TY - Generic T1 - GET-Viz: a library for automatic generation of visual dashboard for geographical time series T2 - 8th International Conference on Computational Social Science (IC2S2) Y1 - 2022 A1 - Fadda, Daniele A1 - Michela Natilli A1 - S Rinzivillo JF - 8th International Conference on Computational Social Science (IC2S2) CY - Chicago, USA ER - TY - JOUR T1 - Benchmarking and Survey of Explanation Methods for Black Box Models JF - CoRR Y1 - 2021 A1 - Francesco Bodria A1 - Fosca Giannotti A1 - Riccardo Guidotti A1 - Francesca Naretto A1 - Dino Pedreschi A1 - S Rinzivillo VL - abs/2102.13076 UR - https://arxiv.org/abs/2102.13076 ER - TY - JOUR T1 - Give more data, awareness and control to individual citizens, and they will help COVID-19 containment Y1 - 2021 A1 - Mirco Nanni A1 - Andrienko, Gennady A1 - Barabasi, Albert-Laszlo A1 - Boldrini, Chiara A1 - Bonchi, Francesco A1 - Cattuto, Ciro A1 - Chiaromonte, Francesca A1 - Comandé, Giovanni A1 - Conti, Marco A1 - Coté, Mark A1 - Dignum, Frank A1 - Dignum, Virginia A1 - Domingo-Ferrer, Josep A1 - Ferragina, Paolo A1 - Fosca Giannotti A1 - Riccardo Guidotti A1 - Helbing, Dirk A1 - Kaski, Kimmo A1 - Kertész, János A1 - Lehmann, Sune A1 - Lepri, Bruno A1 - Lukowicz, Paul A1 - Matwin, Stan A1 - Jiménez, David Megías A1 - Anna Monreale A1 - Morik, Katharina A1 - Oliver, Nuria A1 - Passarella, Andrea A1 - Passerini, Andrea A1 - Dino Pedreschi A1 - Pentland, Alex A1 - Pianesi, Fabio A1 - Francesca Pratesi A1 - S Rinzivillo A1 - Salvatore Ruggieri A1 - Siebes, Arno A1 - Torra, Vicenc A1 - Roberto Trasarti A1 - Hoven, Jeroen van den A1 - Vespignani, Alessandro AB - 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outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence.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. SN - 1572-8439 UR - https://link.springer.com/article/10.1007/s10676-020-09572-w JO - Ethics and Information Technology ER - TY - CONF T1 - Analysis and Visualization of Performance Indicators in University Admission Tests T2 - Formal Methods. FM 2019 International Workshops Y1 - 2020 A1 - Michela Natilli A1 - Daniele Fadda A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Licari, Federica ED - Sekerinski, Emil ED - Moreira, Nelma ED - Oliveira, José N. ED - Ratiu, Daniel ED - Riccardo Guidotti ED - Farrell, Marie ED - Luckcuck, Matt ED - Marmsoler, Diego ED - Campos, José ED - Astarte, Troy ED - Gonnord, Laure ED - Cerone, Antonio ED - Couto, Luis ED - Dongol, Brijesh ED - Kutrib, Martin ED - Monteiro, Pedro ED - Delmas, David AB - 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. JF - Formal Methods. FM 2019 International Workshops PB - Springer International Publishing CY - Cham SN - 978-3-030-54994-7 UR - https://link.springer.com/chapter/10.1007/978-3-030-54994-7_14 ER - TY - ABST T1 - Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown Y1 - 2020 A1 - Pietro Bonato A1 - Paolo Cintia A1 - Francesco Fabbri A1 - Daniele Fadda A1 - Fosca Giannotti A1 - Pier Luigi Lopalco A1 - Sara Mazzilli A1 - Mirco Nanni A1 - Luca Pappalardo A1 - Dino Pedreschi A1 - Francesco Penone A1 - S Rinzivillo A1 - Giulio Rossetti A1 - Marcello Savarese A1 - Lara Tavoschi AB - 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? UR - https://arxiv.org/abs/2004.11278 ER - TY - CONF T1 - Prediction and Explanation of Privacy Risk on Mobility Data with Neural Networks T2 - ECML PKDD 2020 Workshops Y1 - 2020 A1 - Francesca Naretto A1 - Roberto Pellungrini A1 - Nardini, Franco Maria A1 - Fosca Giannotti ED - Koprinska, Irena ED - Kamp, Michael ED - Appice, Annalisa ED - Loglisci, Corrado ED - Antonie, Luiza ED - Zimmermann, Albrecht ED - Riccardo Guidotti ED - Özgöbek, Özlem ED - Ribeiro, Rita P. ED - Gavaldà, Ricard ED - Gama, João ED - Adilova, Linara ED - Krishnamurthy, Yamuna ED - Ferreira, Pedro M. ED - Malerba, Donato ED - Medeiros, Ibéria ED - Ceci, Michelangelo ED - Manco, Giuseppe ED - Masciari, Elio ED - Ras, Zbigniew W. ED - Christen, Peter ED - Ntoutsi, Eirini ED - Schubert, Erich ED - Zimek, Arthur ED - Anna Monreale ED - Biecek, Przemyslaw ED - S Rinzivillo ED - Kille, Benjamin ED - Lommatzsch, Andreas ED - Gulla, Jon Atle AB - 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. JF - ECML PKDD 2020 Workshops PB - Springer International Publishing CY - Cham SN - 978-3-030-65965-3 UR - https://link.springer.com/chapter/10.1007/978-3-030-65965-3_34 ER - TY - JOUR T1 - The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy JF - arXiv preprint arXiv:2006.03141 Y1 - 2020 A1 - Paolo Cintia A1 - Daniele Fadda A1 - Fosca Giannotti A1 - Luca Pappalardo A1 - Giulio Rossetti A1 - Dino Pedreschi A1 - S Rinzivillo A1 - Bonato, Pietro A1 - Fabbri, Francesco A1 - Penone, Francesco A1 - Savarese, Marcello A1 - Checchi, Daniele A1 - Chiaromonte, Francesca A1 - Vineis , Paolo A1 - Guzzetta, Giorgio A1 - Riccardo, Flavia A1 - Marziano, Valentina A1 - Poletti, Piero A1 - Trentini, Filippo A1 - Bella, Antonio A1 - Andrianou, Xanthi A1 - Del Manso, Martina A1 - Fabiani, Massimo A1 - Bellino, Stefania A1 - Boros, Stefano A1 - Mateo Urdiales, Alberto A1 - Vescio, Maria Fenicia A1 - Brusaferro, Silvio A1 - Rezza, Giovanni A1 - Pezzotti, Patrizio A1 - Ajelli, Marco A1 - Merler, Stefano AB - 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. UR - https://arxiv.org/abs/2006.03141 ER - TY - JOUR T1 - A Visual Analytics Platform to Measure Performance on University Entrance Tests (Discussion Paper) Y1 - 2019 A1 - Boncoraglio, Daniele A1 - Deri, Francesca A1 - Distefano, Francesco A1 - Daniele Fadda A1 - Filippi, Giorgio A1 - Forte, Giuseppe A1 - Licari, Federica A1 - Michela Natilli A1 - Dino Pedreschi A1 - S Rinzivillo ER - TY - CHAP T1 - How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science T2 - A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years Y1 - 2018 A1 - Amato, G. A1 - Candela, L. A1 - Castelli, D. A1 - Esuli, A. A1 - Falchi, F. A1 - Gennaro, C. A1 - Fosca Giannotti A1 - Anna Monreale A1 - Mirco Nanni A1 - Pagano, P. A1 - Luca Pappalardo A1 - Dino Pedreschi A1 - Francesca Pratesi A1 - Rabitti, F. A1 - S Rinzivillo A1 - Giulio Rossetti A1 - Salvatore Ruggieri A1 - Sebastiani, F. A1 - Tesconi, M. ED - Flesca, Sergio ED - Greco, Sergio ED - Masciari, Elio ED - Saccà, Domenico AB - 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. JF - A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years PB - Springer International Publishing CY - Cham SN - 978-3-319-61893-7 UR - https://link.springer.com/chapter/10.1007%2F978-3-319-61893-7_17 ER - TY - CONF T1 - Learning Data Mining T2 - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) Y1 - 2018 A1 - Riccardo Guidotti A1 - Anna Monreale A1 - S Rinzivillo AB - 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. JF - 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA) UR - https://ieeexplore.ieee.org/document/8631453 ER - TY - JOUR T1 - NDlib: a python library to model and analyze diffusion processes over complex networks JF - International Journal of Data Science and Analytics Y1 - 2018 A1 - Giulio Rossetti A1 - Letizia Milli A1 - S Rinzivillo A1 - Alina Sirbu A1 - Dino Pedreschi A1 - Fosca Giannotti AB - 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. VL - 5 UR - https://link.springer.com/article/10.1007/s41060-017-0086-6 ER - TY - JOUR T1 - NDlib: a python library to model and analyze diffusion processes over complex networks JF - International Journal of Data Science and Analytics Y1 - 2017 A1 - Giulio Rossetti A1 - Letizia Milli A1 - S Rinzivillo A1 - Alina Sirbu A1 - Dino Pedreschi A1 - Fosca Giannotti AB - 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. ER - TY - CONF T1 - NDlib: Studying Network Diffusion Dynamics T2 - IEEE International Conference on Data Science and Advanced Analytics, DSA Y1 - 2017 A1 - Giulio Rossetti A1 - Letizia Milli A1 - S Rinzivillo A1 - Alina Sirbu A1 - Dino Pedreschi A1 - Fosca Giannotti AB - 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. JF - IEEE International Conference on Data Science and Advanced Analytics, DSA CY - Tokyo UR - https://ieeexplore.ieee.org/abstract/document/8259774 ER - TY - JOUR T1 - Never drive alone: Boosting carpooling with network analysis JF - Information Systems Y1 - 2017 A1 - Riccardo Guidotti A1 - Mirco Nanni A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Fosca Giannotti AB - 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. VL - 64 ER - TY - CONF T1 - A novel approach to evaluate community detection algorithms on ground truth T2 - 7th Workshop on Complex Networks Y1 - 2016 A1 - Giulio Rossetti A1 - Luca Pappalardo A1 - S Rinzivillo AB - 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. JF - 7th Workshop on Complex Networks PB - Springer-Verlag CY - Dijon, France UR - http://www.giuliorossetti.net/about/wp-content/uploads/2015/12/Complenet16.pdf ER - TY - CHAP T1 - Understanding human mobility with big data T2 - Solving Large Scale Learning Tasks. Challenges and Algorithms Y1 - 2016 A1 - Fosca Giannotti A1 - Lorenzo Gabrielli A1 - Dino Pedreschi A1 - S Rinzivillo AB - 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. JF - Solving Large Scale Learning Tasks. Challenges and Algorithms PB - Springer International Publishing ER - TY - JOUR T1 - Unveiling mobility complexity through complex network analysis JF - Social Network Analysis and Mining Y1 - 2016 A1 - Riccardo Guidotti A1 - Anna Monreale A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Fosca Giannotti AB - 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. VL - 6 ER - TY - CONF T1 - ComeWithMe: An Activity-Oriented Carpooling Approach T2 - 2015 {IEEE} 18th International Conference on Intelligent Transportation Systems Y1 - 2015 A1 - Vinicius Monteiro de Lira A1 - Valéria Cesário Times A1 - Chiara Renso A1 - S Rinzivillo AB - 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%. JF - 2015 {IEEE} 18th International Conference on Intelligent Transportation Systems PB - Institute of Electrical {&} Electronics Engineers ({IEEE}) UR - http://dx.doi.org/10.1109/itsc.2015.414 ER - TY - JOUR T1 - Returners and explorers dichotomy in human mobility JF - Nat Commun Y1 - 2015 A1 - Luca Pappalardo A1 - Filippo Simini A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Fosca Giannotti A1 - Barabasi, Albert-Laszlo AB - 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. VL - 6 UR - http://dx.doi.org/10.1038/ncomms9166 ER - TY - JOUR T1 - Small Area Model-Based Estimators Using Big Data Sources JF - Journal of Official Statistics Y1 - 2015 A1 - Stefano Marchetti A1 - Caterina Giusti A1 - Monica Pratesi A1 - Nicola Salvati A1 - Fosca Giannotti A1 - Dino Pedreschi A1 - S Rinzivillo A1 - Luca Pappalardo A1 - Lorenzo Gabrielli VL - 31 ER - TY - CHAP T1 - Use of Mobile Phone Data to Estimate Visitors Mobility Flows T2 - Software Engineering and Formal Methods Y1 - 2015 A1 - Lorenzo Gabrielli A1 - Barbara Furletti A1 - Fosca Giannotti A1 - Mirco Nanni A1 - S Rinzivillo AB - 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 “proxies”, such as the mobile calls data for mobility. In this paper we investigate to what extent data coming from mobile operators could be a support in producing reliable and timely estimates of intra-city mobility flows. The idea is to define an estimation method based on calling data to characterize the mobility habits of visitors at the level of a single municipality. JF - Software Engineering and Formal Methods PB - Springer International Publishing VL - 8938 UR - http://link.springer.com/chapter/10.1007%2F978-3-319-15201-1_14 ER - TY - CONF T1 - Investigating semantic regularity of human mobility lifestyle T2 - 18th International Database Engineering {&} Applications Symposium, {IDEAS} 2014, Porto, Portugal, July 7-9, 2014 Y1 - 2014 A1 - Vinicius Monteiro de Lira A1 - S Rinzivillo A1 - Chiara Renso A1 - Valéria Cesário Times A1 - Patr{\'ı}cia C. A. R. Tedesco JF - 18th International Database Engineering {&} Applications Symposium, {IDEAS} 2014, Porto, Portugal, July 7-9, 2014 PB - ACM CY - Porto, Portugal UR - http://doi.acm.org/10.1145/2628194.2628226 ER - TY - CONF T1 - {MAPMOLTY:} {A} Web Tool for Discovering Place Loyalty Based on Mobile Crowdsource Data T2 - Web Engineering, 14th International Conference, {ICWE} 2014, Toulouse, France, July 1-4, 2014. Proceedings Y1 - 2014 A1 - Vinicius Monteiro de Lira A1 - S Rinzivillo A1 - Valéria Cesário Times A1 - Chiara Renso JF - Web Engineering, 14th International Conference, {ICWE} 2014, Toulouse, France, July 1-4, 2014. Proceedings UR - http://dx.doi.org/10.1007/978-3-319-08245-5_43 ER - TY - CHAP T1 - Mobility Profiling T2 - Data Science and Simulation in Transportation Research Y1 - 2014 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Paolo Cintia A1 - Barbara Furletti A1 - Chiara Renso A1 - Lorenzo Gabrielli A1 - S Rinzivillo A1 - Fosca Giannotti AB - 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. JF - Data Science and Simulation in Transportation Research PB - IGI Global ER - TY - JOUR T1 - Privacy-by-Design in Big Data Analytics and Social Mining JF - EPJ Data Science Y1 - 2014 A1 - Anna Monreale A1 - S Rinzivillo A1 - Francesca Pratesi A1 - Fosca Giannotti A1 - Dino Pedreschi AB - 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. VL - 10 N1 - 2014:10 ER - TY - CONF T1 - The purpose of motion: Learning activities from Individual Mobility Networks T2 - International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014 Y1 - 2014 A1 - S Rinzivillo A1 - Lorenzo Gabrielli A1 - Mirco Nanni A1 - Luca Pappalardo A1 - Dino Pedreschi A1 - Fosca Giannotti JF - International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014 UR - http://dx.doi.org/10.1109/DSAA.2014.7058090 ER - TY - JOUR T1 - The retail market as a complex system JF - EPJ Data Science Y1 - 2014 A1 - Diego Pennacchioli A1 - Michele Coscia A1 - S Rinzivillo A1 - Fosca Giannotti A1 - Dino Pedreschi AB - 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’s GDP will grow. Rather than isolate actors in homogeneous groups, this strategy requires to consider the system as a whole, as the emerging pattern can be detected only as a result of the interaction between its self-organizing parts. This assumption holds also in the retail market: each customer can be seen as an independent unit maximizing its own utility function. As a consequence, the global behaviour of the retail market naturally emerges, enabling a novel description of its properties, complementary to the local pattern approach. Such task demands for a data-driven empirical framework. In this paper, we analyse a unique transaction database, recording the micro-purchases of a million customers observed for several years in the stores of a national supermarket chain. We show the emergence of the fundamental pattern of this complex system, connecting the products’ volumes of sales with the customers’ volumes of purchases. This pattern has a number of applications. We provide three of them. By enabling us to evaluate the sophistication of needs that a customer has and a product satisfies, this pattern has been applied to the task of uncovering the hierarchy of needs of the customers, providing a hint about what is the next product a customer could be interested in buying and predicting in which shop she is likely to go to buy it. VL - 3 UR - http://link.springer.com/article/10.1140/epjds/s13688-014-0033-x ER - TY - CHAP T1 - Retrieving Points of Interest from Human Systematic Movements T2 - Software Engineering and Formal Methods Y1 - 2014 A1 - Riccardo Guidotti A1 - Anna Monreale A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Fosca Giannotti AB - 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’ 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. JF - Software Engineering and Formal Methods PB - Springer International Publishing ER - TY - CONF T1 - Use of mobile phone data to estimate visitors mobility flows T2 - Proceedings of MoKMaSD Y1 - 2014 A1 - Lorenzo Gabrielli A1 - Barbara Furletti A1 - Fosca Giannotti A1 - Mirco Nanni A1 - S Rinzivillo AB - 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 “proxies”, such as the mo- bile calls data for mobility. In this paper we investigate to what extent data coming from mobile operators could be a support in producing reliable and timely estimates of intra-city mobility flows. The idea is to define an estimation method based on calling data to characterize the mobility habits of visitors at the level of a single municipality JF - Proceedings of MoKMaSD UR - http://www.di.unipi.it/mokmasd/symposium-2014/preproceedings/GabrielliEtAl-mokmasd2014.pdf ER - TY - Generic T1 - Analysis of GSM Calls Data for Understanding User Mobility Behavior T2 - IEEE Big Data Y1 - 2013 A1 - Barbara Furletti A1 - Lorenzo Gabrielli A1 - Chiara Renso A1 - S Rinzivillo JF - IEEE Big Data CY - Santa Clara, California ER - TY - CONF T1 - Comparing General Mobility and Mobility by Car T2 - Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI CBIC), 2013 BRICS Congress on Y1 - 2013 A1 - Luca Pappalardo A1 - Filippo Simini A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Fosca Giannotti JF - Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI CBIC), 2013 BRICS Congress on ER - TY - Generic T1 - Explaining the PRoduct Range Effect in Purchase Data T2 - IEEE Big Data Y1 - 2013 A1 - Diego Pennacchioli A1 - Michele Coscia A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Fosca Giannotti JF - IEEE Big Data ER - TY - CONF T1 - Pisa Tourism fluxes Observatory: deriving mobility indicators from GSM call habits T2 - NetMob Conference 2013 Y1 - 2013 A1 - Barbara Furletti A1 - Lorenzo Gabrielli A1 - Chiara Renso A1 - S Rinzivillo JF - NetMob Conference 2013 ER - TY - CONF T1 - Privacy-Aware Distributed Mobility Data Analytics T2 - SEBD Y1 - 2013 A1 - Francesca Pratesi A1 - Anna Monreale A1 - Hui Wendy Wang A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Gennady Andrienko A1 - Natalia Andrienko AB - 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. JF - SEBD CY - Roccella Jonica ER - TY - CHAP T1 - Privacy-Preserving Distributed Movement Data Aggregation T2 - Geographic Information Science at the Heart of Europe Y1 - 2013 A1 - Anna Monreale A1 - Hui Wendy Wang A1 - Francesca Pratesi A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Gennady Andrienko A1 - Natalia Andrienko ED - Vandenbroucke, Danny ED - Bucher, Bénédicte ED - Crompvoets, Joep AB - 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. JF - Geographic Information Science at the Heart of Europe T3 - Lecture Notes in Geoinformation and Cartography PB - Springer International Publishing SN - 978-3-319-00614-7 UR - http://dx.doi.org/10.1007/978-3-319-00615-4_13 ER - TY - JOUR T1 - Scalable Analysis of Movement Data for Extracting and Exploring Significant Places JF - IEEE Transactions on Visualization and Computer Graphics Y1 - 2013 A1 - Gennady Andrienko A1 - Natalia Andrienko A1 - C. Hunter A1 - S Rinzivillo A1 - Stefan Wrobel VL - 19 ER - TY - JOUR T1 - Spatial and Temporal Evaluation of Network-based Analysis of Human Mobility JF - Social Network Analysis and Mining Y1 - 2013 A1 - Michele Coscia A1 - S Rinzivillo A1 - Fosca Giannotti A1 - Dino Pedreschi VL - to appear ER - TY - JOUR T1 - {Understanding the patterns of car travel} JF - The European Physical Journal Special Topics Y1 - 2013 A1 - Luca Pappalardo A1 - S Rinzivillo A1 - Qu, Zehui A1 - Dino Pedreschi A1 - Fosca Giannotti AB - {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.} VL - 215 UR - http://dx.doi.org/10.1140/epjst%252fe2013-01715-5 ER - TY - CONF T1 - Where Have You Been Today? Annotating Trajectories with DayTag T2 - International Conference on Spatial and Spatio-temporal Databases (SSTD) Y1 - 2013 A1 - S Rinzivillo A1 - Fernando de Lucca Siqueira A1 - Lorenzo Gabrielli A1 - Chiara Renso A1 - Vania Bogorny JF - International Conference on Spatial and Spatio-temporal Databases (SSTD) ER - TY - RPRT T1 - Analisi di Mobilita' con dati eterogenei Y1 - 2012 A1 - Barbara Furletti A1 - Roberto Trasarti A1 - Lorenzo Gabrielli A1 - S Rinzivillo A1 - Luca Pappalardo A1 - Fosca Giannotti PB - ISTI - CNR CY - Pisa ER - TY - JOUR T1 - Data Science for Simulating the Era of Electric Vehicles JF - KI - Künstliche Intelligenz Y1 - 2012 A1 - Davy Janssens A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - S Rinzivillo ER - TY - JOUR T1 - Discovering the Geographical Borders of Human Mobility JF - KI - Künstliche Intelligenz Y1 - 2012 A1 - S Rinzivillo A1 - Simone Mainardi A1 - Fabio Pezzoni A1 - Michele Coscia A1 - Fosca Giannotti A1 - Dino Pedreschi AB - 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. UR - https://link.springer.com/article/10.1007%2Fs13218-012-0181-8 ER - TY - CONF T1 - Identifying users profiles from mobile calls habits T2 - ACM SIGKDD International Workshop on Urban Computing Y1 - 2012 A1 - Barbara Furletti A1 - Lorenzo Gabrielli A1 - Chiara Renso A1 - S Rinzivillo AB - 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. JF - ACM SIGKDD International Workshop on Urban Computing PB - ACM New York, NY, USA ©2012 CY - Beijing, China SN - 978-1-4503-1542-5 UR - 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 ER - TY - Generic T1 - Optimal Spatial Resolution for the Analysis of Human Mobility T2 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining Y1 - 2012 A1 - Michele Coscia A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Fosca Giannotti JF - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining CY - Instanbul, Turkey ER - TY - Generic T1 - From Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data T2 - IEEE Conference on Visual Analytics Science and Technology Y1 - 2011 A1 - Gennady Andrienko A1 - Natalia Andrienko A1 - Cristophe Hurter A1 - S Rinzivillo A1 - Stefan Wrobel JF - IEEE Conference on Visual Analytics Science and Technology ER - TY - CONF T1 - Traffic Jams Detection Using Flock Mining T2 - ECML/PKDD (3) Y1 - 2011 A1 - Rebecca Ong A1 - Fabio Pinelli A1 - Roberto Trasarti A1 - Mirco Nanni A1 - Chiara Renso A1 - S Rinzivillo A1 - Fosca Giannotti JF - ECML/PKDD (3) ER - TY - JOUR T1 - Unveiling the complexity of human mobility by querying and mining massive trajectory data JF - VLDB J. Y1 - 2011 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fabio Pinelli A1 - Chiara Renso A1 - S Rinzivillo A1 - Roberto Trasarti VL - 20 ER - TY - CONF T1 - Who/Where Are My New Customers? T2 - ISMIS Industrial Session Y1 - 2011 A1 - S Rinzivillo A1 - Salvatore Ruggieri JF - ISMIS Industrial Session ER - TY - CONF T1 - Exploring Real Mobility Data with M-Atlas T2 - ECML/PKDD (3) Y1 - 2010 A1 - Roberto Trasarti A1 - S Rinzivillo A1 - Fabio Pinelli A1 - Mirco Nanni A1 - Anna Monreale A1 - Chiara Renso A1 - Dino Pedreschi A1 - Fosca Giannotti AB - 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. JF - ECML/PKDD (3) ER - TY - Generic T1 - A Generalisation-based Approach to Anonymising Movement Data T2 - 13th AGILE conference on Geographic Information Science Y1 - 2010 A1 - Gennady Andrienko A1 - Natalia Andrienko A1 - Fosca Giannotti A1 - Anna Monreale A1 - Dino Pedreschi A1 - S Rinzivillo AB - 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. JF - 13th AGILE conference on Geographic Information Science UR - http://agile2010.dsi.uminho.pt/pen/ShortPapers_PDF%5C122_DOC.pdf ER - TY - CONF T1 - Mobility data mining: discovering movement patterns from trajectory data T2 - Computational Transportation Science Y1 - 2010 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fabio Pinelli A1 - Chiara Renso A1 - S Rinzivillo A1 - Roberto Trasarti JF - Computational Transportation Science ER - TY - JOUR T1 - Movement Data Anonymity through Generalization JF - Transactions on Data Privacy Y1 - 2010 A1 - Anna Monreale A1 - Gennady Andrienko A1 - Natalia Andrienko A1 - Fosca Giannotti A1 - Dino Pedreschi A1 - S Rinzivillo A1 - Stefan Wrobel AB - 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. VL - 3 UR - http://www.tdp.cat/issues/abs.a045a10.php ER - TY - CHAP T1 - Spatio-temporal clustering T2 - Data Mining and Knowledge Discovery Handbook Y1 - 2010 A1 - Slava Kisilevich A1 - Florian Mansmann A1 - Mirco Nanni A1 - S Rinzivillo JF - Data Mining and Knowledge Discovery Handbook ER - TY - CONF T1 - GeoPKDD – Geographic Privacy-aware Knowledge Discovery T2 - The European Future Technologies Conference (FET 2009) Y1 - 2009 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Chiara Renso A1 - S Rinzivillo A1 - Roberto Trasarti JF - The European Future Technologies Conference (FET 2009) ER - TY - CONF T1 - A Visual Analytics Toolkit for Cluster-Based Classification of Mobility Data T2 - SSTD Y1 - 2009 A1 - Gennady Andrienko A1 - Natalia Andrienko A1 - S Rinzivillo A1 - Mirco Nanni A1 - Dino Pedreschi JF - SSTD ER - TY - CONF T1 - Visual Cluster Analysis of Large Collections of Trajectories T2 - IEEE Visual Analytics Science and Tecnology (VAST 2009) Y1 - 2009 A1 - Gennady Andrienko A1 - Natalia Andrienko A1 - S Rinzivillo A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fosca Giannotti JF - IEEE Visual Analytics Science and Tecnology (VAST 2009) PB - IEEE Computer Society Press ER - TY - CHAP T1 - Knowledge Discovery from Geographical Data T2 - Mobility, Data Mining and Privacy Y1 - 2008 A1 - S Rinzivillo A1 - Franco Turini A1 - Vania Bogorny A1 - Christine Körner A1 - Bart Kuijpers A1 - Michael May JF - Mobility, Data Mining and Privacy ER - TY - JOUR T1 - Visually driven analysis of movement data by progressive clustering JF - Information Visualization Y1 - 2008 A1 - S Rinzivillo A1 - Dino Pedreschi A1 - Mirco Nanni A1 - Fosca Giannotti A1 - Natalia Andrienko A1 - Gennady Andrienko PB - Palgrave Macmillan Ltd VL - 7 ER - TY - JOUR T1 - Knowledge discovery from spatial transactions JF - Journal of Intelligent Information Systems Y1 - 2007 A1 - S Rinzivillo A1 - Franco Turini VL - 28 ER - TY - CONF T1 - Examples of Integration of Induction and Deduction in Knowledge Discovery T2 - Reasoning, Action and Interaction in AI Theories and Systems Y1 - 2006 A1 - Franco Turini A1 - Miriam Baglioni A1 - Barbara Furletti A1 - S Rinzivillo JF - Reasoning, Action and Interaction in AI Theories and Systems ER - TY - CHAP T1 - Examples of Integration of Induction and Deduction in Knowledge Discovery T2 - Reasoning, Action and Interaction in AI Theories and Systems Y1 - 2006 A1 - Franco Turini A1 - Miriam Baglioni A1 - Barbara Furletti A1 - S Rinzivillo JF - Reasoning, Action and Interaction in AI Theories and Systems T3 - LNAI VL - 4155 UR - http://www.springerlink.com/content/m400v4507476n18g/fulltext.pdf ER - TY - CONF T1 - Extracting spatial association rules from spatial transactions T2 - ACM GIS Y1 - 2005 A1 - S Rinzivillo A1 - Franco Turini JF - ACM GIS ER - TY - CONF T1 - Classification in Geographical Information Systems T2 - PKDD Y1 - 2004 A1 - S Rinzivillo A1 - Franco Turini JF - PKDD ER - TY - CONF T1 - Using Spin to Generate Tests from ASM Specifications T2 - Abstract State Machines Y1 - 2003 A1 - Angelo Gargantini A1 - Elvinia Riccobene A1 - S Rinzivillo JF - Abstract State Machines ER -