@article {1540,
	title = {Explaining Black Box with visual exploration of Latent Space},
	journal = {EuroVis{\textendash}Short Papers},
	year = {2022},
	abstract = {Autoencoders are a powerful yet opaque feature reduction technique, on top of which we propose a novel way for the joint visual exploration of both latent and real space. By interactively exploiting the mapping between latent and real features, it is possible to unveil the meaning of latent features while providing deeper insight into the original variables. To achieve this goal, we exploit and re-adapt existing approaches from eXplainable Artificial Intelligence (XAI) to understand the relationships between the input and latent features. The uncovered relationships between input features and latent ones allow the user to understand the data structure concerning external variables such as the predictions of a classification model. We developed an interactive framework that visually explores the latent space and allows the user to understand the relationships of the input features with model prediction.},
	url = {https://diglib.eg.org/xmlui/bitstream/handle/10.2312/evs20221098/085-089.pdf?sequence=1},
	author = {Bodria, Francesco and Rinzivillo, Salvatore and Fadda, Daniele and Guidotti, Riccardo and Giannotti, Fosca and Pedreschi, Dino}
}
@proceedings {1509,
	title = {GET-Viz: a library for automatic generation of visual dashboard for geographical time series},
	year = {2022},
	address = {Chicago, USA},
	author = {Fadda, Daniele and Michela Natilli and S Rinzivillo}
}
@article {1476,
	title = {Benchmarking and Survey of Explanation Methods for Black Box Models},
	journal = {CoRR},
	volume = {abs/2102.13076},
	year = {2021},
	url = {https://arxiv.org/abs/2102.13076},
	author = {Francesco Bodria and Fosca Giannotti and Riccardo Guidotti and Francesca Naretto and Dino Pedreschi and S Rinzivillo}
}
@article {1400,
	title = {Give more data, awareness and control to individual citizens, and they will help COVID-19 containment},
	year = {2021},
	month = {2021/02/02},
	abstract = {The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the {\textquotedblleft}phase 2{\textquotedblright} of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens{\textquoteright} privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens{\textquoteright} {\textquotedblleft}personal data stores{\textquotedblright}, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates{\textemdash}if and when they want and for specific aims{\textemdash}with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.},
	isbn = {1572-8439},
	doi = {https://doi.org/10.1007/s10676-020-09572-w},
	url = {https://link.springer.com/article/10.1007/s10676-020-09572-w},
	author = {Mirco Nanni and Andrienko, Gennady and Barabasi, Albert-Laszlo and Boldrini, Chiara and Bonchi, Francesco and Cattuto, Ciro and Chiaromonte, Francesca and Comand{\'e}, Giovanni and Conti, Marco and Cot{\'e}, Mark and Dignum, Frank and Dignum, Virginia and Domingo-Ferrer, Josep and Ferragina, Paolo and Fosca Giannotti and Riccardo Guidotti and Helbing, Dirk and Kaski, Kimmo and Kert{\'e}sz, J{\'a}nos and Lehmann, Sune and Lepri, Bruno and Lukowicz, Paul and Matwin, Stan and Jim{\'e}nez, David Meg{\'\i}as and Anna Monreale and Morik, Katharina and Oliver, Nuria and Passarella, Andrea and Passerini, Andrea and Dino Pedreschi and Pentland, Alex and Pianesi, Fabio and Francesca Pratesi and S Rinzivillo and Salvatore Ruggieri and Siebes, Arno and Torra, Vicenc and Roberto Trasarti and Hoven, Jeroen van den and Vespignani, Alessandro}
}
@conference {1408,
	title = {Analysis and Visualization of Performance Indicators in University Admission Tests},
	booktitle = {Formal Methods. FM 2019 International Workshops},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {This paper presents an analytical platform for evaluation of the performance and anomaly detection of tests for admission to public universities in Italy. Each test is personalized for each student and is composed of a series of questions, classified on different domains (e.g. maths, science, logic, etc.). Since each test is unique for composition, it is crucial to guarantee a similar level of difficulty for all the tests in a session. For this reason, to each question, it is assigned a level of difficulty from a domain expert. Thus, the general difficultness of a test depends on the correct classification of each item. We propose two approaches to detect outliers. A visualization-based approach using dynamic filter and responsive visual widgets. A data mining approach to evaluate the performance of the different questions for five years. We used clustering to group the questions according to a set of performance indicators to provide labeling of the data-driven level of difficulty. The measured level is compared with the a priori assigned by experts. The misclassifications are then highlighted to the expert, who will be able to refine the question or the classification. Sequential pattern mining is used to check if biases are present in the composition of the tests and their performance. This analysis is meant to exclude overlaps or direct dependencies among questions. Analyzing co-occurrences we are able to state that the composition of each test is fair and uniform for all the students, even on several sessions. The analytical results are presented to the expert through a visual web application that loads the analytical data and indicators and composes an interactive dashboard. The user may explore the patterns and models extracted by filtering and changing thresholds and analytical parameters.},
	isbn = {978-3-030-54994-7},
	doi = {https://doi.org/10.1007/978-3-030-54994-7_14},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-54994-7_14},
	author = {Michela Natilli and Daniele Fadda and S Rinzivillo and Dino Pedreschi and Licari, Federica},
	editor = {Sekerinski, Emil and Moreira, Nelma and Oliveira, Jos{\'e} N. and Ratiu, Daniel and Riccardo Guidotti and Farrell, Marie and Luckcuck, Matt and Marmsoler, Diego and Campos, Jos{\'e} and Astarte, Troy and Gonnord, Laure and Cerone, Antonio and Couto, Luis and Dongol, Brijesh and Kutrib, Martin and Monteiro, Pedro and Delmas, David}
}
@booklet {1425,
	title = {Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown},
	year = {2020},
	abstract = {Understanding collective mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in stand-by to fight the diffusion of the epidemics. In this report, we use mobile phone data to infer the movements of people between Italian provinces and municipalities, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modelling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. We address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?},
	doi = {https://dx.doi.org/10.32079/ISTI-TR-2020/005},
	url = {https://arxiv.org/abs/2004.11278},
	author = {Pietro Bonato and Paolo Cintia and Francesco Fabbri and Daniele Fadda and Fosca Giannotti and Pier Luigi Lopalco and Sara Mazzilli and Mirco Nanni and Luca Pappalardo and Dino Pedreschi and Francesco Penone and S Rinzivillo and Giulio Rossetti and Marcello Savarese and Lara Tavoschi}
}
@conference {1430,
	title = {Prediction and Explanation of Privacy Risk on Mobility Data with Neural Networks},
	booktitle = {ECML PKDD 2020 Workshops},
	year = {2020},
	month = {2020//},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {The analysis of privacy risk for mobility data is a fundamental part of any privacy-aware process based on such data. Mobility data are highly sensitive. Therefore, the correct identification of the privacy risk before releasing the data to the public is of utmost importance. However, existing privacy risk assessment frameworks have high computational complexity. To tackle these issues, some recent work proposed a solution based on classification approaches to predict privacy risk using mobility features extracted from the data. In this paper, we propose an improvement of this approach by applying long short-term memory (LSTM) neural networks to predict the privacy risk directly from original mobility data. We empirically evaluate privacy risk on real data by applying our LSTM-based approach. Results show that our proposed method based on a LSTM network is effective in predicting the privacy risk with results in terms of F1 of up~to 0.91. Moreover, to explain the predictions of our model, we employ a state-of-the-art explanation algorithm, Shap. We explore the resulting explanation, showing how it is possible to provide effective predictions while explaining them to the end-user.},
	isbn = {978-3-030-65965-3},
	doi = {https://doi.org/10.1007/978-3-030-65965-3_34},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-65965-3_34},
	author = {Francesca Naretto and Roberto Pellungrini and Nardini, Franco Maria and Fosca Giannotti},
	editor = {Koprinska, Irena and Kamp, Michael and Appice, Annalisa and Loglisci, Corrado and Antonie, Luiza and Zimmermann, Albrecht and Riccardo Guidotti and {\"O}zg{\"o}bek, {\"O}zlem and Ribeiro, Rita P. and Gavald{\`a}, Ricard and Gama, Jo{\~a}o and Adilova, Linara and Krishnamurthy, Yamuna and Ferreira, Pedro M. and Malerba, Donato and Medeiros, Ib{\'e}ria and Ceci, Michelangelo and Manco, Giuseppe and Masciari, Elio and Ras, Zbigniew W. and Christen, Peter and Ntoutsi, Eirini and Schubert, Erich and Zimek, Arthur and Anna Monreale and Biecek, Przemyslaw and S Rinzivillo and Kille, Benjamin and Lommatzsch, Andreas and Gulla, Jon Atle}
}
@article {1339,
	title = {The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy},
	journal = {arXiv preprint arXiv:2006.03141},
	year = {2020},
	abstract = {We describe in this report our studies to understand the relationship between human mobility and the spreading of COVID-19, as an aid to manage the restart of the social and economic activities after the lockdown and monitor the epidemics in the coming weeks and months. We compare the evolution (from January to May 2020) of the daily mobility flows in Italy, measured by means of nation-wide mobile phone data, and the evolution of transmissibility, measured by the net reproduction number, i.e., the mean number of secondary infections generated by one primary infector in the presence of control interventions and human behavioural adaptations. We find a striking relationship between the negative variation of mobility flows and the net reproduction number, in all Italian regions, between March 11th and March 18th, when the country entered the lockdown. This observation allows us to quantify the time needed to "switch off" the country mobility (one week) and the time required to bring the net reproduction number below 1 (one week). A reasonably simple regression model provides evidence that the net reproduction number is correlated with a region{\textquoteright}s incoming, outgoing and internal mobility. We also find a strong relationship between the number of days above the epidemic threshold before the mobility flows reduce significantly as an effect of lockdowns, and the total number of confirmed SARS-CoV-2 infections per 100k inhabitants, thus indirectly showing the effectiveness of the lockdown and the other non-pharmaceutical interventions in the containment of the contagion. Our study demonstrates the value of "big" mobility data to the monitoring of key epidemic indicators to inform choices as the epidemics unfolds in the coming months.},
	url = {https://arxiv.org/abs/2006.03141},
	author = {Paolo Cintia and Daniele Fadda and Fosca Giannotti and Luca Pappalardo and Giulio Rossetti and Dino Pedreschi and S Rinzivillo and Bonato, Pietro and Fabbri, Francesco and Penone, Francesco and Savarese, Marcello and Checchi, Daniele and Chiaromonte, Francesca and Vineis , Paolo and Guzzetta, Giorgio and Riccardo, Flavia and Marziano, Valentina and Poletti, Piero and Trentini, Filippo and Bella, Antonio and Andrianou, Xanthi and Del Manso, Martina and Fabiani, Massimo and Bellino, Stefania and Boros, Stefano and Mateo Urdiales, Alberto and Vescio, Maria Fenicia and Brusaferro, Silvio and Rezza, Giovanni and Pezzotti, Patrizio and Ajelli, Marco and Merler, Stefano}
}
@article {1272,
	title = {A Visual Analytics Platform to Measure Performance on University Entrance Tests (Discussion Paper)},
	year = {2019},
	author = {Boncoraglio, Daniele and Deri, Francesca and Distefano, Francesco and Daniele Fadda and Filippi, Giorgio and Forte, Giuseppe and Licari, Federica and Michela Natilli and Dino Pedreschi and S Rinzivillo}
}
@inbook {1422,
	title = {How Data Mining and Machine Learning Evolved from Relational Data Base to Data Science},
	booktitle = {A Comprehensive Guide Through the Italian Database Research Over the Last 25 Years},
	year = {2018},
	pages = {287 - 306},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	abstract = {During the last 35 years, data management principles such as physical and logical independence, declarative querying and cost-based optimization have led to profound pervasiveness of relational databases in any kind of organization. More importantly, these technical advances have enabled the first round of business intelligence applications and laid the foundation for managing and analyzing Big Data today.},
	isbn = {978-3-319-61893-7},
	doi = {https://doi.org/10.1007/978-3-319-61893-7_17},
	url = {https://link.springer.com/chapter/10.1007\%2F978-3-319-61893-7_17},
	author = {Amato, G. and Candela, L. and Castelli, D. and Esuli, A. and Falchi, F. and Gennaro, C. and Fosca Giannotti and Anna Monreale and Mirco Nanni and Pagano, P. and Luca Pappalardo and Dino Pedreschi and Francesca Pratesi and Rabitti, F. and S Rinzivillo and Giulio Rossetti and Salvatore Ruggieri and Sebastiani, F. and Tesconi, M.},
	editor = {Flesca, Sergio and Greco, Sergio and Masciari, Elio and Sacc{\`a}, Domenico}
}
@conference {1427,
	title = {Learning Data Mining},
	booktitle = {2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)},
	year = {2018},
	month = {2018},
	abstract = {In the last decade the usage and study of data mining and machine learning algorithms have received an increasing attention from several and heterogeneous fields of research. Learning how and why a certain algorithm returns a particular result, and understanding which are the main problems connected to its execution is a hot topic in the education of data mining methods. In order to support data mining beginners, students, teachers, and researchers we introduce a novel didactic environment. The Didactic Data Mining Environment (DDME) allows to execute a data mining algorithm on a dataset and to observe the algorithm behavior step by step to learn how and why a certain result is returned. DDME can be practically exploited by teachers and students for having a more interactive learning of data mining. Indeed, on top of the core didactic library, we designed a visual platform that allows online execution of experiments and the visualization of the algorithm steps. The visual platform abstracts the coding activity and makes available the execution of algorithms to non-technicians.
},
	doi = {https://doi.org/10.1109/DSAA.2018.00047},
	url = {https://ieeexplore.ieee.org/document/8631453},
	author = {Riccardo Guidotti and Anna Monreale and S Rinzivillo}
}
@article {1047,
	title = {NDlib: a python library to model and analyze diffusion processes over complex networks},
	journal = {International Journal of Data Science and Analytics},
	volume = {5},
	number = {1},
	year = {2018},
	pages = {61{\textendash}79},
	abstract = {Nowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground. To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.},
	doi = {10.1007/s41060-017-0086-6},
	url = {https://link.springer.com/article/10.1007/s41060-017-0086-6},
	author = {Giulio Rossetti and Letizia Milli and S Rinzivillo and Alina Sirbu and Dino Pedreschi and Fosca Giannotti}
}
@article {1020,
	title = {NDlib: a python library to model and analyze diffusion processes over complex networks},
	journal = {International Journal of Data Science and Analytics},
	year = {2017},
	pages = {1{\textendash}19},
	abstract = {Nowadays the analysis of dynamics of and on networks represents a hot topic in the social network analysis playground.To support students, teachers, developers and researchers, in this work we introduce a novel framework, namely NDlib, an environment designed to describe diffusion simulations. NDlib is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. For this reason, upon NDlib, we designed a simulation server that allows remote execution of experiments as well as an online visualization tool that abstracts its programmatic interface and makes available the simulation platform to non-technicians.},
	author = {Giulio Rossetti and Letizia Milli and S Rinzivillo and Alina Sirbu and Dino Pedreschi and Fosca Giannotti}
}
@conference {1022,
	title = {NDlib: Studying Network Diffusion Dynamics},
	booktitle = {IEEE International Conference on Data Science and Advanced Analytics, DSA},
	year = {2017},
	address = {Tokyo},
	abstract = {Nowadays the analysis of diffusive phenomena occurring on top of complex networks represents a hot topic in the Social Network Analysis playground. In order to support students, teachers, developers and researchers in this work we introduce a novel simulation framework, ND LIB . ND LIB is designed to be a multi-level ecosystem that can be fruitfully used by different user segments. Upon the diffusion library, we designed a simulation server that allows remote execution of experiments and an online visualization tool that abstract the programmatic interface and makes available the simulation platform to non-technicians.},
	doi = {https://doi.org/10.1109/DSAA.2017.6},
	url = {https://ieeexplore.ieee.org/abstract/document/8259774},
	author = {Giulio Rossetti and Letizia Milli and S Rinzivillo and Alina Sirbu and Dino Pedreschi and Fosca Giannotti}
}
@article {956,
	title = {Never drive alone: Boosting carpooling with network analysis},
	journal = {Information Systems},
	volume = {64},
	year = {2017},
	pages = {237{\textendash}257},
	abstract = {Carpooling, i.e., the act where two or more travelers share the same car for a common trip, is one of the possibilities brought forward to reduce traffic and its externalities, but experience shows that it is difficult to boost the adoption of carpooling to significant levels. In our study, we analyze the potential impact of carpooling as a collective phenomenon emerging from people׳s mobility, by network analytics. Based on big mobility data from travelers in a given territory, we construct the network of potential carpooling, where nodes correspond to the users and links to possible shared trips, and analyze the structural and topological properties of this network, such as network communities and node ranking, to the purpose of highlighting the subpopulations with higher chances to create a carpooling community, and the propensity of users to be either drivers or passengers in a shared car. Our study is anchored to reality thanks to a large mobility dataset, consisting of the complete one-month-long GPS trajectories of approx. 10\% circulating cars in Tuscany. We also analyze the aggregated outcome of carpooling by means of empirical simulations, showing how an assignment policy exploiting the network analytic concepts of communities and node rankings minimizes the number of single occupancy vehicles observed after carpooling.},
	doi = {10.1016/j.is.2016.03.006},
	author = {Riccardo Guidotti and Mirco Nanni and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@conference {822,
	title = {A novel approach to evaluate community detection algorithms on ground truth},
	booktitle = {7th Workshop on Complex Networks},
	year = {2016},
	publisher = {Springer-Verlag},
	organization = {Springer-Verlag},
	address = {Dijon, France},
	abstract = {Evaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i.e., Normalized Mutual Information). In this paper we propose a novel approach aimed at evaluating the adherence of a community partition to the ground truth: our methodology provides more information than the state-of-the-art ones and is fast to compute on large-scale networks. We evaluate its correctness by applying it to six popular community detection algorithms on four large-scale network datasets. Experimental results show how our approach allows to easily evaluate the obtained communities on the ground truth and to characterize the quality of community detection algorithms.},
	doi = {10.1007/978-3-319-30569-1_10},
	url = {http://www.giuliorossetti.net/about/wp-content/uploads/2015/12/Complenet16.pdf},
	author = {Giulio Rossetti and Luca Pappalardo and S Rinzivillo}
}
@inbook {964,
	title = {Understanding human mobility with big data},
	booktitle = {Solving Large Scale Learning Tasks. Challenges and Algorithms},
	year = {2016},
	pages = {208{\textendash}220},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {The paper illustrates basic methods of mobility data mining, designed to extract from the big mobility data the patterns of collective movement behavior, i.e., discover the subgroups of travelers characterized by a common purpose, profiles of individual movement activity, i.e., characterize the routine mobility of each traveler. We illustrate a number of concrete case studies where mobility data mining is put at work to create powerful analytical services for policy makers, businesses, public administrations, and individual citizens.},
	doi = {10.1007/978-3-319-41706-6_10},
	author = {Fosca Giannotti and Lorenzo Gabrielli and Dino Pedreschi and S Rinzivillo}
}
@article {867,
	title = {Unveiling mobility complexity through complex network analysis},
	journal = {Social Network Analysis and Mining},
	volume = {6},
	number = {1},
	year = {2016},
	pages = {59},
	abstract = {The availability of massive digital traces of individuals is offering a series of novel insights on the understanding of patterns characterizing human mobility. Many studies try to semantically enrich mobility data with annotations about human activities. However, these approaches either focus on places with high frequencies (e.g., home and work), or relay on background knowledge (e.g., public available points of interest). In this paper, we depart from the concept of frequency and we focus on a high level representation of mobility using network analytics. The visits of each driver to each systematic destination are modeled as links in a bipartite network where a set of nodes represents drivers and the other set represents places. We extract such network from two real datasets of human mobility based, respectively, on GPS and GSM data. We introduce the concept of mobility complexity of drivers and places as a ranking analysis over the nodes of these networks. In addition, by means of community discovery analysis, we differentiate subgroups of drivers and places according both to their homogeneity and to their mobility complexity.},
	doi = {10.1007/s13278-016-0369-2},
	author = {Riccardo Guidotti and Anna Monreale and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@conference {774,
	title = {ComeWithMe: An Activity-Oriented Carpooling Approach},
	booktitle = {2015 {IEEE} 18th International Conference on Intelligent Transportation Systems},
	year = {2015},
	month = {09/2015},
	publisher = {Institute of Electrical {\&} Electronics Engineers ({IEEE})},
	organization = {Institute of Electrical {\&} Electronics Engineers ({IEEE})},
	abstract = {The interest in carpooling is increasing due to the need to reduce traffic and noise pollution. Most of the available approaches and systems are route oriented, where driver and passengers are matched when the destination location is the same. ComeWithMe offers a new perspective: the destination is the intended activity instead of a location. This novel matching method is aimed to boost the possibilities of rides if passenger reaches a different location maintaining the activity. We conducted experiments using a real data set of trajectories and our results showed that the proposed matching algorithm improved the traditional carpooling approach in more than 80\%.},
	doi = {10.1109/itsc.2015.414},
	url = {http://dx.doi.org/10.1109/itsc.2015.414},
	author = {Vinicius Monteiro de Lira and Val{\'e}ria Ces{\'a}rio Times and Chiara Renso and S Rinzivillo}
}
@article {723,
	title = {Returners and explorers dichotomy in human mobility},
	journal = {Nat Commun},
	volume = {6},
	year = {2015},
	month = {09},
	abstract = {The availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.},
	url = {http://dx.doi.org/10.1038/ncomms9166},
	author = {Luca Pappalardo and Filippo Simini and S Rinzivillo and Dino Pedreschi and Fosca Giannotti and Barabasi, Albert-Laszlo}
}
@article {724,
	title = {Small Area Model-Based Estimators Using Big Data Sources},
	journal = {Journal of Official Statistics},
	volume = {31},
	number = {2},
	year = {2015},
	pages = {263{\textendash}281},
	author = {Stefano Marchetti and Caterina Giusti and Monica Pratesi and Nicola Salvati and Fosca Giannotti and Dino Pedreschi and S Rinzivillo and Luca Pappalardo and Lorenzo Gabrielli}
}
@inbook {777,
	title = {Use of Mobile Phone Data to Estimate Visitors Mobility Flows},
	booktitle = {Software Engineering and Formal Methods},
	volume = {8938},
	number = {Lecture Notes in Computer Science},
	year = {2015},
	pages = {214-226},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {Big Data originating from the digital breadcrumbs of human activities, sensed as by-product of the technologies that we use for our daily activities, allows us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data {\textquotedblleft}proxies{\textquotedblright}, such as the mobile calls data for mobility. In this paper we investigate to what extent data coming from mobile operators could be a support in producing reliable and timely estimates of intra-city mobility flows. The idea is to define an estimation method based on calling data to characterize the mobility habits of visitors at the level of a single municipality.},
	issn = {978-3-319-15200-4},
	doi = {10.1007/978-3-319-15201-1_14},
	url = {http://link.springer.com/chapter/10.1007\%2F978-3-319-15201-1_14},
	author = {Lorenzo Gabrielli and Barbara Furletti and Fosca Giannotti and Mirco Nanni and S Rinzivillo}
}
@conference {ideas/LiraRRTT14,
	title = {Investigating semantic regularity of human mobility lifestyle},
	booktitle = {18th International Database Engineering {\&} Applications Symposium, {IDEAS} 2014, Porto, Portugal, July 7-9, 2014},
	year = {2014},
	pages = {314{\textendash}317},
	publisher = {ACM},
	organization = {ACM},
	address = {Porto, Portugal},
	doi = {10.1145/2628194.2628226},
	url = {http://doi.acm.org/10.1145/2628194.2628226},
	author = {Vinicius Monteiro de Lira and S Rinzivillo and Chiara Renso and Val{\'e}ria Ces{\'a}rio Times and Patr{\'{\i}}cia C. A. R. Tedesco}
}
@conference {icwe/LiraRTR14,
	title = {{MAPMOLTY:} {A} Web Tool for Discovering Place Loyalty Based on Mobile Crowdsource Data},
	booktitle = {Web Engineering, 14th International Conference, {ICWE} 2014, Toulouse, France, July 1-4, 2014. Proceedings},
	year = {2014},
	pages = {528{\textendash}531},
	doi = {10.1007/978-3-319-08245-5_43},
	url = {http://dx.doi.org/10.1007/978-3-319-08245-5_43},
	author = {Vinicius Monteiro de Lira and S Rinzivillo and Val{\'e}ria Ces{\'a}rio Times and Chiara Renso}
}
@inbook {575,
	title = {Mobility Profiling},
	booktitle = {Data Science and Simulation in Transportation Research},
	year = {2014},
	pages = {1-29},
	publisher = {IGI Global},
	organization = {IGI Global},
	chapter = {1},
	abstract = {The ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network. },
	doi = {10.4018/978-1-4666-4920-0.ch001},
	author = {Mirco Nanni and Roberto Trasarti and Paolo Cintia and Barbara Furletti and Chiara Renso and Lorenzo Gabrielli and S Rinzivillo and Fosca Giannotti}
}
@article {EPJ14,
	title = {Privacy-by-Design in Big Data Analytics and Social Mining},
	journal = {EPJ Data Science},
	volume = {10},
	year = {2014},
	note = {2014:10},
	abstract = {Privacy is ever-growing concern in our society and is becoming a fundamental aspect to take into account when one wants to use, publish and analyze data involving human personal sensitive information. Unfortunately, it is increasingly hard to transform the data in a way that it protects sensitive information: we live in the era of big data characterized by unprecedented opportunities to sense, store and analyze social data describing human activities in great detail and resolution. As a result, privacy preservation simply cannot be accomplished by de-identification alone. In this paper, we propose the privacy-by-design paradigm to develop technological frameworks for countering the threats of undesirable, unlawful effects of privacy violation, without obstructing the knowledge discovery opportunities of social mining and big data analytical technologies. Our main idea is to inscribe privacy protection into the knowledge discovery technology by design, so that the analysis incorporates the relevant privacy requirements from the start.},
	doi = {10.1140/epjds/s13688-014-0010-4},
	author = {Anna Monreale and S Rinzivillo and Francesca Pratesi and Fosca Giannotti and Dino Pedreschi}
}
@conference {725,
	title = {The purpose of motion: Learning activities from Individual Mobility Networks},
	booktitle = {International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014},
	year = {2014},
	doi = {10.1109/DSAA.2014.7058090},
	url = {http://dx.doi.org/10.1109/DSAA.2014.7058090},
	author = {S Rinzivillo and Lorenzo Gabrielli and Mirco Nanni and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti}
}
@article {828,
	title = {The retail market as a complex system},
	journal = {EPJ Data Science},
	volume = {3},
	number = {1},
	year = {2014},
	pages = {1{\textendash}27},
	abstract = {Aim of this paper is to introduce the complex system perspective into retail market analysis. Currently, to understand the retail market means to search for local patterns at the micro level, involving the segmentation, separation and profiling of diverse groups of consumers. In other contexts, however, markets are modelled as complex systems. Such strategy is able to uncover emerging regularities and patterns that make markets more predictable, e.g. enabling to predict how much a country{\textquoteright}s GDP will grow. Rather than isolate actors in homogeneous groups, this strategy requires to consider the system as a whole, as the emerging pattern can be detected only as a result of the interaction between its self-organizing parts. This assumption holds also in the retail market: each customer can be seen as an independent unit maximizing its own utility function. As a consequence, the global behaviour of the retail market naturally emerges, enabling a novel description of its properties, complementary to the local pattern approach. Such task demands for a data-driven empirical framework. In this paper, we analyse a unique transaction database, recording the micro-purchases of a million customers observed for several years in the stores of a national supermarket chain. We show the emergence of the fundamental pattern of this complex system, connecting the products{\textquoteright} volumes of sales with the customers{\textquoteright} volumes of purchases. This pattern has a number of applications. We provide three of them. By enabling us to evaluate the sophistication of needs that a customer has and a product satisfies, this pattern has been applied to the task of uncovering the hierarchy of needs of the customers, providing a hint about what is the next product a customer could be interested in buying and predicting in which shop she is likely to go to buy it.},
	doi = {10.1140/epjds/s13688-014-0033-x},
	url = {http://link.springer.com/article/10.1140/epjds/s13688-014-0033-x},
	author = {Diego Pennacchioli and Michele Coscia and S Rinzivillo and Fosca Giannotti and Dino Pedreschi}
}
@inbook {636,
	title = {Retrieving Points of Interest from Human Systematic Movements},
	booktitle = {Software Engineering and Formal Methods},
	year = {2014},
	pages = {294{\textendash}308},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {Human mobility analysis is emerging as a more and more fundamental task to deeply understand human behavior. In the last decade these kind of studies have become feasible thanks to the massive increase in availability of mobility data. A crucial point, for many mobility applications and analysis, is to extract interesting locations for people. In this paper, we propose a novel methodology to retrieve efficiently significant places of interest from movement data. Using car drivers{\textquoteright} systematic movements we mine everyday interesting locations, that is, places around which people life gravitates. The outcomes show the empirical evidence that these places capture nearly the whole mobility even though generated only from systematic movements abstractions.},
	doi = {10.1007/978-3-319-15201-1_19},
	author = {Riccardo Guidotti and Anna Monreale and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@conference {MokMasd2014,
	title = {Use of mobile phone data to estimate visitors mobility flows},
	booktitle = {Proceedings of MoKMaSD},
	year = {2014},
	abstract = {Big Data originating from the digital breadcrumbs of human activities,
sensed as by-product of the technologies that we use for our daily activities, allows
us to observe the individual and collective behavior of people at an unprecedented
detail. Many dimensions of our social life have big data {\textquotedblleft}proxies{\textquotedblright}, such as the mo-
bile calls data for mobility. In this paper we investigate to what extent data coming
from mobile operators could be a support in producing reliable and timely estimates
of intra-city mobility flows. The idea is to define an estimation method based on
calling data to characterize the mobility habits of visitors at the level of a single
municipality},
	url = {http://www.di.unipi.it/mokmasd/symposium-2014/preproceedings/GabrielliEtAl-mokmasd2014.pdf},
	author = {Lorenzo Gabrielli and Barbara Furletti and Fosca Giannotti and Mirco Nanni and S Rinzivillo}
}
@proceedings {528,
	title = {Analysis of GSM Calls Data for Understanding User Mobility Behavior},
	year = {2013},
	address = {Santa Clara, California},
	author = {Barbara Furletti and Lorenzo Gabrielli and Chiara Renso and S Rinzivillo}
}
@conference {731,
	title = {Comparing General Mobility and Mobility by Car},
	booktitle = {Computational Intelligence and 11th Brazilian Congress on Computational Intelligence (BRICS-CCI CBIC), 2013 BRICS Congress on},
	year = {2013},
	month = {Sept},
	doi = {10.1109/BRICS-CCI-CBIC.2013.116},
	author = {Luca Pappalardo and Filippo Simini and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@proceedings {529,
	title = {Explaining the PRoduct Range Effect in Purchase Data},
	year = {2013},
	author = {Diego Pennacchioli and Michele Coscia and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@conference {535,
	title = {Pisa Tourism fluxes Observatory: deriving mobility indicators from GSM call habits},
	booktitle = {NetMob Conference 2013},
	year = {2013},
	author = {Barbara Furletti and Lorenzo Gabrielli and Chiara Renso and S Rinzivillo}
}
@conference {615,
	title = {Privacy-Aware Distributed Mobility Data Analytics},
	booktitle = {SEBD},
	year = {2013},
	address = {Roccella Jonica},
	abstract = {We propose an approach to preserve privacy in an analytical processing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by individual vehicles and shipped to a central server. Movement data are sensitive because they may describe typical movement behaviors and therefore be used for re-identification of individuals in a database. We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential privacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the effectiveness of our approach also in terms of data utility preserved by the data transformation.
},
	author = {Francesca Pratesi and Anna Monreale and Hui Wendy Wang and S Rinzivillo and Dino Pedreschi and Gennady Andrienko and Natalia Andrienko}
}
@inbook {571,
	title = {Privacy-Preserving Distributed Movement Data Aggregation},
	booktitle = {Geographic Information Science at the Heart of Europe},
	series = {Lecture Notes in Geoinformation and Cartography},
	year = {2013},
	pages = {225-245},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {We propose a novel approach to privacy-preserving analytical processing within a distributed setting, and tackle the problem of obtaining aggregated information about vehicle traffic in a city from movement data collected by individual vehicles and shipped to a central server. Movement data are sensitive because people{\textquoteright}s whereabouts have the potential to reveal intimate personal traits, such as religious or sexual preferences, and may allow re-identification of individuals in a database. We provide a privacy-preserving framework for movement data aggregation based on trajectory generalization in a distributed environment. The proposed solution, based on the differential privacy model and on sketching techniques for efficient data compression, provides a formal data protection safeguard. Using real-life data, we demonstrate the effectiveness of our approach also in terms of data utility preserved by the data transformation.},
	isbn = {978-3-319-00614-7},
	doi = {10.1007/978-3-319-00615-4_13},
	url = {http://dx.doi.org/10.1007/978-3-319-00615-4_13},
	author = {Anna Monreale and Hui Wendy Wang and Francesca Pratesi and S Rinzivillo and Dino Pedreschi and Gennady Andrienko and Natalia Andrienko},
	editor = {Vandenbroucke, Danny and Bucher, B{\'e}n{\'e}dicte and Crompvoets, Joep}
}
@article {526,
	title = {Scalable Analysis of Movement Data for Extracting and Exploring Significant Places},
	journal = {IEEE Transactions on Visualization and Computer Graphics},
	volume = {19},
	number = {7},
	year = {2013},
	chapter = {49},
	author = {Gennady Andrienko and Natalia Andrienko and C. Hunter and S Rinzivillo and Stefan Wrobel}
}
@article {527,
	title = {Spatial and Temporal Evaluation of Network-based Analysis of Human Mobility},
	journal = {Social Network Analysis and Mining},
	volume = {to appear},
	year = {2013},
	author = {Michele Coscia and S Rinzivillo and Fosca Giannotti and Dino Pedreschi}
}
@article {732,
	title = {{Understanding the patterns of car travel}},
	journal = {The European Physical Journal Special Topics},
	volume = {215},
	number = {1},
	year = {2013},
	pages = {61{\textendash}73},
	abstract = {{Are the patterns of car travel different from those of general human mobility? Based on a unique dataset consisting of the GPS trajectories of 10 million travels accomplished by 150,000 cars in Italy, we investigate how known mobility models apply to car travels, and illustrate novel analytical findings. We also assess to what extent the sample in our dataset is representative of the overall car mobility, and discover how to build an extremely accurate model that, given our GPS data, estimates the real traffic values as measured by road sensors.}},
	doi = {10.1140/epjst\%252fe2013-01715-5},
	url = {http://dx.doi.org/10.1140/epjst\%252fe2013-01715-5},
	author = {Luca Pappalardo and S Rinzivillo and Qu, Zehui and Dino Pedreschi and Fosca Giannotti}
}
@conference {daytag2013,
	title = {Where Have You Been Today? Annotating Trajectories with DayTag},
	booktitle = {International Conference on Spatial and Spatio-temporal Databases (SSTD)},
	year = {2013},
	pages = {467-471},
	doi = {http://dx.doi.org/10.1007/978-3-642-40235-7_30},
	author = {S Rinzivillo and Fernando de Lucca Siqueira and Lorenzo Gabrielli and Chiara Renso and Vania Bogorny}
}
@article {488,
	title = {Analisi di Mobilita{\textquoteright} con dati eterogenei},
	year = {2012},
	institution = {ISTI - CNR},
	address = {Pisa},
	author = {Barbara Furletti and Roberto Trasarti and Lorenzo Gabrielli and S Rinzivillo and Luca Pappalardo and Fosca Giannotti}
}
@article {462,
	title = {Data Science for Simulating the Era of Electric Vehicles},
	journal = {KI - K{\"u}nstliche Intelligenz},
	year = {2012},
	doi = {10.1007/s13218-012-0183-6},
	author = {Davy Janssens and Fosca Giannotti and Mirco Nanni and Dino Pedreschi and S Rinzivillo}
}
@article {455,
	title = {Discovering the Geographical Borders of Human Mobility},
	journal = {KI - K{\"u}nstliche Intelligenz},
	year = {2012},
	chapter = {1},
	abstract = {The availability of massive network and mobility data from diverse domains has fostered the analysis of human behavior and interactions. Broad, extensive, and multidisciplinary research has been devoted to the extraction of non-trivial knowledge from this novel form of data. We propose a general method to determine the influence of social and mobility behavior over a specific geographical area in order to evaluate to what extent the current administrative borders represent the real basin of human movement. We build a network representation of human movement starting with vehicle GPS tracks and extract relevant clusters, which are then mapped back onto the territory, finding a good match with the existing administrative borders. The novelty of our approach is the focus on a detailed spatial resolution, we map emerging borders in terms of individual municipalities, rather than macro regional or national areas. We present a series of experiments to illustrate and evaluate the effectiveness of our approach.},
	issn = {0933-1875},
	doi = {10.1007/s13218-012-0181-8},
	url = {https://link.springer.com/article/10.1007\%2Fs13218-012-0181-8},
	author = {S Rinzivillo and Simone Mainardi and Fabio Pezzoni and Michele Coscia and Fosca Giannotti and Dino Pedreschi}
}
@conference {487,
	title = {Identifying users profiles from mobile calls habits},
	booktitle = {ACM SIGKDD International Workshop on Urban Computing},
	year = {2012},
	publisher = {ACM New York, NY, USA {\textcopyright}2012},
	organization = {ACM New York, NY, USA {\textcopyright}2012},
	address = {Beijing, China},
	abstract = {The huge quantity of positioning data registered by our mobile phones stimulates several research questions, mainly originating from the combination of this huge quantity of data with the extreme heterogeneity of the tracked user and the low granularity of the data. We propose a methodology to partition the users tracked by GSM phone calls into profiles like resident, commuters, in transit and tourists. The methodology analyses the phone calls with a combination of top-down and bottom up techniques where the top-down phase is based on a sequence of queries that identify some behaviors. The bottom-up is a machine learning phase to find groups of similar call behavior, thus refining the previous step. The integration of the two steps results in the partitioning of mobile traces into these four user categories that can be deeper analyzed, for example to understand the tourist movements in city or the traffic effects of commuters. An experiment on the identification of user profiles on a real dataset collecting call records from one month in the city of Pisa illustrates the methodology.},
	isbn = {978-1-4503-1542-5},
	doi = {10.1145/2346496.2346500},
	url = {http://delivery.acm.org/10.1145/2350000/2346500/p17-furletti.pdf?ip=146.48.83.121\&acc=ACTIVE\%20SERVICE\&CFID=166768290\&CFTOKEN=58719386\&__acm__=1357648050_e23771c2f6bd8feb96bd66b39294175d},
	author = {Barbara Furletti and Lorenzo Gabrielli and Chiara Renso and S Rinzivillo}
}
@proceedings {461,
	title = {Optimal Spatial Resolution for the Analysis of Human Mobility},
	year = {2012},
	address = {Instanbul, Turkey},
	author = {Michele Coscia and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
@proceedings {358,
	title = {From Movement Tracks through Events to Places: Extracting and Characterizing Significant Places from Mobility Data},
	year = {2011},
	author = {Gennady Andrienko and Natalia Andrienko and Cristophe Hurter and S Rinzivillo and Stefan Wrobel}
}
@conference {OngPTNRRG11,
	title = {Traffic Jams Detection Using Flock Mining},
	booktitle = {ECML/PKDD (3)},
	year = {2011},
	pages = {650-653},
	author = {Rebecca Ong and Fabio Pinelli and Roberto Trasarti and Mirco Nanni and Chiara Renso and S Rinzivillo and Fosca Giannotti}
}
@article {vlbdjMatlas,
	title = {Unveiling the complexity of human mobility by querying and mining massive trajectory data},
	journal = {VLDB J.},
	volume = {20},
	number = {5},
	year = {2011},
	pages = {695-719},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@conference {RinzivilloR11,
	title = {Who/Where Are My New Customers?},
	booktitle = {ISMIS Industrial Session},
	year = {2011},
	pages = {307-317},
	author = {S Rinzivillo and Salvatore Ruggieri}
}
@conference {TrasartiRPNM10,
	title = {Exploring Real Mobility Data with M-Atlas},
	booktitle = {ECML/PKDD (3)},
	year = {2010},
	pages = {624-627},
	abstract = {Research on moving-object data analysis has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing location aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks. These have made available massive repositories of spatio-temporal data recording human mobile activities, that call for suitable analytical methods, capable of enabling the development of innovative, location-aware applications.},
	doi = {10.1007/978-3-642-15939-8_48},
	author = {Roberto Trasarti and S Rinzivillo and Fabio Pinelli and Mirco Nanni and Anna Monreale and Chiara Renso and Dino Pedreschi and Fosca Giannotti}
}
@proceedings {337,
	title = {A Generalisation-based Approach to Anonymising Movement Data},
	year = {2010},
	abstract = {The possibility to collect, store, disseminate, and analyze data about movements of people raises
very serious privacy concerns, given the sensitivity of the information about personal positions. In
particular, sensitive information about individuals can be uncovered with the use of data mining and
visual analytics methods. In this paper we present a method for the generalization of trajectory data
that can be adopted as the first step of a process to obtain k-anonymity in spatio-temporal datasets.
We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this
method of generalization of trajectories preserves the clustering analysis results. },
	issn = {978-989-20-1953-6},
	url = {http://agile2010.dsi.uminho.pt/pen/ShortPapers_PDF\%5C122_DOC.pdf},
	author = {Gennady Andrienko and Natalia Andrienko and Fosca Giannotti and Anna Monreale and Dino Pedreschi and S Rinzivillo}
}
@conference {GiannottiNPPR10,
	title = {Mobility data mining: discovering movement patterns from trajectory data},
	booktitle = {Computational Transportation Science},
	year = {2010},
	pages = {7-10},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@article {572,
	title = {Movement Data Anonymity through Generalization},
	journal = {Transactions on Data Privacy},
	volume = {3},
	number = {2},
	year = {2010},
	pages = {91{\textendash}121},
	abstract = {Wireless networks and mobile devices, such as mobile phones and GPS receivers, sense
and track the movements of people and vehicles, producing society-wide mobility databases. This is
a challenging scenario for data analysis and mining. On the one hand, exciting opportunities arise out
of discovering new knowledge about human mobile behavior, and thus fuel intelligent info-mobility
applications. On other hand, new privacy concerns arise when mobility data are published. The
risk is particularly high for GPS trajectories, which represent movement of a very high precision and
spatio-temporal resolution: the de-identification of such trajectories (i.e., forgetting the ID of their
associated owners) is only a weak protection, as generally it is possible to re-identify a person by observing
her routine movements. In this paper we propose a method for achieving true anonymity in
a dataset of published trajectories, by defining a transformation of the original GPS trajectories based
on spatial generalization and k-anonymity. The proposed method offers a formal data protection
safeguard, quantified as a theoretical upper bound to the probability of re-identification. We conduct
a thorough study on a real-life GPS trajectory dataset, and provide strong empirical evidence that
the proposed anonymity techniques achieve the conflicting goals of data utility and data privacy. In
practice, the achieved anonymity protection is much stronger than the theoretical worst case, while
the quality of the cluster analysis on the trajectory data is preserved.},
	url = {http://www.tdp.cat/issues/abs.a045a10.php},
	author = {Anna Monreale and Gennady Andrienko and Natalia Andrienko and Fosca Giannotti and Dino Pedreschi and S Rinzivillo and Stefan Wrobel}
}
@inbook {KisilevichMNR10,
	title = {Spatio-temporal clustering},
	booktitle = {Data Mining and Knowledge Discovery Handbook},
	year = {2010},
	pages = {855-874},
	author = {Slava Kisilevich and Florian Mansmann and Mirco Nanni and S Rinzivillo}
}
@conference {fet2009,
	title = {GeoPKDD {\textendash} Geographic Privacy-aware Knowledge Discovery},
	booktitle = {The European Future Technologies Conference (FET 2009)},
	year = {2009},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@conference {AndrienkoARNP09,
	title = {A Visual Analytics Toolkit for Cluster-Based Classification of Mobility Data},
	booktitle = {SSTD},
	year = {2009},
	pages = {432-435},
	author = {Gennady Andrienko and Natalia Andrienko and S Rinzivillo and Mirco Nanni and Dino Pedreschi}
}
@conference {ClusterVAST,
	title = {Visual Cluster Analysis of Large Collections of Trajectories},
	booktitle = {IEEE Visual Analytics Science and Tecnology (VAST 2009)},
	year = {2009},
	publisher = {IEEE Computer Society Press},
	organization = {IEEE Computer Society Press},
	author = {Gennady Andrienko and Natalia Andrienko and S Rinzivillo and Mirco Nanni and Dino Pedreschi and Fosca Giannotti}
}
@inbook {RTBKKM08,
	title = {Knowledge Discovery from Geographical Data},
	booktitle = {Mobility, Data Mining and Privacy},
	year = {2008},
	pages = {243-265},
	author = {S Rinzivillo and Franco Turini and Vania Bogorny and Christine K{\"o}rner and Bart Kuijpers and Michael May}
}
@article {IV2008,
	title = {Visually driven analysis of movement data by progressive clustering},
	journal = {Information Visualization},
	volume = {7},
	number = {3-4},
	year = {2008},
	pages = {225-239},
	publisher = {Palgrave Macmillan Ltd},
	author = {S Rinzivillo and Dino Pedreschi and Mirco Nanni and Fosca Giannotti and Natalia Andrienko and Gennady Andrienko}
}
@article {RT07,
	title = {Knowledge discovery from spatial transactions},
	journal = {Journal of Intelligent Information Systems},
	volume = {28},
	number = {1},
	year = {2007},
	pages = {1-22},
	author = {S Rinzivillo and Franco Turini}
}
@conference {TuriniBFR06,
	title = {Examples of Integration of Induction and Deduction in Knowledge Discovery},
	booktitle = {Reasoning, Action and Interaction in AI Theories and Systems},
	year = {2006},
	pages = {307-326},
	author = {Franco Turini and Miriam Baglioni and Barbara Furletti and S Rinzivillo}
}
@inbook {466,
	title = {Examples of Integration of Induction and Deduction in Knowledge Discovery},
	booktitle = {Reasoning, Action and Interaction in AI Theories and Systems},
	series = {LNAI},
	volume = {4155},
	year = {2006},
	pages = {307-326},
	doi = {10.1007/11829263_17},
	url = {http://www.springerlink.com/content/m400v4507476n18g/fulltext.pdf},
	author = {Franco Turini and Miriam Baglioni and Barbara Furletti and S Rinzivillo}
}
@conference {RinzivilloT05,
	title = {Extracting spatial association rules from spatial transactions},
	booktitle = {ACM GIS},
	year = {2005},
	pages = {79-86},
	author = {S Rinzivillo and Franco Turini}
}
@conference {RinzivilloT04,
	title = {Classification in Geographical Information Systems},
	booktitle = {PKDD},
	year = {2004},
	pages = {374-385},
	author = {S Rinzivillo and Franco Turini}
}
@conference {GargantiniRR03,
	title = {Using Spin to Generate Tests from ASM Specifications},
	booktitle = {Abstract State Machines},
	year = {2003},
	pages = {263-277},
	author = {Angelo Gargantini and Elvinia Riccobene and S Rinzivillo}
}
