
	pages = {1-19},
	author = {Francesco Bonchi and Fosca Giannotti}
}
@conference {DBLP:conf/cinq/BonchiGP04,
	title = {A Relational Query Primitive for Constraint-Based Pattern Mining},
	booktitle = {Constraint-Based Mining and Inductive Databases},
	year = {2004},
	pages = {14-37},
	author = {Francesco Bonchi and Fosca Giannotti and Dino Pedreschi}
}
@article {DBLP:journals/tkde/GiannottiMT04,
	title = {Specifying Mining Algorithms with Iterative User-Defined Aggregates},
	journal = {IEEE Trans. Knowl. Data Eng.},
	volume = {16},
	number = {10},
	year = {2004},
	pages = {1232-1246},
	author = {Fosca Giannotti and Giuseppe Manco and Franco Turini}
}
@conference {DBLP:conf/cinq/GiannottiMT04,
	title = {Towards a Logic Query Language for Data Mining},
	booktitle = {Database Support for Data Mining Applications},
	year = {2004},
	pages = {76-94},
	author = {Fosca Giannotti and Giuseppe Manco and Franco Turini}
}
@conference {DBLP:conf/pkdd/BonchiGMP03,
	title = {Adaptive Constraint Pushing in Frequent Pattern Mining},
	booktitle = {PKDD},
	year = {2003},
	pages = {47-58},
	author = {Francesco Bonchi and Fosca Giannotti and Alessio Mazzanti and Dino Pedreschi}
}
@conference {DBLP:conf/icdm/BonchiGMP03,
	title = {ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraint},
	booktitle = {ICDM},
	year = {2003},
	pages = {11-18},
	author = {Francesco Bonchi and Fosca Giannotti and Alessio Mazzanti and Dino Pedreschi}
}
@conference {DBLP:conf/pkdd/BonchiGMP03a,
	title = {ExAnte: Anticipated Data Reduction in Constrained Pattern Mining},
	booktitle = {PKDD},
	year = {2003},
	pages = {59-70},
	author = {Francesco Bonchi and Fosca Giannotti and Alessio Mazzanti and Dino Pedreschi}
}
@article {DBLP:journals/scp/PedreschiR03,
	title = {On logic programs that always succeed},
	journal = {Sci. Comput. Program.},
	volume = {48},
	number = {2-3},
	year = {2003},
	pages = {163-196},
	author = {Dino Pedreschi and Salvatore Ruggieri}
}
@conference {DBLP:conf/dagstuhl/GiannottiMW03,
	title = {Logical Languages for Data Mining},
	booktitle = {Logics for Emerging Applications of Databases},
	year = {2003},
	pages = {325-361},
	author = {Fosca Giannotti and Giuseppe Manco and Jef Wijsen}
}
@conference {1281,
	title = {Personal income in the gross and net forms: applications of the Siena Micro-Simulation Model (SM2)},
	booktitle = {conference of the Societ{\`a} Italiana di Economia, Demografia e Statistica (SIEDS), Campobasso},
	year = {2003},
	author = {Verma, V and Betti, G and Ballini, F and Michela Natilli and Galgani, S}
}
@conference {DBLP:conf/sebd/BonchiGMP03,
	title = {Pre-processing for Constrained Pattern Mining},
	booktitle = {SEBD},
	year = {2003},
	pages = {519-530},
	author = {Francesco Bonchi and Fosca Giannotti and Alessio Mazzanti and Dino Pedreschi}
}
@conference {DBLP:conf/aiia/RaffaetaRT03,
	title = {Qualitative Spatial Reasoning in a Logical Framework},
	booktitle = {AI*IA},
	year = {2003},
	pages = {78-90},
	author = {Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {GargantiniRR03,
	title = {Using Spin to Generate Tests from ASM Specifications},
	booktitle = {Abstract State Machines},
	year = {2003},
	pages = {263-277},
	author = {Angelo Gargantini and Elvinia Riccobene and S Rinzivillo}
}
@conference {DBLP:conf/sebd/GiannottiNPS03,
	title = {WebCat: Automatic Categorization of Web Search Results},
	booktitle = {SEBD},
	year = {2003},
	pages = {507-518},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and F. Samaritani}
}
@conference {DBLP:conf/itcc/GiannottiGM02,
	title = {Characterizing Web User Accesses: A Transactional Approach to Web Log Clustering},
	booktitle = {ITCC},
	year = {2002},
	pages = {312},
	author = {Fosca Giannotti and Cristian Gozzi and Giuseppe Manco}
}
@article {DBLP:journals/tplp/PedreschiRS02,
	title = {Classes of terminating logic programs},
	journal = {TPLP},
	volume = {2},
	number = {3},
	year = {2002},
	pages = {369-418},
	author = {Dino Pedreschi and Salvatore Ruggieri and Jan-Georg Smaus}
}
@conference {DBLP:conf/pkdd/GiannottiGM02,
	title = {Clustering Transactional Data},
	booktitle = {PKDD},
	year = {2002},
	pages = {175-187},
	author = {Fosca Giannotti and Cristian Gozzi and Giuseppe Manco}
}
@conference {DBLP:conf/birthday/MascellaniP02,
	title = {The Declarative Side of Magic},
	booktitle = {Computational Logic: Logic Programming and Beyond},
	year = {2002},
	pages = {83-108},
	author = {Paolo Mascellani and Dino Pedreschi}
}
@conference {DBLP:conf/gis/RaffaetaTR02,
	title = {Enhancing GISs for spatio-temporal reasoning},
	booktitle = {ACM-GIS},
	year = {2002},
	pages = {42-48},
	author = {Alessandra Raffaet{\`a} and Franco Turini and Chiara Renso}
}
@conference {DBLP:conf/kdid/Giannotti02,
	title = {Invited talk: Logical Data Mining Query Languages},
	booktitle = {KDID},
	year = {2002},
	pages = {1},
	author = {Fosca Giannotti}
}
@conference {DBLP:conf/jelia/GiannottiM02,
	title = {LDL-M$_{\mbox{ine}}$: Integrating Data Mining with Intelligent Query Answering},
	booktitle = {JELIA},
	year = {2002},
	pages = {517-520},
	author = {Fosca Giannotti and Giuseppe Manco}
}
@conference {DBLP:conf/birthday/MancarellaPR02,
	title = {Negation as Failure through Abduction: Reasoning about Termination},
	booktitle = {Computational Logic: Logic Programming and Beyond},
	year = {2002},
	pages = {240-272},
	author = {Paolo Mancarella and Dino Pedreschi and Salvatore Ruggieri}
}
@conference {DBLP:conf/sebd/RaffaeteaRT02,
	title = {Qualitative Reasoning in a Spatio-Temporal Language},
	booktitle = {SEBD},
	year = {2002},
	pages = {105-118},
	author = {Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@article {DBLP:journals/corr/cs-LO-0106050,
	title = {Classes of Terminating Logic Programs},
	journal = {CoRR},
	volume = {cs.LO/0106},
	year = {2001},
	author = {Dino Pedreschi and Salvatore Ruggieri and Jan-Georg Smaus}
}
@conference {DBLP:conf/sebd/GiannottiGM01,
	title = {Clustering Transactional Data},
	booktitle = {SEBD},
	year = {2001},
	pages = {163-176},
	author = {Fosca Giannotti and Cristian Gozzi and Giuseppe Manco}
}
@conference {DBLP:conf/sebd/GiannottiRRT01,
	title = {Complex Reasoning on Geographical Data},
	booktitle = {SEBD},
	year = {2001},
	pages = {331-338},
	author = {Fosca Giannotti and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/sebd/GiannottiRRT01,
	title = {Complex Reasoning on Geographical Data},
	booktitle = {SEBD},
	year = {2001},
	pages = {331-338},
	author = {Fosca Giannotti and Alessandra Raffaet{\`a} and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/itcc/BonchiGMRNPR01,
	title = {Data Mining for Intelligent Web Caching},
	booktitle = {ITCC},
	year = {2001},
	pages = {599-603},
	author = {Francesco Bonchi and Fosca Giannotti and Giuseppe Manco and Chiara Renso and Mirco Nanni and Dino Pedreschi and Salvatore Ruggieri}
}
@conference {DBLP:conf/itcc/BonchiGMRNPR01,
	title = {Data Mining for Intelligent Web Caching},
	booktitle = {ITCC},
	year = {2001},
	pages = {599-603},
	author = {Francesco Bonchi and Fosca Giannotti and Giuseppe Manco and Chiara Renso and Mirco Nanni and Dino Pedreschi and Salvatore Ruggieri}
}
@article {DBLP:journals/tkde/GiannottiMNP01,
	title = {Nondeterministic, Nonmonotonic Logic Databases},
	journal = {IEEE Trans. Knowl. Data Eng.},
	volume = {13},
	number = {5},
	year = {2001},
	pages = {813-823},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
@article {DBLP:journals/jcss/GiannottiPZ01,
	title = {Semantics and Expressive Power of Nondeterministic Constructs in Deductive Databases},
	journal = {J. Comput. Syst. Sci.},
	volume = {62},
	number = {1},
	year = {2001},
	pages = {15-42},
	author = {Fosca Giannotti and Dino Pedreschi and Carlo Zaniolo}
}
@conference {DBLP:conf/pkdd/GiannottiMT01,
	title = {Specifying Mining Algorithms with Iterative User-Defined Aggregates: A Case Study},
	booktitle = {PKDD},
	year = {2001},
	pages = {128-139},
	author = {Fosca Giannotti and Giuseppe Manco and Franco Turini}
}
@article {DBLP:journals/dke/BonchiGGMNPRR01,
	title = {Web log data warehousing and mining for intelligent web caching},
	journal = {Data Knowl. Eng.},
	volume = {39},
	number = {2},
	year = {2001},
	pages = {165-189},
	author = {Francesco Bonchi and Fosca Giannotti and Cristian Gozzi and Giuseppe Manco and Mirco Nanni and Dino Pedreschi and Chiara Renso and Salvatore Ruggieri}
}
@article {DBLP:journals/dke/BonchiGGMNPRR01,
	title = {Web log data warehousing and mining for intelligent web caching},
	journal = {Data Knowl. Eng.},
	volume = {39},
	number = {2},
	year = {2001},
	pages = {165-189},
	author = {Francesco Bonchi and Fosca Giannotti and Cristian Gozzi and Giuseppe Manco and Mirco Nanni and Dino Pedreschi and Chiara Renso and Salvatore Ruggieri}
}
@article {BGGMNPRR01,
	title = {Web Log Data Warehousing and Mining for Intelligent Web Caching},
	journal = {Data and Knowledge Engineering},
	year = {2001},
	note = {39:165, November .},
	author = {Francesco Bonchi and Fosca Giannotti and Cristian Gozzi and Giuseppe Manco and Mirco Nanni and Dino Pedreschi and Chiara Renso and Salvatore Ruggieri}
}
@conference {DBLP:conf/fqas/GiannottiM00,
	title = {Declarative Knowledge Extraction with Interactive User-Defined Aggregates},
	booktitle = {FQAS},
	year = {2000},
	pages = {435-444},
	author = {Fosca Giannotti and Giuseppe Manco}
}
@article {DBLP:journals/amai/FayzullinNPS00,
	title = {Foundations of distributed interaction systems},
	journal = {Ann. Math. Artif. Intell.},
	volume = {28},
	number = {1-4},
	year = {2000},
	pages = {127-168},
	author = {Marat Fayzullin and Mirco Nanni and Dino Pedreschi and V. S. Subrahmanian}
}
@conference {DBLP:conf/ejc/GiannottiNP00,
	title = {Logic-Based Knowledge Discovery in Databases},
	booktitle = {EJC},
	year = {2000},
	pages = {279-283},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/pakdd/GiannottiM00,
	title = {Making Knowledge Extraction and Reasoning Closer},
	booktitle = {PAKDD},
	year = {2000},
	pages = {360-371},
	author = {Fosca Giannotti and Giuseppe Manco}
}
@conference {DBLP:conf/dexaw/RaffaetaR00,
	title = {Temporal Reasoning in Geographical Information Systems},
	booktitle = {DEXA Workshop},
	year = {2000},
	pages = {899-905},
	author = {Alessandra Raffaet{\`a} and Chiara Renso}
}
@article {AAFRT99,
	title = {Using Medlan to Integrate Geographical Data},
	journal = {Journal of Logic Programming},
	year = {2000},
	note = {43(1):.},
	pages = {3{\textendash}14},
	author = {Domenico Aquilino and Patrizia Asirelli and A Formuso and Chiara Renso and Franco Turini}
}
@article {DBLP:journals/jlp/AquilinoAFRT00,
	title = {Using MedLan to Integrate Geographical Data},
	journal = {J. Log. Program.},
	volume = {43},
	number = {1},
	year = {2000},
	pages = {3-14},
	author = {Domenico Aquilino and Patrizia Asirelli and A Formuso and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/cl/BonchiGP00,
	title = {On Verification in Logic Database Languages},
	booktitle = {Computational Logic},
	year = {2000},
	pages = {957-971},
	author = {Francesco Bonchi and Fosca Giannotti and Dino Pedreschi}
}
@conference {DBLP:conf/dexaw/GiannottiJT99,
	title = {Beyond Current Technology: The Perspective of Three EC GIS Projects},
	booktitle = {DEXA Workshop},
	year = {1999},
	pages = {510},
	author = {Fosca Giannotti and Robert Jeansoulin and Yannis Theodoridis}
}
@conference {DBLP:conf/iclp/PedreschiR99,
	title = {Bounded Nondeterminism of Logic Programs},
	booktitle = {ICLP},
	year = {1999},
	pages = {350-364},
	author = {Dino Pedreschi and Salvatore Ruggieri}
}
@conference {DBLP:conf/kdd/BonchiGMP99,
	title = {A Classification-Based Methodology for Planning Audit Strategies in Fraud Detection},
	booktitle = {KDD},
	year = {1999},
	pages = {175-184},
	author = {Francesco Bonchi and Fosca Giannotti and Gianni Mainetto and Dino Pedreschi}
}
@article {BRT99,
	title = {Dynamic Composition of Parameterised Logic Modules},
	journal = {Computer Languages},
	year = {1999},
	note = {25(4):.},
	pages = {211{\textendash}242},
	author = {Antonio Brogi and Chiara Renso and Franco Turini}
}
@article {DBLP:journals/cl/BrogiRT99,
	title = {Dynamic composition of parameterised logic modules},
	journal = {Comput. Lang.},
	volume = {25},
	number = {4},
	year = {1999},
	pages = {211-242},
	author = {Antonio Brogi and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/dmkd/GiannottiMPT99,
	title = {Experiences with a Logic-based knowledge discovery Support Environment},
	booktitle = {1999 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery},
	year = {1999},
	author = {Fosca Giannotti and Giuseppe Manco and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/aiia/GiannottiMPT99,
	title = {Experiences with a Logic-Based Knowledge Discovery Support Environment},
	booktitle = {AI*IA},
	year = {1999},
	pages = {202-213},
	author = {Fosca Giannotti and Giuseppe Manco and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/sebd/GiannottiMNPT99,
	title = {Integration of Deduction and Induction for Mining Supermarket Sales Data},
	booktitle = {SEBD},
	year = {1999},
	pages = {117-131},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi and Franco Turini}
}
@article {DBLP:journals/tcs/PedreschiR99,
	title = {On Logic Programs That Do Not Fail},
	journal = {Electr. Notes Theor. Comput. Sci.},
	volume = {30},
	number = {1},
	year = {1999},
	author = {Dino Pedreschi and Salvatore Ruggieri}
}
@conference {DBLP:conf/agp/GiannottiM99,
	title = {Querying inductive Databases via Logic-Based user-defined aggregates},
	booktitle = {APPIA-GULP-PRODE},
	year = {1999},
	pages = {605-620},
	author = {Fosca Giannotti and Giuseppe Manco}
}
@conference {DBLP:conf/pkdd/GiannottiM99,
	title = {Querying Inductive Databases via Logic-Based User-Defined Aggregates},
	booktitle = {PKDD},
	year = {1999},
	pages = {125-135},
	author = {Fosca Giannotti and Giuseppe Manco}
}
@conference {DBLP:conf/sebd/BonchiGMP99,
	title = {Una Metodologia Basata sulla Classificazione per la Pianificazione degli Accertamenti nel Rilevamento di Frodi},
	booktitle = {SEBD},
	year = {1999},
	pages = {69-84},
	author = {Francesco Bonchi and Fosca Giannotti and Gianni Mainetto and Dino Pedreschi}
}
@conference {DBLP:conf/dawak/BonchiGMP99,
	title = {Using Data Mining Techniques in Fiscal Fraud Detection},
	booktitle = {DaWaK},
	year = {1999},
	pages = {369-376},
	author = {Francesco Bonchi and Fosca Giannotti and Gianni Mainetto and Dino Pedreschi}
}
@article {DBLP:journals/jlp/PedreschiR99,
	title = {Verification of Logic Programs},
	journal = {J. Log. Program.},
	volume = {39},
	number = {1-3},
	year = {1999},
	pages = {125-176},
	author = {Dino Pedreschi and Salvatore Ruggieri}
}
@conference {DBLP:conf/tc11-5/AsirelliRT98,
	title = {The Constraint Operator of MedLan: Its Efficient Implementation and Use},
	booktitle = {IICIS},
	year = {1998},
	pages = {41-55},
	author = {Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@article {DBLP:journals/jlp/GiannottiP98,
	title = {Datalog with Non-Deterministic Choice Computers NDB-PTIME},
	journal = {J. Log. Program.},
	volume = {35},
	number = {1},
	year = {1998},
	pages = {79-101},
	author = {Fosca Giannotti and Dino Pedreschi}
}
@conference {DBLP:conf/csl/GiannottiMNP98,
	title = {On the Effective Semantics of Nondeterministic, Nonmonotonic, Temporal Logic Databases},
	booktitle = {CSL},
	year = {1998},
	pages = {58-72},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
@book {Ren98,
	title = {Mechanisms for Semantic Integration of Deductive Databases},
	year = {1998},
	note = {PhD thesis, Dipartimento di Informatica, University of Pisa, .},
	author = {Chiara Renso}
}
@article {RR98,
	title = {A Mediator Approach for Representing Knowledge},
	journal = {Intelligent Multimedia Presentation Systems. Human Computer Interaction Letters, 1 (1): 32-38, April 1998.},
	year = {1998},
	author = {Chiara Renso and Salvatore Ruggieri}
}
@conference {DBLP:conf/fqas/GiannottiMNP98,
	title = {Query Answering in Nondeterministic, Nonmonotonic Logic Databases},
	booktitle = {FQAS},
	year = {1998},
	pages = {175-187},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
@article {DBLP:journals/ipl/PedreschiR98,
	title = {Weakest Preconditions for Pure Prolog Programs},
	journal = {Inf. Process. Lett.},
	volume = {67},
	number = {3},
	year = {1998},
	pages = {145-150},
	author = {Dino Pedreschi and Salvatore Ruggieri}
}
@article {AART97,
	title = {Applying Restriction Constraint to Deductive Databases},
	journal = {Annals of Mathematics and Artificial Intelligence},
	year = {1997},
	note = {1997},
	pages = {3{\textendash}25},
	author = {Domenico Aquilino and Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@article {DBLP:journals/amai/AquilinoART97,
	title = {Applying Restriction Constraints to Deductive Databases},
	journal = {Ann. Math. Artif. Intell.},
	volume = {19},
	number = {1-2},
	year = {1997},
	pages = {3-25},
	author = {Domenico Aquilino and Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/dood/GiannottiMNP97,
	title = {Datalog++: A Basis for Active Object-Oriented Databases},
	booktitle = {DOOD},
	year = {1997},
	pages = {283-301},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/sebd/GiannottiMNP97,
	title = {Datalog++: a Basis for Active Object.Oriented Databases},
	booktitle = {SEBD},
	year = {1997},
	pages = {325-340},
	author = {Fosca Giannotti and Giuseppe Manco and Mirco Nanni and Dino Pedreschi}
}
@conference {DBLP:conf/agp/GiannottiMP97,
	title = {A Deductive Data Model for Representing and Querying Semistructured Data},
	booktitle = {APPIA-GULP-PRODE},
	year = {1997},
	pages = {129-140},
	author = {Fosca Giannotti and Giuseppe Manco and Dino Pedreschi}
}
@article {DBLP:journals/amai/PedreschiS97,
	title = {Non-determinism in Deductive Databases - Preface},
	journal = {Ann. Math. Artif. Intell.},
	volume = {19},
	number = {1-2},
	year = {1997},
	pages = {1-2},
	author = {Dino Pedreschi and V. S. Subrahmanian}
}
@article {DBLP:journals/amai/GiannottiGSZ97,
	title = {Programming with Non-Determinism in Deductive Databases},
	journal = {Ann. Math. Artif. Intell.},
	volume = {19},
	number = {1-2},
	year = {1997},
	pages = {97-125},
	author = {Fosca Giannotti and Sergio Greco and Domenico Sacc{\`a} and Carlo Zaniolo}
}
@conference {DBLP:conf/dbpl/AmatoGM97,
	title = {Static Analysis of Transactions for Conservative Multigranularity Locking},
	booktitle = {DBPL},
	year = {1997},
	pages = {413-430},
	author = {Giuseppe Amato and Fosca Giannotti and Gianni Mainetto}
}
@article {DBLP:journals/logcom/PedreschiR97,
	title = {Verification of Meta-Interpreters},
	journal = {J. Log. Comput.},
	volume = {7},
	number = {2},
	year = {1997},
	pages = {267-303},
	author = {Dino Pedreschi and Salvatore Ruggieri}
}
@article {DBLP:journals/jlp/AptGP96,
	title = {A Closer Look at Declarative Interpretations},
	journal = {J. Log. Program.},
	volume = {28},
	number = {2},
	year = {1996},
	pages = {147-180},
	author = {Krzysztof R. Apt and Maurizio Gabbrielli and Dino Pedreschi}
}
@conference {DBLP:conf/lid/AsirelliRT96,
	title = {Language Extensions for Semantic Integration of Deductive Databases},
	booktitle = {Logic in Databases},
	year = {1996},
	pages = {415-434},
	author = {Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@proceedings {DBLP:conf/lid/1996,
	title = {Logic in Databases, International Workshop LID{\textquoteright}96, San Miniato, Italy, July 1-2, 1996, Proceedings},
	volume = {1154},
	year = {1996},
	publisher = {Springer},
	isbn = {3-540-61814-7},
	author = {Dino Pedreschi and Carlo Zaniolo}
}
@conference {DBLP:conf/sebd/CarboniDGM96,
	title = {Ragionamento spazio-temporale con LDLT: primi esperimenti verso un sistema deduttivo per applicazioni geografiche},
	booktitle = {SEBD},
	year = {1996},
	pages = {73-90},
	author = {Marilisa E. Carboni and Annalisa Di Deo and Fosca Giannotti and Maria V Masserotti}
}
@conference {DBLP:conf/deductive/CarboniDGM96,
	title = {Spatio-Temporal Reasoning with LDLT: First Steps Towards a Deductive System for Geographical Applications},
	booktitle = {DDLP},
	year = {1996},
	pages = {135-151},
	author = {Marilisa E. Carboni and Annalisa Di Deo and Fosca Giannotti and Maria V Masserotti}
}
@inbook {ART96,
	title = {Towards {D}eclarative {GIS} {A}nalysis},
	year = {1996},
	note = {{\em Proocedings of the {F}ourth {ACM} {W}orkshop on {A}dvances in {G}eographic {I}nformation {S}ystems}, pages.},
	pages = {99{\textendash}105},
	author = {Domenico Aquilino and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/gis/AquilinoRT96,
	title = {Towards Declarative GIS Analysis},
	booktitle = {ACM-GIS},
	year = {1996},
	pages = {98-104},
	author = {Domenico Aquilino and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/fapr/MontesiRT96,
	title = {Using Temporary Integrity Constraints to Optimize Databases},
	booktitle = {FAPR},
	year = {1996},
	pages = {430-435},
	author = {Danilo Montesi and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/agp/PedreschiR95,
	title = {A Case Study in Logic Program Verification: the Vanilla Metainterpreter},
	booktitle = {GULP-PRODE},
	year = {1995},
	pages = {643-654},
	author = {Dino Pedreschi and Salvatore Ruggieri}
}
@conference {DBLP:conf/sebd/CarboniGFP95,
	title = {Declarative Reconstruction of Updates in Logic Databases: A Compilative Approach},
	booktitle = {SEBD},
	year = {1995},
	pages = {3-13},
	author = {Marilisa E. Carboni and Fosca Giannotti and V. Foddai and Dino Pedreschi}
}
@conference {DBLP:conf/agp/CarboniFGP95,
	title = {Declarative Reconstruction of Updates in Logic Databases: a Compilative Approach},
	booktitle = {GULP-PRODE},
	year = {1995},
	pages = {169-182},
	author = {Marilisa E. Carboni and V. Foddai and Fosca Giannotti and Dino Pedreschi}
}
@article {AART95,
	title = {An Operator for Composing Deductive Databases with Theories of Constraints},
	year = {1995},
	note = {Logic Programming and Nonmonotonic Reasoning, Third International Conference Lecture Notes in Computer Science vol 928,},
	pages = {57{\textendash}70},
	author = {Domenico Aquilino and Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/lpnmr/AquilinoART95,
	title = {An Operator for Composing Deductive Databases with Theories of Constraints},
	booktitle = {LPNMR},
	year = {1995},
	pages = {57-70},
	author = {Domenico Aquilino and Patrizia Asirelli and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/forte/FioreG94,
	title = {An abstract interpreter for the specification language LOTOS},
	booktitle = {FORTE},
	year = {1994},
	pages = {309-323},
	author = {Franco Fiore and Fosca Giannotti}
}
@conference {DBLP:conf/agp/BrogiRT94,
	title = {Amalgamating Language and Meta-language for Composing Logic Programs},
	booktitle = {GULP-PRODE (2)},
	year = {1994},
	pages = {408-422},
	author = {Antonio Brogi and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/sebd/AmatoGM94,
	title = {Conservative Multigranularity Locking for an Obiect-Oriented Persistent Language via Abstract Interpretation},
	booktitle = {SEBD},
	year = {1994},
	pages = {329-349},
	author = {Giuseppe Amato and Fosca Giannotti and Gianni Mainetto}
}
@conference {DBLP:conf/deductive/CorciuloGPZ94,
	title = {Expressive Power of Non-Deterministic Operators for Logic-based Languages},
	booktitle = {Workshop on Deductive Databases and Logic Programming},
	year = {1994},
	pages = {27-40},
	author = {Luca Corciulo and Fosca Giannotti and Dino Pedreschi and Carlo Zaniolo}
}
@article {DBLP:journals/scp/GiannottiL94,
	title = {Gate Splitting in LOTOS Specifications Using Abstract Interpretation},
	journal = {Sci. Comput. Program.},
	volume = {23},
	number = {2-3},
	year = {1994},
	pages = {127-149},
	author = {Fosca Giannotti and Diego Latella}
}
@article {BCMMPRT94,
	title = {Implementations of Program Composition Operations},
	year = {1994},
	note = {Programming Language Implementation and Logic Programming Lecture Notes in Computer Science, volume 844,},
	pages = {292{\textendash}307},
	author = {Antonio Brogi and A. Chiarelli and Paolo Mancarella and V. Mazzotta and Dino Pedreschi and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/plilp/BrogiCMMPRT94,
	title = {Implementations of Program Composition Operations},
	booktitle = {PLILP},
	year = {1994},
	pages = {292-307},
	author = {Antonio Brogi and A. Chiarelli and Paolo Mancarella and V. Mazzotta and Dino Pedreschi and Chiara Renso and Franco Turini}
}
@conference {DBLP:conf/plilp/BrogiCMMPRT94,
	title = {Implementations of Program Composition Operations},
	booktitle = {PLILP},
	year = {1994},
	pages = {292-307},
	author = {Antonio Brogi and A. Chiarelli and Paolo Mancarella and V. Mazzotta and Dino Pedreschi and Chiara Renso and Franco Turini}
}
@article {DBLP:journals/toplas/BrogiMPT94,
	title = {Modular Logic Programming},
	journal = {ACM Trans. Program. Lang. Syst.},
	volume = {16},
	number = {4},
	year = {1994},
	pages = {1361-1398},
	author = {Antonio Brogi and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/iclp/Pedreschi94,
	title = {A Proof Method for Runtime Properties of Prolog Programs},
	booktitle = {ICLP},
	year = {1994},
	pages = {584-598},
	author = {Dino Pedreschi}
}
@conference {DBLP:conf/agp/MascellaniP94,
	title = {Proving termination of Prolog programs},
	booktitle = {GULP-PRODE (1)},
	year = {1994},
	pages = {46-61},
	author = {Paolo Mascellani and Dino Pedreschi}
}
@conference {DBLP:conf/vldb/AmatoGM93,
	title = {Data Sharing Analysis for a Database Programming Lanaguage via Abstract Interpretation},
	booktitle = {VLDB},
	year = {1993},
	pages = {405-415},
	author = {Giuseppe Amato and Fosca Giannotti and Gianni Mainetto}
}
@conference {DBLP:conf/dood/CorciuloGP93,
	title = {Datalog with Non-Deterministic Choice Computes NDB-PTIME},
	booktitle = {DOOD},
	year = {1993},
	pages = {49-66},
	author = {Luca Corciulo and Fosca Giannotti and Dino Pedreschi}
}
@conference {DBLP:conf/tapsoft/GiannottiL93,
	title = {Gate Splitting in LOTOS Specifications Using Abstract Interpretation},
	booktitle = {TAPSOFT},
	year = {1993},
	pages = {437-452},
	author = {Fosca Giannotti and Diego Latella}
}
@article {DBLP:journals/iandc/AptP93,
	title = {Reasoning about Termination of Pure Prolog Programs},
	journal = {Inf. Comput.},
	volume = {106},
	number = {1},
	year = {1993},
	pages = {109-157},
	author = {Krzysztof R. Apt and Dino Pedreschi}
}
@conference {DBLP:conf/fmldo/AmatoGM93,
	title = {Static Analysis of Transactions: an Experiment of Abstract Interpretation Usage},
	booktitle = {FMLDO},
	year = {1993},
	pages = {19-29},
	author = {Giuseppe Amato and Fosca Giannotti and Gianni Mainetto}
}
@conference {DBLP:conf/agp/ChiarelliMR93,
	title = {A WAM Estesa per la Composizione di Programi Logici},
	booktitle = {GULP},
	year = {1993},
	pages = {189-202},
	author = {A. Chiarelli and V. Mazzotta and Chiara Renso}
}
@conference {DBLP:conf/sas/AmatoGM92,
	title = {Analysis of Concurrent Transactions in a Functional Database Programming Language},
	booktitle = {WSA},
	year = {1992},
	pages = {174-184},
	author = {Giuseppe Amato and Fosca Giannotti and Gianni Mainetto}
}
@conference {DBLP:conf/meta/BrogiMPT92,
	title = {Meta for Modularising Logic Programming},
	booktitle = {META},
	year = {1992},
	pages = {105-119},
	author = {Antonio Brogi and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@book {DBLP:books/mit/pfenning92/BertolinoMPT92,
	title = {The Type System of LML},
	series = {Types in Logic Programming},
	year = {1992},
	pages = {313-332},
	author = {Bruno Bertolino and Luigi Meo and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/sas/GiannottiL92,
	title = {Using Abstract Interpretation for Gate splitting in LOTOS Specifications},
	booktitle = {WSA},
	year = {1992},
	pages = {194-204},
	author = {Fosca Giannotti and Diego Latella}
}
@conference {DBLP:conf/dood/GiannottiPSZ91,
	title = {Non-Determinism in Deductive Databases},
	booktitle = {DOOD},
	year = {1991},
	pages = {129-146},
	author = {Fosca Giannotti and Dino Pedreschi and Domenico Sacc{\`a} and Carlo Zaniolo}
}
@conference {DBLP:conf/tacs/AptP91,
	title = {Proving Termination of General Prolog Programs},
	booktitle = {TACS},
	year = {1991},
	pages = {265-289},
	author = {Krzysztof R. Apt and Dino Pedreschi}
}
@conference {DBLP:conf/plilp/GiannottiH91,
	title = {A Technique for Recursive Invariance Detection and Selective Program Specification},
	booktitle = {PLILP},
	year = {1991},
	pages = {323-334},
	author = {Fosca Giannotti and Manuel V. Hermenegildo}
}
@conference {DBLP:conf/iclp/BrogiMPT91,
	title = {Theory Construction in Computational Logic},
	booktitle = {ICLP Workshop on Construction of Logic Programs},
	year = {1991},
	pages = {241-250},
	author = {Antonio Brogi and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/slp/MancarellaPRT90,
	title = {Algebraic Properties of a Class of Logic Programs},
	booktitle = {NACLP},
	year = {1990},
	pages = {23-39},
	author = {Paolo Mancarella and Dino Pedreschi and Marina Rondinelli and Marco Tagliatti}
}
@conference {DBLP:conf/lpnmr/GiannottiP90,
	title = {Declarative Semantics for Pruning Operators in Logic Programming},
	booktitle = {LPNMR},
	year = {1990},
	pages = {27-37},
	author = {Fosca Giannotti and Dino Pedreschi}
}
@conference {DBLP:conf/plilp/BrogiMPT90,
	title = {Logic Programming within a Functional Framework},
	booktitle = {PLILP},
	year = {1990},
	pages = {372-386},
	author = {Antonio Brogi and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/ieaaie/BertazzoniG90,
	title = {RASP: A Resource Allocator for Software Projects},
	booktitle = {IEA/AIE (Vol. 2)},
	year = {1990},
	pages = {628-637},
	author = {C. Bertazzoni and Fosca Giannotti}
}
@article {DBLP:journals/jlp/BarbutiMPT90,
	title = {A Transformational Approach to Negation in Logic Programming},
	journal = {J. Log. Program.},
	volume = {8},
	number = {3},
	year = {1990},
	pages = {201-228},
	author = {Roberto Barbuti and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/ecai/BrogiMPT90,
	title = {Universal Quantification by Case Analysis},
	booktitle = {ECAI},
	year = {1990},
	pages = {111-116},
	author = {Antonio Brogi and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/iclp/MancarellaP88,
	title = {An Algebra of Logic Programs},
	booktitle = {ICLP/SLP},
	year = {1988},
	pages = {1006-1023},
	author = {Paolo Mancarella and Dino Pedreschi}
}
@article {DBLP:journals/jlp/MancarellaMP88,
	title = {Complete Logic Programs with Domain-Closure Axiom},
	journal = {J. Log. Program.},
	volume = {5},
	number = {3},
	year = {1988},
	pages = {263-276},
	author = {Paolo Mancarella and Simone Martini and Dino Pedreschi}
}
@conference {DBLP:conf/fgcs/BertolinoMMNPT88,
	title = {A Progress Report on the LML Project},
	booktitle = {FGCS},
	year = {1988},
	pages = {675-684},
	author = {Bruno Bertolino and Paolo Mancarella and Luigi Meo and Luca Nini and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/tapsoft/BarbutiMPT87,
	title = {Intensional Negation of Logic Programs: Examples and Implementation Techniques},
	booktitle = {TAPSOFT, Vol.2},
	year = {1987},
	pages = {96-110},
	author = {Roberto Barbuti and Paolo Mancarella and Dino Pedreschi and Franco Turini}
}
@article {DBLP:journals/scp/GiannottiMPT87,
	title = {Symbolic Evaluation with Structural Recursive Symbolic Constants},
	journal = {Sci. Comput. Program.},
	volume = {9},
	number = {2},
	year = {1987},
	pages = {161-177},
	author = {Fosca Giannotti and Attilio Matteucci and Dino Pedreschi and Franco Turini}
}
@article {DBLP:journals/tse/AmbriolaGPT85,
	title = {Symbolic Semantics and Program Reduction},
	journal = {IEEE Trans. Software Eng.},
	volume = {11},
	number = {8},
	year = {1985},
	pages = {784-794},
	author = {Vincenzo Ambriola and Fosca Giannotti and Dino Pedreschi and Franco Turini}
}
@conference {DBLP:conf/pos/AlbanoGOP85,
	title = {The Type System of Galileo},
	booktitle = {Data Types and Persistence (Appin), Informal Proceedings},
	year = {1985},
	pages = {175-195},
	author = {Antonio Albano and Fosca Giannotti and Renzo Orsini and Dino Pedreschi}
}
@conference {DBLP:conf/db-workshops/AlbanoGOP85,
	title = {The Type System of Galileo},
	booktitle = {Data Types and Persistence (Appin)},
	year = {1985},
	pages = {101-119},
	author = {Antonio Albano and Fosca Giannotti and Renzo Orsini and Dino Pedreschi}
}
