%0 Journal Article %D 2021 %T Give more data, awareness and control to individual citizens, and they will help COVID-19 containment %A Mirco Nanni %A Andrienko, Gennady %A Barabasi, Albert-Laszlo %A Boldrini, Chiara %A Bonchi, Francesco %A Cattuto, Ciro %A Chiaromonte, Francesca %A Comandé, Giovanni %A Conti, Marco %A Coté, Mark %A Dignum, Frank %A Dignum, Virginia %A Domingo-Ferrer, Josep %A Ferragina, Paolo %A Fosca Giannotti %A Riccardo Guidotti %A Helbing, Dirk %A Kaski, Kimmo %A Kertész, János %A Lehmann, Sune %A Lepri, Bruno %A Lukowicz, Paul %A Matwin, Stan %A Jiménez, David Megías %A Anna Monreale %A Morik, Katharina %A Oliver, Nuria %A Passarella, Andrea %A Passerini, Andrea %A Dino Pedreschi %A Pentland, Alex %A Pianesi, Fabio %A Francesca Pratesi %A S Rinzivillo %A Salvatore Ruggieri %A Siebes, Arno %A Torra, Vicenc %A Roberto Trasarti %A Hoven, Jeroen van den %A Vespignani, Alessandro %X The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society. %8 2021/02/02 %@ 1572-8439 %G eng %U https://link.springer.com/article/10.1007/s10676-020-09572-w %! Ethics and Information Technology %R https://doi.org/10.1007/s10676-020-09572-w %0 Journal Article %J ERCIM News %D 2019 %T Transparency in Algorithmic Decision Making %A Andreas Rauber %A Roberto Trasarti %A Fosca Giannotti %B ERCIM News %G eng %U https://ercim-news.ercim.eu/en116/special/transparency-in-algorithmic-decision-making-introduction-to-the-special-theme %0 Journal Article %J Transactions on Data Privacy %D 2018 %T PRUDEnce: a system for assessing privacy risk vs utility in data sharing ecosystems %A Francesca Pratesi %A Anna Monreale %A Roberto Trasarti %A Fosca Giannotti %A Dino Pedreschi %A Yanagihara, Tadashi %X Data describing human activities are an important source of knowledge useful for understanding individual and collective behavior and for developing a wide range of user services. Unfortunately, this kind of data is sensitive, because people’s whereabouts may allow re-identification of individuals in a de-identified database. Therefore, Data Providers, before sharing those data, must apply any sort of anonymization to lower the privacy risks, but they must be aware and capable of controlling also the data quality, since these two factors are often a trade-off. In this paper we propose PRUDEnce (Privacy Risk versus Utility in Data sharing Ecosystems), a system enabling a privacy-aware ecosystem for sharing personal data. It is based on a methodology for assessing both the empirical (not theoretical) privacy risk associated to users represented in the data, and the data quality guaranteed only with users not at risk. Our proposal is able to support the Data Provider in the exploration of a repertoire of possible data transformations with the aim of selecting one specific transformation that yields an adequate trade-off between data quality and privacy risk. We study the practical effectiveness of our proposal over three data formats underlying many services, defined on real mobility data, i.e., presence data, trajectory data and road segment data. %B Transactions on Data Privacy %V 11 %8 08/2018 %G eng %U http://www.tdp.cat/issues16/tdp.a284a17.pdf %0 Conference Paper %B Companion of the The Web Conference 2018 on The Web Conference 2018 %D 2018 %T SoBigData: Social Mining & Big Data Ecosystem %A Fosca Giannotti %A Roberto Trasarti %A Bontcheva, Kalina %A Valerio Grossi %X One of the most pressing and fascinating challenges scientists face today, is understanding the complexity of our globally interconnected society. The big data arising from the digital breadcrumbs of human activities has the potential of providing a powerful social microscope, which can help us understand many complex and hidden socio-economic phenomena. Such challenge requires high-level analytics, modeling and reasoning across all the social dimensions above. There is a need to harness these opportunities for scientific advancement and for the social good, compared to the currently prevalent exploitation of big data for commercial purposes or, worse, social control and surveillance. The main obstacle to this accomplishment, besides the scarcity of data scientists, is the lack of a large-scale open ecosystem where big data and social mining research can be carried out. The SoBigData Research Infrastructure (RI) provides an integrated ecosystem for ethic-sensitive scientific discoveries and advanced applications of social data mining on the various dimensions of social life as recorded by "big data". The research community uses the SoBigData facilities as a "secure digital wind-tunnel" for large-scale social data analysis and simulation experiments. SoBigData promotes repeatable and open science and supports data science research projects by providing: i) an ever-growing, distributed data ecosystem for procurement, access and curation and management of big social data, to underpin social data mining research within an ethic-sensitive context; ii) an ever-growing, distributed platform of interoperable, social data mining methods and associated skills: tools, methodologies and services for mining, analysing, and visualising complex and massive datasets, harnessing the techno-legal barriers to the ethically safe deployment of big data for social mining; iii) an ecosystem where protection of personal information and the respect for fundamental human rights can coexist with a safe use of the same information for scientific purposes of broad and central societal interest. SoBigData has a dedicated ethical and legal board, which is implementing a legal and ethical framework. %B Companion of the The Web Conference 2018 on The Web Conference 2018 %I International World Wide Web Conferences Steering Committee %G eng %U http://www.sobigdata.eu/sites/default/files/www%202018.pdf %0 Journal Article %J Information %D 2017 %T Discovering and Understanding City Events with Big Data: The Case of Rome %A Barbara Furletti %A Roberto Trasarti %A Paolo Cintia %A Lorenzo Gabrielli %X The increasing availability of large amounts of data and digital footprints has given rise to ambitious research challenges in many fields, which spans from medical research, financial and commercial world, to people and environmental monitoring. Whereas traditional data sources and census fail in capturing actual and up-to-date behaviors, Big Data integrate the missing knowledge providing useful and hidden information to analysts and decision makers. With this paper, we focus on the identification of city events by analyzing mobile phone data (Call Detail Record), and we study and evaluate the impact of these events over the typical city dynamics. We present an analytical process able to discover, understand and characterize city events from Call Detail Record, designing a distributed computation to implement Sociometer, that is a profiling tool to categorize phone users. The methodology provides an useful tool for city mobility manager to manage the events and taking future decisions on specific classes of users, i.e., residents, commuters and tourists. %B Information %V 8 %P 74 %8 06/2017 %G eng %U https://doi.org/10.3390/info8030074 %R 10.3390/info8030074 %0 Journal Article %J D-Lib Magazine %D 2017 %T HyWare: a HYbrid Workflow lAnguage for Research E-infrastructures %A Leonardo Candela %A Paolo Manghi %A Fosca Giannotti %A Valerio Grossi %A Roberto Trasarti %X Research e-infrastructures are "systems of systems", patchworks of tools, services and data sources, evolving over time to address the needs of the scientific process. Accordingly, in such environments, researchers implement their scientific processes by means of workflows made of a variety of actions, including for example usage of web services, download and execution of shared software libraries or tools, or local and manual manipulation of data. Although scientists may benefit from sharing their scientific process, the heterogeneity underpinning e-infrastructures hinders their ability to represent, share and eventually reproduce such workflows. This work presents HyWare, a language for representing scientific process in highly-heterogeneous e-infrastructures in terms of so-called hybrid workflows. HyWare lays in between "business process modeling languages", which offer a formal and high-level description of a reasoning, protocol, or procedure, and "workflow execution languages", which enable the fully automated execution of a sequence of computational steps via dedicated engines. %B D-Lib Magazine %V 23 %G eng %U http://dx.doi.org/10.1045/january2017-candela %R 10.1045/january2017-candela %0 Conference Paper %B Personal Analytics and Privacy. An Individual and Collective Perspective - First International Workshop, {PAP} 2017, Held in Conjunction with {ECML} {PKDD} 2017, Skopje, Macedonia, September 18, 2017, Revised Selected Papers %D 2017 %T Movement Behaviour Recognition for Water Activities %A Mirco Nanni %A Roberto Trasarti %A Fosca Giannotti %B Personal Analytics and Privacy. An Individual and Collective Perspective - First International Workshop, {PAP} 2017, Held in Conjunction with {ECML} {PKDD} 2017, Skopje, Macedonia, September 18, 2017, Revised Selected Papers %G eng %U https://doi.org/10.1007/978-3-319-71970-2_7 %R 10.1007/978-3-319-71970-2_7 %0 Journal Article %J Information Systems %D 2017 %T MyWay: Location prediction via mobility profiling %A Roberto Trasarti %A Riccardo Guidotti %A Anna Monreale %A Fosca Giannotti %X Forecasting the future positions of mobile users is a valuable task allowing us to operate efficiently a myriad of different applications which need this type of information. We propose MyWay, a prediction system which exploits the individual systematic behaviors modeled by mobility profiles to predict human movements. MyWay provides three strategies: the individual strategy uses only the user individual mobility profile, the collective strategy takes advantage of all users individual systematic behaviors, and the hybrid strategy that is a combination of the previous two. A key point is that MyWay only requires the sharing of individual mobility profiles, a concise representation of the user׳s movements, instead of raw trajectory data revealing the detailed movement of the users. We evaluate the prediction performances of our proposal by a deep experimentation on large real-world data. The results highlight that the synergy between the individual and collective knowledge is the key for a better prediction and allow the system to outperform the state-of-art methods. %B Information Systems %V 64 %P 350–367 %8 03/2017 %G eng %0 Conference Paper %B 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017) %D 2017 %T There's A Path For Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas %A Riccardo Guidotti %A Roberto Trasarti %A Mirco Nanni %A Fosca Giannotti %A Dino Pedreschi %B 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017) %I IEEE %C Tokyo %G eng %0 Journal Article %J Journal ACM Transactions on Intelligent Systems and Technology (TIST) %D 2016 %T Driving Profiles Computation and Monitoring for Car Insurance CRM %A Mirco Nanni %A Roberto Trasarti %A Anna Monreale %A Valerio Grossi %A Dino Pedreschi %X Customer segmentation is one of the most traditional and valued tasks in customer relationship management (CRM). In this article, we explore the problem in the context of the car insurance industry, where the mobility behavior of customers plays a key role: Different mobility needs, driving habits, and skills imply also different requirements (level of coverage provided by the insurance) and risks (of accidents). In the present work, we describe a methodology to extract several indicators describing the driving profile of customers, and we provide a clustering-oriented instantiation of the segmentation problem based on such indicators. Then, we consider the availability of a continuous flow of fresh mobility data sent by the circulating vehicles, aiming at keeping our segments constantly up to date. We tackle a major scalability issue that emerges in this context when the number of customers is large-namely, the communication bottleneck-by proposing and implementing a sophisticated distributed monitoring solution that reduces communications between vehicles and company servers to the essential. We validate the framework on a large database of real mobility data coming from GPS devices on private cars. Finally, we analyze the privacy risks that the proposed approach might involve for the users, providing and evaluating a countermeasure based on data perturbation. %B Journal ACM Transactions on Intelligent Systems and Technology (TIST) %V 8 %P 14:1–14:26 %G eng %U http://doi.acm.org/10.1145/2912148 %R 10.1145/2912148 %0 Journal Article %J Computer Communications %D 2016 %T Special Issue on Mobile Traffic Analytics %A Marco Fiore %A Zubair Shafiq %A Zbigniew Smoreda %A Razvan Stanica %A Roberto Trasarti %X This Special Issue of Computer Communications is dedicated to mobile traffic data analysis. This is an emerging field of research that stems from the increasing pervasiveness in our lives of always-connected mobile devices. These devices continuously collect, generate, receive or communicate data; in doing so, they leave trails of digital crumbs that can be followed, recorded and analysed in many and varied ways, and for a number of different purposes. From a data collection perspective, applications running on smartphones allow tracking user activities with extreme accuracy, in terms of mobility, context, and service usage. Yet, having individuals informedly install and run software that monitors their actions is not obvious; finding adequate incentives is equivalently complex. The other option is gathering mobile traffic data in the mobile network. This is an increasingly common practice for telecommunication operators: the collection of minimum information required for billing is giving way to in-depth inspection and recording of mobile service usages in space and time, and of traffic flows at the network edge and core. In this case, data access remains the major impediment, due to privacy and industrial secrecy reasons. Despite the issues inherent to the data collection, the richness of knowledge that can be extracted from the aforementioned sources is such that actors in both academia and industry are putting significant effort in gathering, analysing and possibly making available mobile traffic data. Indeed, mobile traffic data typically contain information on large populations of individuals (from thousands to millions users) with high spatio-temporal granularity. The combination of accuracy and coverage is unprecedented, and it has proven key in validating theories and scaling up experimental studies in a number of research fields across many disciplines, including physics, sociology, epidemiology, transportation systems, and, of course, mobile networking. As a result, we witness today a rapid growth of the literature that proposes or exploits mobile traffic analytics. Included in this Special Issue are eight papers that cover a significant portion of the different research topics in this area, ranging from data collection to the characterization of land use and mobile service consumption, from the inference and prediction of user mobility to the detection of malicious traffic. These papers were selected from 30 high-quality submissions after at least two rounds of reviews by experts and guest editors. The original submissions were received from five continents and a variety of countries, including Austria, Argentina, Belgium, Brazil, Chile, China, France, Germany, Italy, South Korea, Luxembourg, Pakistan, Saudi Arabia, Spain, Sweden, Tunisia, Turkey, USA. The accepted papers reflect this geographical heterogeneity, and are authored by researchers based in Europe, North and South America. %B Computer Communications %V 95 %P 1–2 %G eng %U http://dx.doi.org/10.1016/j.comcom.2016.10.009 %R 10.1016/j.comcom.2016.10.009 %0 Conference Paper %B IEEE Big Data %D 2015 %T City users’ classification with mobile phone data %A Lorenzo Gabrielli %A Barbara Furletti %A Roberto Trasarti %A Fosca Giannotti %A Dino Pedreschi %X Nowadays mobile phone data are an actual proxy for studying the users’ social life and urban dynamics. In this paper we present the Sociometer, and analytical framework aimed at classifying mobile phone users into behavioral categories by means of their call habits. The analytical process starts from spatio-temporal profiles, learns the different behaviors, and returns annotated profiles. After the description of the methodology and its evaluation, we present an application of the Sociometer for studying city users of one small and one big city, evaluating the impact of big events in these cities. %B IEEE Big Data %C Santa Clara (CA) - USA %8 11/2015 %G eng %0 Conference Paper %B NetMob %D 2015 %T Detecting and understanding big events in big cities %A Barbara Furletti %A Lorenzo Gabrielli %A Roberto Trasarti %A Zbigniew Smoreda %A Maarten Vanhoof %A Cezary Ziemlicki %X Recent studies have shown the great potential of big data such as mobile phone location data to model human behavior. Big data allow to analyze people presence in a territory in a fast and effective way with respect to the classical surveys (diaries or questionnaires). One of the drawbacks of these collection systems is incompleteness of the users' traces; people are localized only when they are using their phones. In this work we define a data mining method for identifying people presence and understanding the impact of big events in big cities. We exploit the ability of the Sociometer for classifying mobile phone users in mobility categories through their presence profile. The experiment in cooperation with Orange Telecom has been conduced in Paris during the event F^ete de la Musique using a privacy preserving protocol. %B NetMob %C Boston %8 04/2015 %G eng %U http://www.netmob.org/assets/img/netmob15_book_of_abstracts_posters.pdf %0 Journal Article %J Journal of Trust Management %D 2015 %T A risk model for privacy in trajectory data %A Anirban Basu %A Anna Monreale %A Roberto Trasarti %A Juan Camilo Corena %A Fosca Giannotti %A Dino Pedreschi %A Shinsaku Kiyomoto %A Yutaka Miyake %A Tadashi Yanagihara %X Time sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacy-preserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users with a high degree of certainty. These methods provide theoretical worst-case privacy risks as measures of the privacy protection that they offer. However, often with many real-world data the worst-case scenario is too pessimistic and does not provide a realistic view of the privacy risks: the real probability of re-identification is often much lower than the theoretical worst-case risk. In this paper, we propose a novel empirical risk model for privacy which, in relation to the cost of privacy attacks, demonstrates better the practical risks associated with a privacy preserving data release. We show detailed evaluation of the proposed risk model by using k-anonymised real-world mobility data and then, we show how the empirical evaluation of the privacy risk has a different trend in synthetic data describing random movements. %B Journal of Trust Management %V 2 %P 9 %G eng %R 10.1186/s40493-015-0020-6 %0 Conference Paper %B Proceedings of the 23rd {SIGSPATIAL} International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA, November 3-6, 2015 %D 2015 %T {TOSCA:} two-steps clustering algorithm for personal locations detection %A Riccardo Guidotti %A Roberto Trasarti %A Mirco Nanni %B Proceedings of the 23rd {SIGSPATIAL} International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA, November 3-6, 2015 %G eng %U http://doi.acm.org/10.1145/2820783.2820818 %R 10.1145/2820783.2820818 %0 Conference Paper %B Proceedings of the 4th {ACM} {SIGSPATIAL} International Workshop on Mobile Geographic Information Systems, MobiGIS 2015, Bellevue, WA, USA, November 3-6, 2015 %D 2015 %T Towards user-centric data management: individual mobility analytics for collective services %A Riccardo Guidotti %A Roberto Trasarti %A Mirco Nanni %A Fosca Giannotti %B Proceedings of the 4th {ACM} {SIGSPATIAL} International Workshop on Mobile Geographic Information Systems, MobiGIS 2015, Bellevue, WA, USA, November 3-6, 2015 %G eng %U http://doi.acm.org/10.1145/2834126.2834132 %R 10.1145/2834126.2834132 %0 Conference Paper %B EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD) %D 2014 %T Big data analytics for smart mobility: a case study %A Barbara Furletti %A Roberto Trasarti %A Lorenzo Gabrielli %A Mirco Nanni %A Dino Pedreschi %B EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD) %C Athens, Greece %8 03/2014 %U http://ceur-ws.org/Vol-1133/paper-57.pdf %M ISSN - 1613-0073 %0 Journal Article %J Telecommunications Policy %D 2014 %T Discovering urban and country dynamics from mobile phone data with spatial correlation patterns %A Roberto Trasarti %A Ana-Maria Olteanu-Raimond %A Mirco Nanni %A Thomas Couronné %A Barbara Furletti %A Fosca Giannotti %A Zbigniew Smoreda %A Cezary Ziemlicki %K Urban dynamics %X Abstract Mobile communication technologies pervade our society and existing wireless networks are able to sense the movement of people, generating large volumes of data related to human activities, such as mobile phone call records. At the present, this kind of data is collected and stored by telecom operators infrastructures mainly for billing reasons, yet it represents a major source of information in the study of human mobility. In this paper, we propose an analytical process aimed at extracting interconnections between different areas of the city that emerge from highly correlated temporal variations of population local densities. To accomplish this objective, we propose a process based on two analytical tools: (i) a method to estimate the presence of people in different geographical areas; and (ii) a method to extract time- and space-constrained sequential patterns capable to capture correlations among geographical areas in terms of significant co-variations of the estimated presence. The methods are presented and combined in order to deal with two real scenarios of different spatial scale: the Paris Region and the whole France. %B Telecommunications Policy %P - %U http://www.sciencedirect.com/science/article/pii/S0308596113002012 %R http://dx.doi.org/10.1016/j.telpol.2013.12.002 %0 Book Section %B Data Science and Simulation in Transportation Research %D 2014 %T Mobility Profiling %A Mirco Nanni %A Roberto Trasarti %A Paolo Cintia %A Barbara Furletti %A Chiara Renso %A Lorenzo Gabrielli %A S Rinzivillo %A Fosca Giannotti %X 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. %B Data Science and Simulation in Transportation Research %I IGI Global %P 1-29 %& 1 %R 10.4018/978-1-4666-4920-0.ch001 %0 Conference Paper %B Trust Management {VIII} - 8th {IFIP} {WG} 11.11 International Conference, {IFIPTM} 2014, Singapore, July 7-10, 2014. Proceedings %D 2014 %T A Privacy Risk Model for Trajectory Data %A Anirban Basu %A Anna Monreale %A Juan Camilo Corena %A Fosca Giannotti %A Dino Pedreschi %A Shinsaku Kiyomoto %A Yutaka Miyake %A Tadashi Yanagihara %A Roberto Trasarti %X Time sequence data relating to users, such as medical histories and mobility data, are good candidates for data mining, but often contain highly sensitive information. Different methods in privacy-preserving data publishing are utilised to release such private data so that individual records in the released data cannot be re-linked to specific users with a high degree of certainty. These methods provide theoretical worst-case privacy risks as measures of the privacy protection that they offer. However, often with many real-world data the worst-case scenario is too pessimistic and does not provide a realistic view of the privacy risks: the real probability of re-identification is often much lower than the theoretical worst-case risk. In this paper we propose a novel empirical risk model for privacy which, in relation to the cost of privacy attacks, demonstrates better the practical risks associated with a privacy preserving data release. We show detailed evaluation of the proposed risk model by using k-anonymised real-world mobility data. %B Trust Management {VIII} - 8th {IFIP} {WG} 11.11 International Conference, {IFIPTM} 2014, Singapore, July 7-10, 2014. Proceedings %P 125–140 %U http://dx.doi.org/10.1007/978-3-662-43813-8_9 %R 10.1007/978-3-662-43813-8_9 %0 Conference Paper %B 2013 {IEEE} 14th International Conference on Mobile Data Management, Milan, Italy, June 3-6, 2013 - Volume 2 %D 2013 %T A Gravity Model for Speed Estimation over Road Network %A Paolo Cintia %A Roberto Trasarti %A José Antônio Fernandes de Macêdo %A Livia Almada %A Camila Fereira %B 2013 {IEEE} 14th International Conference on Mobile Data Management, Milan, Italy, June 3-6, 2013 - Volume 2 %G eng %U http://dx.doi.org/10.1109/MDM.2013.83 %R 10.1109/MDM.2013.83 %0 Journal Article %J Knowl. Inf. Syst. %D 2013 %T How you move reveals who you are: understanding human behavior by analyzing trajectory data %A Chiara Renso %A Miriam Baglioni %A José Antônio Fernandes de Macêdo %A Roberto Trasarti %A Monica Wachowicz %B Knowl. Inf. Syst. %V 37 %P 331–362 %G eng %U http://dx.doi.org/10.1007/s10115-012-0511-z %R 10.1007/s10115-012-0511-z %0 Conference Paper %B SecoGIS 2013 - International Workshop on Semantic Aspects of GIS, Joint to ER conference 2013 %D 2013 %T Mob-Warehouse: A semantic approach for mobility analysis with a Trajectory Data Ware- house %A Ricardo Wagner %A de José Antônio Fernandes Macêdo %A Alessandra Raffaetà %A Chiara Renso %A Alessandro Roncato %A Roberto Trasarti %B SecoGIS 2013 - International Workshop on Semantic Aspects of GIS, Joint to ER conference 2013 %C Hong Kong %0 Conference Paper %B In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013) %D 2013 %T MP4-A Project: Mobility Planning For Africa %A Mirco Nanni %A Roberto Trasarti %A Barbara Furletti %A Lorenzo Gabrielli %A Peter Van Der Mede %A Joost De Bruijn %A Erik de Romph %A Gerard Bruil %X This project aims to create a tool that uses mobile phone transaction (trajectory) data that will be able to address transportation related challenges, thus allowing promotion and facilitation of sustainable urban mobility planning in Third World countries. The proposed tool is a transport demand model for Ivory Coast, with emphasis on its major urbanization Abidjan. The consortium will bring together available data from the internet, and integrate these with the mobility data obtained from the mobile phones in order to build the best possible transport model. A transport model allows an understanding of current and future infrastructure requirements in Ivory Coast. As such, this project will provide the first proof of concept. In this context, long-term analysis of individual call traces will be performed to reconstruct systematic movements, and to infer an origin-destination matrix. A similar process will be performed using the locations of caller and recipient of phone calls, enabling the comparison of socio-economic ties vs. mobility. The emerging links between different areas will be used to build an effective map to optimize regional border definitions and road infrastructure from a mobility perspective. Finally, we will try to build specialized origin-destination matrices for specific categories of population. Such categories will be inferred from data through analysis of calling behaviours, and will also be used to characterize the population of different cities. The project also includes a study of data compliance with distributions of standard measures observed in literature, including distribution of calls, call durations and call network features. %B In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013) %C Cambridge, USA %G eng %U http://perso.uclouvain.be/vincent.blondel/netmob/2013/D4D-book.pdf %0 Journal Article %J {SIGMOD} Record %D 2013 %T Towards mega-modeling: a walk through data analysis experiences %A Stefano Ceri %A Themis Palpanas %A Emanuele Della Valle %A Dino Pedreschi %A Johann-Christoph Freytag %A Roberto Trasarti %B {SIGMOD} Record %V 42 %P 19–27 %G eng %U http://doi.acm.org/10.1145/2536669.2536673 %R 10.1145/2536669.2536673 %0 Conference Paper %B Citizen in Sensor Networks - Second International Workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, Revised Selected Papers %D 2013 %T Transportation Planning Based on {GSM} Traces: {A} Case Study on Ivory Coast %A Mirco Nanni %A Roberto Trasarti %A Barbara Furletti %A Lorenzo Gabrielli %A Peter Van Der Mede %A Joost De Bruijn %A Erik de Romph %A Gerard Bruil %B Citizen in Sensor Networks - Second International Workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, Revised Selected Papers %G eng %U http://dx.doi.org/10.1007/978-3-319-04178-0_2 %R 10.1007/978-3-319-04178-0_2 %0 Conference Paper %B Proceedings of the 3rd International Conference on Ambient Systems, Networks and Technologies {(ANT} 2012), the 9th International Conference on Mobile Web Information Systems (MobiWIS-2012), Niagara Falls, Ontario, Canada, August 27-29, 2012 %D 2012 %T An Agent-Based Model to Evaluate Carpooling at Large Manufacturing Plants %A Tom Bellemans %A Sebastian Bothe %A Sungjin Cho %A Fosca Giannotti %A Davy Janssens %A Luk Knapen %A Christine Körner %A Michael May %A Mirco Nanni %A Dino Pedreschi %A Hendrik Stange %A Roberto Trasarti %A Ansar-Ul-Haque Yasar %A Geert Wets %B Proceedings of the 3rd International Conference on Ambient Systems, Networks and Technologies {(ANT} 2012), the 9th International Conference on Mobile Web Information Systems (MobiWIS-2012), Niagara Falls, Ontario, Canada, August 27-29, 2012 %G eng %U http://dx.doi.org/10.1016/j.procs.2012.08.001 %R 10.1016/j.procs.2012.08.001 %0 Report %D 2012 %T Analisi di Mobilita' con dati eterogenei %A Barbara Furletti %A Roberto Trasarti %A Lorenzo Gabrielli %A S Rinzivillo %A Luca Pappalardo %A Fosca Giannotti %I ISTI - CNR %C Pisa %0 Conference Proceedings %B MDM 2012 %D 2012 %T ComeTogether: Discovering Communities of Places in Mobility Data %A Igo Brilhante %A Michele Berlingerio %A Roberto Trasarti %A Chiara Renso %A de José Antônio Fernandes Macêdo %A Marco A. Casanova %B MDM 2012 %P 268-273 %8 2012 %0 Conference Paper %B Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings %D 2012 %T Individual Mobility Profiles: Methods and Application on Vehicle Sharing %A Roberto Trasarti %A Fabio Pinelli %A Mirco Nanni %A Fosca Giannotti %B Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings %G eng %U http://sebd2012.dei.unipd.it/documents/188475/32d00b8a-8ead-4d97-923f-bd2f2cf6ddcb %0 Conference Paper %B Conceptual Modeling - 31st International Conference {ER} 2012, Florence, Italy, October 15-18, 2012. Proceedings %D 2012 %T Mega-modeling for Big Data Analytics %A Stefano Ceri %A Emanuele Della Valle %A Dino Pedreschi %A Roberto Trasarti %B Conceptual Modeling - 31st International Conference {ER} 2012, Florence, Italy, October 15-18, 2012. Proceedings %G eng %U http://dx.doi.org/10.1007/978-3-642-34002-4_1 %R 10.1007/978-3-642-34002-4_1 %0 Journal Article %J Transactions on Data Privacy %D 2011 %T C-safety: a framework for the anonymization of semantic trajectories %A Anna Monreale %A Roberto Trasarti %A Dino Pedreschi %A Chiara Renso %A Vania Bogorny %X The increasing abundance of data about the trajectories of personal movement is opening new opportunities for analyzing and mining human mobility. However, new risks emerge since it opens new ways of intruding into personal privacy. Representing the personal movements as sequences of places visited by a person during her/his movements - semantic trajectory - poses great privacy threats. In this paper we propose a privacy model defining the attack model of semantic trajectory linking and a privacy notion, called c-safety based on a generalization of visited places based on a taxonomy. This method provides an upper bound to the probability of inferring that a given person, observed in a sequence of non-sensitive places, has also visited any sensitive location. Coherently with the privacy model, we propose an algorithm for transforming any dataset of semantic trajectories into a c-safe one. We report a study on two real-life GPS trajectory datasets to show how our algorithm preserves interesting quality/utility measures of the original trajectories, when mining semantic trajectories sequential pattern mining results. We also empirically measure how the probability that the attacker’s inference succeeds is much lower than the theoretical upper bound established. %B Transactions on Data Privacy %V 4 %P 73-101 %U http://dl.acm.org/citation.cfm?id=2019319&CFID=803961971&CFTOKEN=35994039 %0 Conference Paper %B KDD %D 2011 %T Mining mobility user profiles for car pooling %A Roberto Trasarti %A Fabio Pinelli %A Mirco Nanni %A Fosca Giannotti %B KDD %P 1190-1198 %0 Journal Article %J IJDWM %D 2011 %T A Query Language for Mobility Data Mining %A Roberto Trasarti %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A Chiara Renso %B IJDWM %V 7 %P 24-45 %0 Conference Paper %B ECML/PKDD (3) %D 2011 %T Traffic Jams Detection Using Flock Mining %A Rebecca Ong %A Fabio Pinelli %A Roberto Trasarti %A Mirco Nanni %A Chiara Renso %A S Rinzivillo %A Fosca Giannotti %B ECML/PKDD (3) %P 650-653 %0 Journal Article %J VLDB J. %D 2011 %T Unveiling the complexity of human mobility by querying and mining massive trajectory data %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A Fabio Pinelli %A Chiara Renso %A S Rinzivillo %A Roberto Trasarti %B VLDB J. %V 20 %P 695-719 %0 Conference Paper %B EDBT %D 2010 %T Advanced knowledge discovery on movement data with the GeoPKDD system %A Mirco Nanni %A Roberto Trasarti %A Chiara Renso %A Fosca Giannotti %A Dino Pedreschi %B EDBT %P 693-696 %0 Conference Paper %B EDBT %D 2010 %T Advanced knowledge discovery on movement data with the GeoPKDD system %A Mirco Nanni %A Roberto Trasarti %A Chiara Renso %A Fosca Giannotti %A Dino Pedreschi %B EDBT %P 693-696 %0 Conference Paper %B ECML/PKDD (3) %D 2010 %T Exploring Real Mobility Data with M-Atlas %A Roberto Trasarti %A S Rinzivillo %A Fabio Pinelli %A Mirco Nanni %A Anna Monreale %A Chiara Renso %A Dino Pedreschi %A Fosca Giannotti %X 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. %B ECML/PKDD (3) %P 624-627 %R 10.1007/978-3-642-15939-8_48 %0 Conference Paper %B SEBD %D 2010 %T Location Prediction through Trajectory Pattern Mining (Extended Abstract) %A Anna Monreale %A Fabio Pinelli %A Roberto Trasarti %A Fosca Giannotti %B SEBD %P 134-141 %0 Conference Paper %B Computational Transportation Science %D 2010 %T Mobility data mining: discovering movement patterns from trajectory data %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A Fabio Pinelli %A Chiara Renso %A S Rinzivillo %A Roberto Trasarti %B Computational Transportation Science %P 7-10 %0 Conference Paper %B SPRINGL %D 2010 %T Preserving privacy in semantic-rich trajectories of human mobility %A Anna Monreale %A Roberto Trasarti %A Chiara Renso %A Dino Pedreschi %A Vania Bogorny %X The increasing abundance of data about the trajectories of personal movement is opening up new opportunities for analyzing and mining human mobility, but new risks emerge since it opens new ways of intruding into personal privacy. Representing the personal movements as sequences of places visited by a person during her/his movements - semantic trajectory - poses even greater privacy threats w.r.t. raw geometric location data. In this paper we propose a privacy model defining the attack model of semantic trajectory linking, together with a privacy notion, called c-safety. This method provides an upper bound to the probability of inferring that a given person, observed in a sequence of nonsensitive places, has also stopped in any sensitive location. Coherently with the privacy model, we propose an algorithm for transforming any dataset of semantic trajectories into a c-safe one. We report a study on a real-life GPS trajectory dataset to show how our algorithm preserves interesting quality/utility measures of the original trajectories, such as sequential pattern mining results. %B SPRINGL %P 47-54 %R 10.1145/1868470.1868481 %0 Conference Paper %B SEBD %D 2010 %T Querying and mining trajectories with gaps: a multi-path reconstruction approach (Extended Abstract) %A Mirco Nanni %A Roberto Trasarti %B SEBD %P 126-133 %0 Journal Article %J Inf. Syst. %D 2009 %T A constraint-based querying system for exploratory pattern discovery %A Francesco Bonchi %A Fosca Giannotti %A Claudio Lucchese %A Salvatore Orlando %A Raffaele Perego %A Roberto Trasarti %B Inf. Syst. %V 34 %P 3-27 %0 Journal Article %J Inf. Syst. %D 2009 %T A constraint-based querying system for exploratory pattern discovery %A Francesco Bonchi %A Fosca Giannotti %A Claudio Lucchese %A Salvatore Orlando %A Raffaele Perego %A Roberto Trasarti %B Inf. Syst. %V 34 %P 3-27 %0 Conference Paper %B DEXA Workshops %D 2009 %T DAMSEL: A System for Progressive Querying and Reasoning on Movement Data %A Roberto Trasarti %A Miriam Baglioni %A Chiara Renso %B DEXA Workshops %P 452-456 %0 Conference Paper %B The European Future Technologies Conference (FET 2009) %D 2009 %T GeoPKDD – Geographic Privacy-aware Knowledge Discovery %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A Chiara Renso %A S Rinzivillo %A Roberto Trasarti %B The European Future Technologies Conference (FET 2009) %0 Conference Paper %B ICDM Workshops %D 2009 %T K-BestMatch Reconstruction and Comparison of Trajectory Data %A Mirco Nanni %A Roberto Trasarti %B ICDM Workshops %P 610-615 %0 Conference Paper %B ICDM Workshops %D 2009 %T K-BestMatch Reconstruction and Comparison of Trajectory Data %A Mirco Nanni %A Roberto Trasarti %B ICDM Workshops %P 610-615 %0 Conference Paper %B CSE (4) %D 2009 %T Mining Mobility Behavior from Trajectory Data %A Fosca Giannotti %A Mirco Nanni %A Dino Pedreschi %A Chiara Renso %A Roberto Trasarti %B CSE (4) %P 948-951 %0 Conference Paper %B SEBD %D 2009 %T A new technique for sequential pattern mining under regular expressions %A Roberto Trasarti %A Francesco Bonchi %A Bart Goethals %B SEBD %P 325-332 %0 Conference Paper %B AGILE Conf. %D 2009 %T Towards Semantic Interpretation of Movement Behavior %A Miriam Baglioni %A de José Antônio Fernandes Macêdo %A Chiara Renso %A Roberto Trasarti %A Monica Wachowicz %B AGILE Conf. %P 271-288 %0 Conference Paper %B AGILE Conf. %D 2009 %T Towards Semantic Interpretation of Movement Behavior %A Miriam Baglioni %A de José Antônio Fernandes Macêdo %A Chiara Renso %A Roberto Trasarti %A Monica Wachowicz %B AGILE Conf. %P 271-288 %0 Conference Proceedings %B 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining %D 2009 %T WhereNext: a Location Predictor on Trajectory Pattern Mining %A Anna Monreale %A Fabio Pinelli %A Roberto Trasarti %A Fosca Giannotti %X The pervasiveness of mobile devices and location based services is leading to an increasing volume of mobility data.This side eect provides the opportunity for innovative methods that analyse the behaviors of movements. In this paper we propose WhereNext, which is a method aimed at predicting with a certain level of accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Patterns, which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with a typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. The tree is learned from the Trajectory Patterns that hold a certain area and it may be used as a predictor of the next location of a new trajectory finding the best matching path in the tree. Three dierent best matching methods to classify a new moving object are proposed and their impact on the quality of prediction is studied extensively. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends on the movement of all available objects in a certain area instead of on the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties that have emerged from the data and this allows us to define matching methods that striclty depend on the properties of such movements. In addition, we propose a set of other measures, that evaluate a priori the predictive power of a set of Trajectory Patterns. This measures were tuned on a real life case study. Finally, an exhaustive set of experiments and results on the real dataset are presented. %B 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining %R 10.1145/1557019.1557091 %0 Conference Paper %B SEBD %D 2008 %T DAEDALUS: A knowledge discovery analysis framework for movement data %A Riccardo Ortale %A E Ritacco %A N. Pelekisy %A Roberto Trasarti %A Gianni Costa %A Fosca Giannotti %A Giuseppe Manco %A Chiara Renso %A Yannis Theodoridis %B SEBD %P 191-198 %G eng %0 Conference Paper %B GIS %D 2008 %T The DAEDALUS framework: progressive querying and mining of movement data %A Riccardo Ortale %A E Ritacco %A Nikos Pelekis %A Roberto Trasarti %A Gianni Costa %A Fosca Giannotti %A Giuseppe Manco %A Chiara Renso %A Yannis Theodoridis %B GIS %P 52 %0 Conference Proceedings %B First International Workshop on Computational Transportation Science %D 2008 %T Location prediction within the mobility data analysis environment Daedalus %A Fabio Pinelli %A Anna Monreale %A Roberto Trasarti %A Fosca Giannotti %X In this paper we propose a method to predict the next location of a moving object based on two recent results in GeoPKDD project: DAEDALUS, a mobility data analysis environment and Trajectory Pattern, a sequential pattern mining algorithm with temporal annotation integrated in DAEDALUS. The first one is a DMQL environment for moving objects, where both data and patterns can be represented. The second one extracts movement patterns as sequences of movements between locations with typical travel times. This paper proposes a prediction method which uses the local models extracted by Trajectory Pattern to build a global model called Prediction Tree. The future location of a moving object is predicted visiting the tree and calculating the best matching function. The integration within DAEDALUS system supports an interactive construction of the predictor on the top of a set of spatio-temporal patterns. Others proposals in literature base the definition of prediction methods for future location of a moving object on previously extracted frequent patterns. They use the recent history of movements of the object itself and often use time only to order the events. Our work uses the movements of all moving objects in a certain area to learn a classifier built on the mined trajectory patterns, which are intrinsically equipped with temporal information. %B First International Workshop on Computational Transportation Science %C Dublin, Ireland %R 10.4108/ICST.MOBIQUITOUS2008.3894 %0 Conference Paper %B ICDE %D 2006 %T ConQueSt: a Constraint-based Querying System for Exploratory Pattern Discovery %A Francesco Bonchi %A Fosca Giannotti %A Claudio Lucchese %A Salvatore Orlando %A Raffaele Perego %A Roberto Trasarti %B ICDE %P 159 %G eng %0 Conference Paper %B SEBD %D 2006 %T On Interactive Pattern Mining from Relational Databases %A Claudio Lucchese %A Francesco Bonchi %A Fosca Giannotti %A Salvatore Orlando %A Raffaele Perego %A Roberto Trasarti %B SEBD %P 329-338 %G eng %0 Conference Paper %B KDID %D 2006 %T On Interactive Pattern Mining from Relational Databases %A Francesco Bonchi %A Fosca Giannotti %A Claudio Lucchese %A Salvatore Orlando %A Raffaele Perego %A Roberto Trasarti %B KDID %P 42-62 %G eng