%0 Conference Paper %B Proceedings of the 30th International Conference on Advances in Geographic Information Systems %D 2022 %T Connected Vehicle Simulation Framework for Parking Occupancy Prediction (Demo Paper) %A Resce, Pierpaolo %A Vorwerk, Lukas %A Han, Zhiwei %A Cornacchia, Giuliano %A Alamdari, Omid Isfahani %A Mirco Nanni %A Luca Pappalardo %A Weimer, Daniel %A Liu, Yuanting %X This paper demonstrates a simulation framework that collects data about connected vehicles' locations and surroundings in a realistic traffic scenario. Our focus lies on the capability to detect parking spots and their occupancy status. We use this data to train machine learning models that predict parking occupancy levels of specific areas in the city center of San Francisco. By comparing their performance to a given ground truth, our results show that it is possible to use simulated connected vehicle data as a base for prototyping meaningful AI-based applications. %B Proceedings of the 30th International Conference on Advances in Geographic Information Systems %I Association for Computing Machinery %C New York, NY, USA %@ 9781450395298 %G eng %U https://doi.org/10.1145/3557915.3560995 %R 10.1145/3557915.3560995 %0 Journal Article %D 2021 %T Give more data, awareness and control to individual citizens, and they will help COVID-19 containment %A Mirco Nanni %A Andrienko, Gennady %A Barabasi, Albert-Laszlo %A Boldrini, Chiara %A Bonchi, Francesco %A Cattuto, Ciro %A Chiaromonte, Francesca %A Comandé, Giovanni %A Conti, Marco %A Coté, Mark %A Dignum, Frank %A Dignum, Virginia %A Domingo-Ferrer, Josep %A Ferragina, Paolo %A Fosca Giannotti %A Riccardo Guidotti %A Helbing, Dirk %A Kaski, Kimmo %A Kertész, János %A Lehmann, Sune %A Lepri, Bruno %A Lukowicz, Paul %A Matwin, Stan %A Jiménez, David Megías %A Anna Monreale %A Morik, Katharina %A Oliver, Nuria %A Passarella, Andrea %A Passerini, Andrea %A Dino Pedreschi %A Pentland, Alex %A Pianesi, Fabio %A Francesca Pratesi %A S Rinzivillo %A Salvatore Ruggieri %A Siebes, Arno %A Torra, Vicenc %A Roberto Trasarti %A Hoven, Jeroen van den %A Vespignani, Alessandro %X The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society. %8 2021/02/02 %@ 1572-8439 %G eng %U https://link.springer.com/article/10.1007/s10676-020-09572-w %! Ethics and Information Technology %R https://doi.org/10.1007/s10676-020-09572-w %0 Conference Paper %B Formal Methods. FM 2019 International Workshops %D 2020 %T Analysis and Visualization of Performance Indicators in University Admission Tests %A Michela Natilli %A Daniele Fadda %A S Rinzivillo %A Dino Pedreschi %A Licari, Federica %E Sekerinski, Emil %E Moreira, Nelma %E Oliveira, José N. %E Ratiu, Daniel %E Riccardo Guidotti %E Farrell, Marie %E Luckcuck, Matt %E Marmsoler, Diego %E Campos, José %E Astarte, Troy %E Gonnord, Laure %E Cerone, Antonio %E Couto, Luis %E Dongol, Brijesh %E Kutrib, Martin %E Monteiro, Pedro %E Delmas, David %X 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. %B Formal Methods. FM 2019 International Workshops %I Springer International Publishing %C Cham %8 2020// %@ 978-3-030-54994-7 %G eng %U https://link.springer.com/chapter/10.1007/978-3-030-54994-7_14 %R https://doi.org/10.1007/978-3-030-54994-7_14 %0 Conference Paper %B Discovery Science %D 2020 %T Explaining Sentiment Classification with Synthetic Exemplars and Counter-Exemplars %A Lampridis, Orestis %A Riccardo Guidotti %A Salvatore Ruggieri %E Appice, Annalisa %E Tsoumakas, Grigorios %E Manolopoulos, Yannis %E Matwin, Stan %X 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 – 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. %B Discovery Science %I Springer International Publishing %C Cham %8 2020// %@ 978-3-030-61527-7 %G eng %U https://link.springer.com/chapter/10.1007/978-3-030-61527-7_24 %R https://doi.org/10.1007/978-3-030-61527-7_24 %0 Journal Article %J International Journal of Data Science and Analytics %D 2020 %T Human migration: the big data perspective %A Alina Sirbu %A Andrienko, Gennady %A Andrienko, Natalia %A Boldrini, Chiara %A Conti, Marco %A Fosca Giannotti %A Riccardo Guidotti %A Bertoli, Simone %A Jisu Kim %A Muntean, Cristina Ioana %A Luca Pappalardo %A Passarella, Andrea %A Dino Pedreschi %A Pollacci, Laura %A Francesca Pratesi %A Sharma, Rajesh %X 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. %B International Journal of Data Science and Analytics %P 1–20 %8 2020/03/23 %@ 2364-4168 %G eng %U https://link.springer.com/article/10.1007%2Fs41060-020-00213-5 %! International Journal of Data Science and Analytics %R https://doi.org/10.1007/s41060-020-00213-5 %0 Conference Paper %B Formal Methods. FM 2019 International Workshops %D 2020 %T “Know Thyself” How Personal Music Tastes Shape the Last.Fm Online Social Network %A Riccardo Guidotti %A Giulio Rossetti %E Sekerinski, Emil %E Moreira, Nelma %E Oliveira, José N. %E Ratiu, Daniel %E Riccardo Guidotti %E Farrell, Marie %E Luckcuck, Matt %E Marmsoler, Diego %E Campos, José %E Astarte, Troy %E Gonnord, Laure %E Cerone, Antonio %E Couto, Luis %E Dongol, Brijesh %E Kutrib, Martin %E Monteiro, Pedro %E Delmas, David %X As Nietzsche once wrote “Without music, life would be a mistake” (Twilight of the Idols, 1889.). The music we listen to reflects our personality, our way to approach life. In order to enforce self-awareness, we devised a Personal Listening Data Model that allows for capturing individual music preferences and patterns of music consumption. We applied our model to 30k users of Last.Fm for which we collected both friendship ties and multiple listening. Starting from such rich data we performed an analysis whose final aim was twofold: (i) capture, and characterize, the individual dimension of music consumption in order to identify clusters of like-minded Last.Fm users; (ii) analyze if, and how, such clusters relate to the social structure expressed by the users in the service. Do there exist individuals having similar Personal Listening Data Models? If so, are they directly connected in the social graph or belong to the same community?. %B Formal Methods. FM 2019 International Workshops %I Springer International Publishing %C Cham %8 2020// %@ 978-3-030-54994-7 %G eng %U https://link.springer.com/chapter/10.1007/978-3-030-54994-7_11 %R https://doi.org/10.1007/978-3-030-54994-7_11 %0 Conference Paper %B Discovery Science %D 2020 %T Predicting and Explaining Privacy Risk Exposure in Mobility Data %A Francesca Naretto %A Roberto Pellungrini %A Anna Monreale %A Nardini, Franco Maria %A Musolesi, Mirco %E Appice, Annalisa %E Tsoumakas, Grigorios %E Manolopoulos, Yannis %E Matwin, Stan %X 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’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. %B Discovery Science %I Springer International Publishing %C Cham %8 2020// %@ 978-3-030-61527-7 %G eng %U https://link.springer.com/chapter/10.1007/978-3-030-61527-7_27 %R https://doi.org/10.1007/978-3-030-61527-7_27 %0 Conference Paper %B ECML PKDD 2020 Workshops %D 2020 %T Prediction and Explanation of Privacy Risk on Mobility Data with Neural Networks %A Francesca Naretto %A Roberto Pellungrini %A Nardini, Franco Maria %A Fosca Giannotti %E Koprinska, Irena %E Kamp, Michael %E Appice, Annalisa %E Loglisci, Corrado %E Antonie, Luiza %E Zimmermann, Albrecht %E Riccardo Guidotti %E Özgöbek, Özlem %E Ribeiro, Rita P. %E Gavaldà, Ricard %E Gama, João %E Adilova, Linara %E Krishnamurthy, Yamuna %E Ferreira, Pedro M. %E Malerba, Donato %E Medeiros, Ibéria %E Ceci, Michelangelo %E Manco, Giuseppe %E Masciari, Elio %E Ras, Zbigniew W. %E Christen, Peter %E Ntoutsi, Eirini %E Schubert, Erich %E Zimek, Arthur %E Anna Monreale %E Biecek, Przemyslaw %E S Rinzivillo %E Kille, Benjamin %E Lommatzsch, Andreas %E Gulla, Jon Atle %X 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. %B ECML PKDD 2020 Workshops %I Springer International Publishing %C Cham %8 2020// %@ 978-3-030-65965-3 %G eng %U https://link.springer.com/chapter/10.1007/978-3-030-65965-3_34 %R https://doi.org/10.1007/978-3-030-65965-3_34 %0 Journal Article %J arXiv preprint arXiv:2006.03141 %D 2020 %T The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy %A Paolo Cintia %A Daniele Fadda %A Fosca Giannotti %A Luca Pappalardo %A Giulio Rossetti %A Dino Pedreschi %A S Rinzivillo %A Bonato, Pietro %A Fabbri, Francesco %A Penone, Francesco %A Savarese, Marcello %A Checchi, Daniele %A Chiaromonte, Francesca %A Vineis , Paolo %A Guzzetta, Giorgio %A Riccardo, Flavia %A Marziano, Valentina %A Poletti, Piero %A Trentini, Filippo %A Bella, Antonio %A Andrianou, Xanthi %A Del Manso, Martina %A Fabiani, Massimo %A Bellino, Stefania %A Boros, Stefano %A Mateo Urdiales, Alberto %A Vescio, Maria Fenicia %A Brusaferro, Silvio %A Rezza, Giovanni %A Pezzotti, Patrizio %A Ajelli, Marco %A Merler, Stefano %X 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'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. %B arXiv preprint arXiv:2006.03141 %G eng %U https://arxiv.org/abs/2006.03141 %0 Journal Article %J International Journal of Data Science and Analytics %D 2020 %T (So) Big Data and the transformation of the city %A Andrienko, Gennady %A Andrienko, Natalia %A Boldrini, Chiara %A Caldarelli, Guido %A Paolo Cintia %A Cresci, Stefano %A Facchini, Angelo %A Fosca Giannotti %A Gionis, Aristides %A Riccardo Guidotti %A others %X 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 “City of Citizens” thematic area of the Horizon 2020 SoBigData initiative, which includes a virtual research environment with mobility datasets and urban analytics methods developed by several institutions around Europe. We conclude the paper outlining the main research challenges that urban data science has yet to address in order to help make the smart city vision a reality. %B International Journal of Data Science and Analytics %G eng %U https://link.springer.com/article/10.1007/s41060-020-00207-3 %R https://doi.org/10.1007/s41060-020-00207-3 %0 Conference Paper %B ECML PKDD 2018 Workshops %D 2019 %T Privacy Risk for Individual Basket Patterns %A Roberto Pellungrini %A Anna Monreale %A Riccardo Guidotti %E Alzate, Carlos %E Anna Monreale %E Bioglio, Livio %E Bitetta, Valerio %E Bordino, Ilaria %E Caldarelli, Guido %E Ferretti, Andrea %E Riccardo Guidotti %E Gullo, Francesco %E Pascolutti, Stefano %E Pensa, Ruggero G. %E Robardet, Céline %E Squartini, Tiziano %X 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. %B ECML PKDD 2018 Workshops %I Springer International Publishing %C Cham %8 2019// %@ 978-3-030-13463-1 %G eng %U https://link.springer.com/chapter/10.1007/978-3-030-13463-1_11 %R https://doi.org/10.1007/978-3-030-13463-1_11 %0 Conference Proceedings %D 2019 %T SAI a Sensible Artificial Intelligence that plays Go %A F Morandin %A G Amato %A R Gini %A C Metta %A M Parton %A G.C. Pascutto %G eng %0 Book Section %B A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years %D 2018 %T How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science %A Amato, G. %A Candela, L. %A Castelli, D. %A Esuli, A. %A Falchi, F. %A Gennaro, C. %A Fosca Giannotti %A Anna Monreale %A Mirco Nanni %A Pagano, P. %A Luca Pappalardo %A Dino Pedreschi %A Francesca Pratesi %A Rabitti, F. %A S Rinzivillo %A Giulio Rossetti %A Salvatore Ruggieri %A Sebastiani, F. %A Tesconi, M. %E Flesca, Sergio %E Greco, Sergio %E Masciari, Elio %E Saccà, Domenico %X 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. %B A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years %I Springer International Publishing %C Cham %P 287 - 306 %@ 978-3-319-61893-7 %G eng %U https://link.springer.com/chapter/10.1007%2F978-3-319-61893-7_17 %R https://doi.org/10.1007/978-3-319-61893-7_17 %0 Book Section %B Participatory Sensing, Opinions and Collective Awareness %D 2017 %T Applications for Environmental Sensing in EveryAware %A Atzmueller, Martin %A Becker, Martin %A Molino, Andrea %A Mueller, Juergen %A Peters, Jan %A Alina Sirbu %X 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. %B Participatory Sensing, Opinions and Collective Awareness %I Springer %P 135–155 %G eng %U http://link.springer.com/chapter/10.1007/978-3-319-25658-0_7 %R 10.1007/978-3-319-25658-0_7 %0 Book %D 2016 %T Realising the European open science cloud %A Ayris, Paul %A Berthou, Jean-Yves %A Bruce, Rachel %A Lindstaedt, Stefanie %A Anna Monreale %A Mons, Barend %A Murayama, Yasuhiro %A Södergård, Caj %A Tochtermann, Klaus %A Wilkinson, Ross %X 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. %@ 978-92-79-61762-1 %G eng %U http://dx.doi.org/10.2777/940154 %R 10.2777/940154 %0 Conference Paper %B 2016 IEEE Tenth International Conference on Semantic Computing (ICSC) %D 2016 %T SPARQL Queries over Source Code %A Mattia Setzu %A Atzori, Maurizio %B 2016 IEEE Tenth International Conference on Semantic Computing (ICSC) %I IEEE %G eng %0 Journal Article %J Concurrency and Computation: Practice and Experience %D 2014 %T Decision tree building on multi-core using FastFlow %A Aldinucci, Marco %A Salvatore Ruggieri %A Torquati, Massimo %X 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 © 2013 John Wiley & Sons, Ltd. %B Concurrency and Computation: Practice and Experience %V 26 %P 800–820 %G eng %R 10.1002/cpe.3063 %0 Journal Article %J Transaction in GIS %D 2013 %T CONSTAnT - A Conceptual Data Model for Semantic Trajectories of Moving Objects %A Vania Bogorny %A Chiara Renso %A Artur Ribeiro de Aquino %A Fernando de Lucca Siqueira %A Luis Otavio Alvares %B Transaction in GIS %0 Conference Paper %B 2013 {IEEE} 14th International Conference on Mobile Data Management, Milan, Italy, June 3-6, 2013 - Volume 2 %D 2013 %T A Gravity Model for Speed Estimation over Road Network %A Paolo Cintia %A Roberto Trasarti %A José Antônio Fernandes de Macêdo %A Livia Almada %A Camila Fereira %B 2013 {IEEE} 14th International Conference on Mobile Data Management, Milan, Italy, June 3-6, 2013 - Volume 2 %G eng %U http://dx.doi.org/10.1109/MDM.2013.83 %R 10.1109/MDM.2013.83 %0 Conference Paper %B SEBD %D 2013 %T Privacy-Aware Distributed Mobility Data Analytics %A Francesca Pratesi %A Anna Monreale %A Hui Wendy Wang %A S Rinzivillo %A Dino Pedreschi %A Gennady Andrienko %A Natalia Andrienko %X 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. %B SEBD %C Roccella Jonica %G eng %0 Book Section %B Geographic Information Science at the Heart of Europe %D 2013 %T Privacy-Preserving Distributed Movement Data Aggregation %A Anna Monreale %A Hui Wendy Wang %A Francesca Pratesi %A S Rinzivillo %A Dino Pedreschi %A Gennady Andrienko %A Natalia Andrienko %E Vandenbroucke, Danny %E Bucher, Bénédicte %E Crompvoets, Joep %X 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’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. %B Geographic Information Science at the Heart of Europe %S Lecture Notes in Geoinformation and Cartography %I Springer International Publishing %P 225-245 %@ 978-3-319-00614-7 %U http://dx.doi.org/10.1007/978-3-319-00615-4_13 %R 10.1007/978-3-319-00615-4_13 %0 Conference Paper %B Mobile Data Management Conference, 2013 %D 2013 %T A Proactive Ap- plication to Monitor Truck Fleets %A Fabio Da Costa Albuquerque %A Marco A. Casanova %A Marcelo Tilio M. de Carvalho %A de José Antônio Fernandes Macêdo %A Chiara Renso %B Mobile Data Management Conference, 2013 %0 Journal Article %J IEEE Transactions on Visualization and Computer Graphics %D 2013 %T Scalable Analysis of Movement Data for Extracting and Exploring Significant Places %A Gennady Andrienko %A Natalia Andrienko %A C. Hunter %A S Rinzivillo %A Stefan Wrobel %B IEEE Transactions on Visualization and Computer Graphics %V 19 %& 49 %0 Journal Article %J ACM Computing Surveys %D 2013 %T Semantic Trajectories Modeling and Analysis %A Christine Parent %A Stefano Spaccapietra %A Chiara Renso %A Gennady Andrienko %A Natalia Andrienko %A Vania Bogorny %A Damiani M L, %A Gkoulalas-Divanis A, %A de José Antônio Fernandes Macêdo %A Nikos Pelekis %B ACM Computing Surveys %V 45 %8 August 2013 %0 Conference Paper %D 2013 %T Spatio temporal keyword-queries in Social Networs %A Vittoria Cozza %A Antonio Messina %A Danilo Montesi %A Luca Arietta %A Matteo Magnani %0 Journal Article %J European Physical Journal-Special Topics %D 2012 %T Smart cities of the future %A Batty, Michael %A Axhausen, Kay W %A Fosca Giannotti %A Pozdnoukhov, Alexei %A Bazzani, Armando %A Monica Wachowicz %A Ouzounis, Georgios %A Portugali, Yuval %X 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. %B European Physical Journal-Special Topics %V 214 %P 481 %G eng %R 10.1140/epjst/e2012-01703-3 %0 Journal Article %J Nature Methods %D 2012 %T Wisdom of crowds for robust gene network inference %A Daniel Marbach %A J.C. Costello %A Robert Küffner %A N.M. Vega %A R.J. Prill %A D.M. Camacho %A K.R. Allison %A Manolis Kellis %A J.J. Collins %A Aderhold, A. %A Gustavo Stolovitzky %A Bonneau, R. %A Chen, Y. %A Cordero, F. %A Martin Crane %A Dondelinger, F. %A Drton, M. %A Esposito, R. %A Foygel, R. %A De La Fuente, A. %A Gertheiss, J. %A Geurts, P. %A Greenfield, A. %A Grzegorczyk, M. %A Haury, A.-C. %A Holmes, B. %A Hothorn, T. %A Husmeier, D. %A Huynh-Thu, V.A. %A Irrthum, A. %A Karlebach, G. %A Lebre, S. %A De Leo, V. %A Madar, A. %A Mani, S. %A Mordelet, F. %A Ostrer, H. %A Ouyang, Z. %A Pandya, R. %A Petri, T. %A Pinna, A. %A Poultney, C.S. %A Rezny, S. %A Heather J Ruskin %A Saeys, Y. %A Shamir, R. %A Alina Sirbu %A Song, M. %A Soranzo, N. %A Statnikov, A. %A N.M. Vega %A Vera-Licona, P. %A Vert, J.-P. %A Visconti, A. %A Haizhou Wang %A Wehenkel, L. %A Windhager, L. %A Zhang, Y. %A Zimmer, R. %B Nature Methods %V 9 %P 796-804 %G eng %U http://www.scopus.com/inward/record.url?eid=2-s2.0-84870305264&partnerID=40&md5=04a686572bdefff60157bf68c95df7ea %R 10.1038/nmeth.2016 %0 Journal Article %J Nat Methods %D 2012 %T Wisdom of crowds for robust gene network inference. %A Daniel Marbach %A J.C. Costello %A Robert Küffner %A N.M. Vega %A R.J. Prill %A D.M. Camacho %A K.R. Allison %A Manolis Kellis %A J.J. Collins %A Gustavo Stolovitzky %X

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.

%B Nat Methods %V 9 %P 796-804 %8 2012 Aug %G eng %R 10.1038/nmeth.2016 %0 Conference Proceedings %B IEEE Conference on Visual Analytics Science and Technology %D 2011 %T From Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data %A Gennady Andrienko %A Natalia Andrienko %A Cristophe Hurter %A S Rinzivillo %A Stefan Wrobel %B IEEE Conference on Visual Analytics Science and Technology %0 Conference Proceedings %B 13th AGILE conference on Geographic Information Science %D 2010 %T A Generalisation-based Approach to Anonymising Movement Data %A Gennady Andrienko %A Natalia Andrienko %A Fosca Giannotti %A Anna Monreale %A Dino Pedreschi %A S Rinzivillo %X 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. %B 13th AGILE conference on Geographic Information Science %U http://agile2010.dsi.uminho.pt/pen/ShortPapers_PDF%5C122_DOC.pdf %0 Journal Article %J Transactions on Data Privacy %D 2010 %T Movement Data Anonymity through Generalization %A Anna Monreale %A Gennady Andrienko %A Natalia Andrienko %A Fosca Giannotti %A Dino Pedreschi %A S Rinzivillo %A Stefan Wrobel %X 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. %B Transactions on Data Privacy %V 3 %P 91–121 %U http://www.tdp.cat/issues/abs.a045a10.php %0 Conference Paper %B Proceedings of the 2nd SIGSPATIAL ACM GIS 2009 International Workshop on Security and Privacy in GIS and LBS %D 2009 %T Movement data anonymity through generalization %A Gennady Andrienko %A Natalia Andrienko %A Fosca Giannotti %A Anna Monreale %A Dino Pedreschi %X 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. %B Proceedings of the 2nd SIGSPATIAL ACM GIS 2009 International Workshop on Security and Privacy in GIS and LBS %I ACM %G eng %R 10.1145/1667502.1667510 %0 Conference Paper %B SSTD %D 2009 %T A Visual Analytics Toolkit for Cluster-Based Classification of Mobility Data %A Gennady Andrienko %A Natalia Andrienko %A S Rinzivillo %A Mirco Nanni %A Dino Pedreschi %B SSTD %P 432-435 %0 Conference Paper %B IEEE Visual Analytics Science and Tecnology (VAST 2009) %D 2009 %T Visual Cluster Analysis of Large Collections of Trajectories %A Gennady Andrienko %A Natalia Andrienko %A S Rinzivillo %A Mirco Nanni %A Dino Pedreschi %A Fosca Giannotti %B IEEE Visual Analytics Science and Tecnology (VAST 2009) %I IEEE Computer Society Press %0 Journal Article %J VLDB J. %D 2008 %T Anonymity preserving pattern discovery %A Maurizio Atzori %A Francesco Bonchi %A Fosca Giannotti %A Dino Pedreschi %B VLDB J. %V 17 %P 703-727 %G eng %0 Book Section %B Mobility, Data Mining and Privacy %D 2008 %T Privacy Protection: Regulations and Technologies, Opportunities and Threats %A Dino Pedreschi %A Francesco Bonchi %A Franco Turini %A Vassilios S. Verykios %A Maurizio Atzori %A Bradley Malin %A Bart Moelans %A Yücel Saygin %B Mobility, Data Mining and Privacy %P 101-119 %0 Journal Article %J Information Visualization %D 2008 %T Visually driven analysis of movement data by progressive clustering %A S Rinzivillo %A Dino Pedreschi %A Mirco Nanni %A Fosca Giannotti %A Natalia Andrienko %A Gennady Andrienko %B Information Visualization %I Palgrave Macmillan Ltd %V 7 %P 225-239 %0 Conference Paper %B ICDM Workshops %D 2007 %T Hiding Sensitive Trajectory Patterns %A Osman Abul %A Maurizio Atzori %A Francesco Bonchi %A Fosca Giannotti %B ICDM Workshops %P 693-698 %G eng %0 Conference Paper %B SEBD %D 2007 %T Hiding Sequences %A Osman Abul %A Maurizio Atzori %A Francesco Bonchi %A Fosca Giannotti %B SEBD %P 233-241 %G eng %0 Conference Paper %B ICDE Workshops %D 2007 %T Hiding Sequences %A Osman Abul %A Maurizio Atzori %A Francesco Bonchi %A Fosca Giannotti %B ICDE Workshops %P 147-156 %G eng %0 Conference Paper %B MDM %D 2007 %T Privacy-Aware Knowledge Discovery from Location Data %A Maurizio Atzori %A Francesco Bonchi %A Fosca Giannotti %A Dino Pedreschi %A Osman Abul %B MDM %P 283-287 %G eng %0 Conference Paper %B SAC %D 2006 %T Towards low-perturbation anonymity preserving pattern discovery %A Maurizio Atzori %A Francesco Bonchi %A Fosca Giannotti %A Dino Pedreschi %B SAC %P 588-592 %G eng %0 Journal Article %J Comput. Syst. Sci. Eng. %D 2005 %T Anonymity and data mining %A Maurizio Atzori %A Francesco Bonchi %A Fosca Giannotti %A Dino Pedreschi %B Comput. Syst. Sci. Eng. %V 20 %G eng %0 Conference Paper %B ICDM %D 2005 %T Blocking Anonymity Threats Raised by Frequent Itemset Mining %A Maurizio Atzori %A Francesco Bonchi %A Fosca Giannotti %A Dino Pedreschi %B ICDM %P 561-564 %G eng %0 Conference Paper %B ITiCSE %D 2004 %T IT4PS: information technology for problem solving %A C. Alfonsi %A Nello Scarabottolo %A Dino Pedreschi %A Maria Simi %B ITiCSE %P 241 %G eng %0 Journal Article %J Journal of Logic Programming %D 2000 %T Using Medlan to Integrate Geographical Data %A Domenico Aquilino %A Patrizia Asirelli %A A Formuso %A Chiara Renso %A Franco Turini %B Journal of Logic Programming %P 3–14 %G eng %0 Journal Article %J J. Log. Program. %D 2000 %T Using MedLan to Integrate Geographical Data %A Domenico Aquilino %A Patrizia Asirelli %A A Formuso %A Chiara Renso %A Franco Turini %B J. Log. Program. %V 43 %P 3-14 %G eng %0 Conference Paper %B IICIS %D 1998 %T The Constraint Operator of MedLan: Its Efficient Implementation and Use %A Patrizia Asirelli %A Chiara Renso %A Franco Turini %B IICIS %P 41-55 %G eng %0 Journal Article %J Annals of Mathematics and Artificial Intelligence %D 1997 %T Applying Restriction Constraint to Deductive Databases %A Domenico Aquilino %A Patrizia Asirelli %A Chiara Renso %A Franco Turini %B Annals of Mathematics and Artificial Intelligence %P 3–25 %G eng %0 Journal Article %J Ann. Math. Artif. Intell. %D 1997 %T Applying Restriction Constraints to Deductive Databases %A Domenico Aquilino %A Patrizia Asirelli %A Chiara Renso %A Franco Turini %B Ann. Math. Artif. Intell. %V 19 %P 3-25 %G eng %0 Conference Paper %B DBPL %D 1997 %T Static Analysis of Transactions for Conservative Multigranularity Locking %A Giuseppe Amato %A Fosca Giannotti %A Gianni Mainetto %B DBPL %P 413-430 %G eng %0 Journal Article %J J. Log. Program. %D 1996 %T A Closer Look at Declarative Interpretations %A Krzysztof R. Apt %A Maurizio Gabbrielli %A Dino Pedreschi %B J. Log. Program. %V 28 %P 147-180 %G eng %0 Conference Paper %B Logic in Databases %D 1996 %T Language Extensions for Semantic Integration of Deductive Databases %A Patrizia Asirelli %A Chiara Renso %A Franco Turini %B Logic in Databases %P 415-434 %G eng %0 Book Section %D 1996 %T Towards {D}eclarative {GIS} {A}nalysis %A Domenico Aquilino %A Chiara Renso %A Franco Turini %P 99–105 %G eng %0 Conference Paper %B ACM-GIS %D 1996 %T Towards Declarative GIS Analysis %A Domenico Aquilino %A Chiara Renso %A Franco Turini %B ACM-GIS %P 98-104 %G eng %0 Journal Article %D 1995 %T An Operator for Composing Deductive Databases with Theories of Constraints %A Domenico Aquilino %A Patrizia Asirelli %A Chiara Renso %A Franco Turini %P 57–70 %G eng %0 Conference Paper %B LPNMR %D 1995 %T An Operator for Composing Deductive Databases with Theories of Constraints %A Domenico Aquilino %A Patrizia Asirelli %A Chiara Renso %A Franco Turini %B LPNMR %P 57-70 %G eng %0 Conference Paper %B SEBD %D 1994 %T Conservative Multigranularity Locking for an Obiect-Oriented Persistent Language via Abstract Interpretation %A Giuseppe Amato %A Fosca Giannotti %A Gianni Mainetto %B SEBD %P 329-349 %G eng %0 Conference Paper %B VLDB %D 1993 %T Data Sharing Analysis for a Database Programming Lanaguage via Abstract Interpretation %A Giuseppe Amato %A Fosca Giannotti %A Gianni Mainetto %B VLDB %P 405-415 %G eng %0 Journal Article %J Inf. Comput. %D 1993 %T Reasoning about Termination of Pure Prolog Programs %A Krzysztof R. Apt %A Dino Pedreschi %B Inf. Comput. %V 106 %P 109-157 %G eng %0 Conference Paper %B FMLDO %D 1993 %T Static Analysis of Transactions: an Experiment of Abstract Interpretation Usage %A Giuseppe Amato %A Fosca Giannotti %A Gianni Mainetto %B FMLDO %P 19-29 %G eng %0 Conference Paper %B WSA %D 1992 %T Analysis of Concurrent Transactions in a Functional Database Programming Language %A Giuseppe Amato %A Fosca Giannotti %A Gianni Mainetto %B WSA %P 174-184 %G eng %0 Conference Paper %B TACS %D 1991 %T Proving Termination of General Prolog Programs %A Krzysztof R. Apt %A Dino Pedreschi %B TACS %P 265-289 %G eng %0 Journal Article %J IEEE Trans. Software Eng. %D 1985 %T Symbolic Semantics and Program Reduction %A Vincenzo Ambriola %A Fosca Giannotti %A Dino Pedreschi %A Franco Turini %B IEEE Trans. Software Eng. %V 11 %P 784-794 %G eng %0 Conference Paper %B Data Types and Persistence (Appin), Informal Proceedings %D 1985 %T The Type System of Galileo %A Antonio Albano %A Fosca Giannotti %A Renzo Orsini %A Dino Pedreschi %B Data Types and Persistence (Appin), Informal Proceedings %P 175-195 %G eng %0 Conference Paper %B Data Types and Persistence (Appin) %D 1985 %T The Type System of Galileo %A Antonio Albano %A Fosca Giannotti %A Renzo Orsini %A Dino Pedreschi %B Data Types and Persistence (Appin) %P 101-119 %G eng