TY - JOUR T1 - Give more data, awareness and control to individual citizens, and they will help COVID-19 containment Y1 - 2021 A1 - Mirco Nanni A1 - Andrienko, Gennady A1 - Barabasi, Albert-Laszlo A1 - Boldrini, Chiara A1 - Bonchi, Francesco A1 - Cattuto, Ciro A1 - Chiaromonte, Francesca A1 - Comandé, Giovanni A1 - Conti, Marco A1 - Coté, Mark A1 - Dignum, Frank A1 - Dignum, Virginia A1 - Domingo-Ferrer, Josep A1 - Ferragina, Paolo A1 - Fosca Giannotti A1 - Riccardo Guidotti A1 - Helbing, Dirk A1 - Kaski, Kimmo A1 - Kertész, János A1 - Lehmann, Sune A1 - Lepri, Bruno A1 - Lukowicz, Paul A1 - Matwin, Stan A1 - Jiménez, David Megías A1 - Anna Monreale A1 - Morik, Katharina A1 - Oliver, Nuria A1 - Passarella, Andrea A1 - Passerini, Andrea A1 - Dino Pedreschi A1 - Pentland, Alex A1 - Pianesi, Fabio A1 - Francesca Pratesi A1 - S Rinzivillo A1 - Salvatore Ruggieri A1 - Siebes, Arno A1 - Torra, Vicenc A1 - Roberto Trasarti A1 - Hoven, Jeroen van den A1 - Vespignani, Alessandro AB - The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the “phase 2” of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens’ privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens’ “personal data stores”, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates—if and when they want and for specific aims—with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society. SN - 1572-8439 UR - https://link.springer.com/article/10.1007/s10676-020-09572-w JO - Ethics and Information Technology ER - TY - JOUR T1 - Transparency in Algorithmic Decision Making JF - ERCIM News Y1 - 2019 A1 - Andreas Rauber A1 - Roberto Trasarti A1 - Fosca Giannotti UR - https://ercim-news.ercim.eu/en116/special/transparency-in-algorithmic-decision-making-introduction-to-the-special-theme ER - TY - JOUR T1 - PRUDEnce: a system for assessing privacy risk vs utility in data sharing ecosystems JF - Transactions on Data Privacy Y1 - 2018 A1 - Francesca Pratesi A1 - Anna Monreale A1 - Roberto Trasarti A1 - Fosca Giannotti A1 - Dino Pedreschi A1 - Yanagihara, Tadashi AB - 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. VL - 11 UR - http://www.tdp.cat/issues16/tdp.a284a17.pdf ER - TY - CONF T1 - SoBigData: Social Mining & Big Data Ecosystem T2 - Companion of the The Web Conference 2018 on The Web Conference 2018 Y1 - 2018 A1 - Fosca Giannotti A1 - Roberto Trasarti A1 - Bontcheva, Kalina A1 - Valerio Grossi AB - 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. JF - Companion of the The Web Conference 2018 on The Web Conference 2018 PB - International World Wide Web Conferences Steering Committee UR - http://www.sobigdata.eu/sites/default/files/www%202018.pdf ER - TY - JOUR T1 - Discovering and Understanding City Events with Big Data: The Case of Rome JF - Information Y1 - 2017 A1 - Barbara Furletti A1 - Roberto Trasarti A1 - Paolo Cintia A1 - Lorenzo Gabrielli AB - 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. VL - 8 UR - https://doi.org/10.3390/info8030074 ER - TY - JOUR T1 - HyWare: a HYbrid Workflow lAnguage for Research E-infrastructures JF - D-Lib Magazine Y1 - 2017 A1 - Leonardo Candela A1 - Paolo Manghi A1 - Fosca Giannotti A1 - Valerio Grossi A1 - Roberto Trasarti AB - 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. VL - 23 UR - http://dx.doi.org/10.1045/january2017-candela ER - TY - CONF T1 - Movement Behaviour Recognition for Water Activities T2 - 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 Y1 - 2017 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Fosca Giannotti JF - 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 UR - https://doi.org/10.1007/978-3-319-71970-2_7 ER - TY - JOUR T1 - MyWay: Location prediction via mobility profiling JF - Information Systems Y1 - 2017 A1 - Roberto Trasarti A1 - Riccardo Guidotti A1 - Anna Monreale A1 - Fosca Giannotti AB - 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. VL - 64 ER - TY - CONF T1 - There's A Path For Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas T2 - 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017) Y1 - 2017 A1 - Riccardo Guidotti A1 - Roberto Trasarti A1 - Mirco Nanni A1 - Fosca Giannotti A1 - Dino Pedreschi JF - 4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017) PB - IEEE CY - Tokyo ER - TY - JOUR T1 - Driving Profiles Computation and Monitoring for Car Insurance CRM JF - Journal ACM Transactions on Intelligent Systems and Technology (TIST) Y1 - 2016 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Anna Monreale A1 - Valerio Grossi A1 - Dino Pedreschi AB - 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. VL - 8 UR - http://doi.acm.org/10.1145/2912148 ER - TY - JOUR T1 - Special Issue on Mobile Traffic Analytics JF - Computer Communications Y1 - 2016 A1 - Marco Fiore A1 - Zubair Shafiq A1 - Zbigniew Smoreda A1 - Razvan Stanica A1 - Roberto Trasarti AB - 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. VL - 95 UR - http://dx.doi.org/10.1016/j.comcom.2016.10.009 ER - TY - CONF T1 - City users’ classification with mobile phone data T2 - IEEE Big Data Y1 - 2015 A1 - Lorenzo Gabrielli A1 - Barbara Furletti A1 - Roberto Trasarti A1 - Fosca Giannotti A1 - Dino Pedreschi AB - 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. JF - IEEE Big Data CY - Santa Clara (CA) - USA ER - TY - CONF T1 - Detecting and understanding big events in big cities T2 - NetMob Y1 - 2015 A1 - Barbara Furletti A1 - Lorenzo Gabrielli A1 - Roberto Trasarti A1 - Zbigniew Smoreda A1 - Maarten Vanhoof A1 - Cezary Ziemlicki AB - 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. JF - NetMob CY - Boston UR - http://www.netmob.org/assets/img/netmob15_book_of_abstracts_posters.pdf ER - TY - JOUR T1 - A risk model for privacy in trajectory data JF - Journal of Trust Management Y1 - 2015 A1 - Anirban Basu A1 - Anna Monreale A1 - Roberto Trasarti A1 - Juan Camilo Corena A1 - Fosca Giannotti A1 - Dino Pedreschi A1 - Shinsaku Kiyomoto A1 - Yutaka Miyake A1 - Tadashi Yanagihara AB - 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. VL - 2 ER - TY - CONF T1 - {TOSCA:} two-steps clustering algorithm for personal locations detection T2 - Proceedings of the 23rd {SIGSPATIAL} International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA, November 3-6, 2015 Y1 - 2015 A1 - Riccardo Guidotti A1 - Roberto Trasarti A1 - Mirco Nanni JF - Proceedings of the 23rd {SIGSPATIAL} International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA, November 3-6, 2015 UR - http://doi.acm.org/10.1145/2820783.2820818 ER - TY - CONF T1 - Towards user-centric data management: individual mobility analytics for collective services T2 - Proceedings of the 4th {ACM} {SIGSPATIAL} International Workshop on Mobile Geographic Information Systems, MobiGIS 2015, Bellevue, WA, USA, November 3-6, 2015 Y1 - 2015 A1 - Riccardo Guidotti A1 - Roberto Trasarti A1 - Mirco Nanni A1 - Fosca Giannotti JF - Proceedings of the 4th {ACM} {SIGSPATIAL} International Workshop on Mobile Geographic Information Systems, MobiGIS 2015, Bellevue, WA, USA, November 3-6, 2015 UR - http://doi.acm.org/10.1145/2834126.2834132 ER - TY - CONF T1 - Big data analytics for smart mobility: a case study T2 - EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD) Y1 - 2014 A1 - Barbara Furletti A1 - Roberto Trasarti A1 - Lorenzo Gabrielli A1 - Mirco Nanni A1 - Dino Pedreschi JF - EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD) CY - Athens, Greece UR - http://ceur-ws.org/Vol-1133/paper-57.pdf ER - TY - JOUR T1 - Discovering urban and country dynamics from mobile phone data with spatial correlation patterns JF - Telecommunications Policy Y1 - 2014 A1 - Roberto Trasarti A1 - Ana-Maria Olteanu-Raimond A1 - Mirco Nanni A1 - Thomas Couronné A1 - Barbara Furletti A1 - Fosca Giannotti A1 - Zbigniew Smoreda A1 - Cezary Ziemlicki KW - Urban dynamics AB - 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. UR - http://www.sciencedirect.com/science/article/pii/S0308596113002012 ER - TY - CHAP T1 - Mobility Profiling T2 - Data Science and Simulation in Transportation Research Y1 - 2014 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Paolo Cintia A1 - Barbara Furletti A1 - Chiara Renso A1 - Lorenzo Gabrielli A1 - S Rinzivillo A1 - Fosca Giannotti AB - The ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network. JF - Data Science and Simulation in Transportation Research PB - IGI Global ER - TY - CONF T1 - A Privacy Risk Model for Trajectory Data T2 - Trust Management {VIII} - 8th {IFIP} {WG} 11.11 International Conference, {IFIPTM} 2014, Singapore, July 7-10, 2014. Proceedings Y1 - 2014 A1 - Anirban Basu A1 - Anna Monreale A1 - Juan Camilo Corena A1 - Fosca Giannotti A1 - Dino Pedreschi A1 - Shinsaku Kiyomoto A1 - Yutaka Miyake A1 - Tadashi Yanagihara A1 - Roberto Trasarti AB - 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. JF - Trust Management {VIII} - 8th {IFIP} {WG} 11.11 International Conference, {IFIPTM} 2014, Singapore, July 7-10, 2014. Proceedings UR - http://dx.doi.org/10.1007/978-3-662-43813-8_9 ER - TY - CONF T1 - A Gravity Model for Speed Estimation over Road Network T2 - 2013 {IEEE} 14th International Conference on Mobile Data Management, Milan, Italy, June 3-6, 2013 - Volume 2 Y1 - 2013 A1 - Paolo Cintia A1 - Roberto Trasarti A1 - José Antônio Fernandes de Macêdo A1 - Livia Almada A1 - Camila Fereira JF - 2013 {IEEE} 14th International Conference on Mobile Data Management, Milan, Italy, June 3-6, 2013 - Volume 2 UR - http://dx.doi.org/10.1109/MDM.2013.83 ER - TY - JOUR T1 - How you move reveals who you are: understanding human behavior by analyzing trajectory data JF - Knowl. Inf. Syst. Y1 - 2013 A1 - Chiara Renso A1 - Miriam Baglioni A1 - José Antônio Fernandes de Macêdo A1 - Roberto Trasarti A1 - Monica Wachowicz VL - 37 UR - http://dx.doi.org/10.1007/s10115-012-0511-z ER - TY - CONF T1 - Mob-Warehouse: A semantic approach for mobility analysis with a Trajectory Data Ware- house T2 - SecoGIS 2013 - International Workshop on Semantic Aspects of GIS, Joint to ER conference 2013 Y1 - 2013 A1 - Ricardo Wagner A1 - de José Antônio Fernandes Macêdo A1 - Alessandra Raffaetà A1 - Chiara Renso A1 - Alessandro Roncato A1 - Roberto Trasarti JF - SecoGIS 2013 - International Workshop on Semantic Aspects of GIS, Joint to ER conference 2013 CY - Hong Kong ER - TY - CONF T1 - MP4-A Project: Mobility Planning For Africa T2 - In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013) Y1 - 2013 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Barbara Furletti A1 - Lorenzo Gabrielli A1 - Peter Van Der Mede A1 - Joost De Bruijn A1 - Erik de Romph A1 - Gerard Bruil AB - 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. JF - In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013) CY - Cambridge, USA UR - http://perso.uclouvain.be/vincent.blondel/netmob/2013/D4D-book.pdf ER - TY - JOUR T1 - Towards mega-modeling: a walk through data analysis experiences JF - {SIGMOD} Record Y1 - 2013 A1 - Stefano Ceri A1 - Themis Palpanas A1 - Emanuele Della Valle A1 - Dino Pedreschi A1 - Johann-Christoph Freytag A1 - Roberto Trasarti VL - 42 UR - http://doi.acm.org/10.1145/2536669.2536673 ER - TY - CONF T1 - Transportation Planning Based on {GSM} Traces: {A} Case Study on Ivory Coast T2 - Citizen in Sensor Networks - Second International Workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, Revised Selected Papers Y1 - 2013 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Barbara Furletti A1 - Lorenzo Gabrielli A1 - Peter Van Der Mede A1 - Joost De Bruijn A1 - Erik de Romph A1 - Gerard Bruil JF - Citizen in Sensor Networks - Second International Workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, Revised Selected Papers UR - http://dx.doi.org/10.1007/978-3-319-04178-0_2 ER - TY - CONF T1 - An Agent-Based Model to Evaluate Carpooling at Large Manufacturing Plants T2 - 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 Y1 - 2012 A1 - Tom Bellemans A1 - Sebastian Bothe A1 - Sungjin Cho A1 - Fosca Giannotti A1 - Davy Janssens A1 - Luk Knapen A1 - Christine Körner A1 - Michael May A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Hendrik Stange A1 - Roberto Trasarti A1 - Ansar-Ul-Haque Yasar A1 - Geert Wets JF - 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 UR - http://dx.doi.org/10.1016/j.procs.2012.08.001 ER - TY - RPRT T1 - Analisi di Mobilita' con dati eterogenei Y1 - 2012 A1 - Barbara Furletti A1 - Roberto Trasarti A1 - Lorenzo Gabrielli A1 - S Rinzivillo A1 - Luca Pappalardo A1 - Fosca Giannotti PB - ISTI - CNR CY - Pisa ER - TY - Generic T1 - ComeTogether: Discovering Communities of Places in Mobility Data T2 - MDM 2012 Y1 - 2012 A1 - Igo Brilhante A1 - Michele Berlingerio A1 - Roberto Trasarti A1 - Chiara Renso A1 - de José Antônio Fernandes Macêdo A1 - Marco A. Casanova JF - MDM 2012 ER - TY - CONF T1 - Individual Mobility Profiles: Methods and Application on Vehicle Sharing T2 - Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings Y1 - 2012 A1 - Roberto Trasarti A1 - Fabio Pinelli A1 - Mirco Nanni A1 - Fosca Giannotti JF - Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings UR - http://sebd2012.dei.unipd.it/documents/188475/32d00b8a-8ead-4d97-923f-bd2f2cf6ddcb ER - TY - CONF T1 - Mega-modeling for Big Data Analytics T2 - Conceptual Modeling - 31st International Conference {ER} 2012, Florence, Italy, October 15-18, 2012. Proceedings Y1 - 2012 A1 - Stefano Ceri A1 - Emanuele Della Valle A1 - Dino Pedreschi A1 - Roberto Trasarti JF - Conceptual Modeling - 31st International Conference {ER} 2012, Florence, Italy, October 15-18, 2012. Proceedings UR - http://dx.doi.org/10.1007/978-3-642-34002-4_1 ER - TY - JOUR T1 - C-safety: a framework for the anonymization of semantic trajectories JF - Transactions on Data Privacy Y1 - 2011 A1 - Anna Monreale A1 - Roberto Trasarti A1 - Dino Pedreschi A1 - Chiara Renso A1 - Vania Bogorny AB - 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. VL - 4 UR - http://dl.acm.org/citation.cfm?id=2019319&CFID=803961971&CFTOKEN=35994039 ER - TY - CONF T1 - Mining mobility user profiles for car pooling T2 - KDD Y1 - 2011 A1 - Roberto Trasarti A1 - Fabio Pinelli A1 - Mirco Nanni A1 - Fosca Giannotti JF - KDD ER - TY - JOUR T1 - A Query Language for Mobility Data Mining JF - IJDWM Y1 - 2011 A1 - Roberto Trasarti A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Chiara Renso VL - 7 ER - TY - CONF T1 - Traffic Jams Detection Using Flock Mining T2 - ECML/PKDD (3) Y1 - 2011 A1 - Rebecca Ong A1 - Fabio Pinelli A1 - Roberto Trasarti A1 - Mirco Nanni A1 - Chiara Renso A1 - S Rinzivillo A1 - Fosca Giannotti JF - ECML/PKDD (3) ER - TY - JOUR T1 - Unveiling the complexity of human mobility by querying and mining massive trajectory data JF - VLDB J. Y1 - 2011 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fabio Pinelli A1 - Chiara Renso A1 - S Rinzivillo A1 - Roberto Trasarti VL - 20 ER - TY - CONF T1 - Advanced knowledge discovery on movement data with the GeoPKDD system T2 - EDBT Y1 - 2010 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Chiara Renso A1 - Fosca Giannotti A1 - Dino Pedreschi JF - EDBT ER - TY - CONF T1 - Advanced knowledge discovery on movement data with the GeoPKDD system T2 - EDBT Y1 - 2010 A1 - Mirco Nanni A1 - Roberto Trasarti A1 - Chiara Renso A1 - Fosca Giannotti A1 - Dino Pedreschi JF - EDBT ER - TY - CONF T1 - Exploring Real Mobility Data with M-Atlas T2 - ECML/PKDD (3) Y1 - 2010 A1 - Roberto Trasarti A1 - S Rinzivillo A1 - Fabio Pinelli A1 - Mirco Nanni A1 - Anna Monreale A1 - Chiara Renso A1 - Dino Pedreschi A1 - Fosca Giannotti AB - Research on moving-object data analysis has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing location aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks. These have made available massive repositories of spatio-temporal data recording human mobile activities, that call for suitable analytical methods, capable of enabling the development of innovative, location-aware applications. JF - ECML/PKDD (3) ER - TY - CONF T1 - Location Prediction through Trajectory Pattern Mining (Extended Abstract) T2 - SEBD Y1 - 2010 A1 - Anna Monreale A1 - Fabio Pinelli A1 - Roberto Trasarti A1 - Fosca Giannotti JF - SEBD ER - TY - CONF T1 - Mobility data mining: discovering movement patterns from trajectory data T2 - Computational Transportation Science Y1 - 2010 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Fabio Pinelli A1 - Chiara Renso A1 - S Rinzivillo A1 - Roberto Trasarti JF - Computational Transportation Science ER - TY - CONF T1 - Preserving privacy in semantic-rich trajectories of human mobility T2 - SPRINGL Y1 - 2010 A1 - Anna Monreale A1 - Roberto Trasarti A1 - Chiara Renso A1 - Dino Pedreschi A1 - Vania Bogorny AB - 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. JF - SPRINGL ER - TY - CONF T1 - Querying and mining trajectories with gaps: a multi-path reconstruction approach (Extended Abstract) T2 - SEBD Y1 - 2010 A1 - Mirco Nanni A1 - Roberto Trasarti JF - SEBD ER - TY - JOUR T1 - A constraint-based querying system for exploratory pattern discovery JF - Inf. Syst. Y1 - 2009 A1 - Francesco Bonchi A1 - Fosca Giannotti A1 - Claudio Lucchese A1 - Salvatore Orlando A1 - Raffaele Perego A1 - Roberto Trasarti VL - 34 ER - TY - JOUR T1 - A constraint-based querying system for exploratory pattern discovery JF - Inf. Syst. Y1 - 2009 A1 - Francesco Bonchi A1 - Fosca Giannotti A1 - Claudio Lucchese A1 - Salvatore Orlando A1 - Raffaele Perego A1 - Roberto Trasarti VL - 34 ER - TY - CONF T1 - DAMSEL: A System for Progressive Querying and Reasoning on Movement Data T2 - DEXA Workshops Y1 - 2009 A1 - Roberto Trasarti A1 - Miriam Baglioni A1 - Chiara Renso JF - DEXA Workshops ER - TY - CONF T1 - GeoPKDD – Geographic Privacy-aware Knowledge Discovery T2 - The European Future Technologies Conference (FET 2009) Y1 - 2009 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Chiara Renso A1 - S Rinzivillo A1 - Roberto Trasarti JF - The European Future Technologies Conference (FET 2009) ER - TY - CONF T1 - K-BestMatch Reconstruction and Comparison of Trajectory Data T2 - ICDM Workshops Y1 - 2009 A1 - Mirco Nanni A1 - Roberto Trasarti JF - ICDM Workshops ER - TY - CONF T1 - K-BestMatch Reconstruction and Comparison of Trajectory Data T2 - ICDM Workshops Y1 - 2009 A1 - Mirco Nanni A1 - Roberto Trasarti JF - ICDM Workshops ER - TY - CONF T1 - Mining Mobility Behavior from Trajectory Data T2 - CSE (4) Y1 - 2009 A1 - Fosca Giannotti A1 - Mirco Nanni A1 - Dino Pedreschi A1 - Chiara Renso A1 - Roberto Trasarti JF - CSE (4) ER - TY - CONF T1 - A new technique for sequential pattern mining under regular expressions T2 - SEBD Y1 - 2009 A1 - Roberto Trasarti A1 - Francesco Bonchi A1 - Bart Goethals JF - SEBD ER - TY - CONF T1 - Towards Semantic Interpretation of Movement Behavior T2 - AGILE Conf. Y1 - 2009 A1 - Miriam Baglioni A1 - de José Antônio Fernandes Macêdo A1 - Chiara Renso A1 - Roberto Trasarti A1 - Monica Wachowicz JF - AGILE Conf. ER - TY - CONF T1 - Towards Semantic Interpretation of Movement Behavior T2 - AGILE Conf. Y1 - 2009 A1 - Miriam Baglioni A1 - de José Antônio Fernandes Macêdo A1 - Chiara Renso A1 - Roberto Trasarti A1 - Monica Wachowicz JF - AGILE Conf. ER - TY - Generic T1 - WhereNext: a Location Predictor on Trajectory Pattern Mining T2 - 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Y1 - 2009 A1 - Anna Monreale A1 - Fabio Pinelli A1 - Roberto Trasarti A1 - Fosca Giannotti AB - 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. JF - 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining ER - TY - CONF T1 - DAEDALUS: A knowledge discovery analysis framework for movement data T2 - SEBD Y1 - 2008 A1 - Riccardo Ortale A1 - E Ritacco A1 - N. Pelekisy A1 - Roberto Trasarti A1 - Gianni Costa A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Chiara Renso A1 - Yannis Theodoridis JF - SEBD ER - TY - CONF T1 - The DAEDALUS framework: progressive querying and mining of movement data T2 - GIS Y1 - 2008 A1 - Riccardo Ortale A1 - E Ritacco A1 - Nikos Pelekis A1 - Roberto Trasarti A1 - Gianni Costa A1 - Fosca Giannotti A1 - Giuseppe Manco A1 - Chiara Renso A1 - Yannis Theodoridis JF - GIS ER - TY - Generic T1 - Location prediction within the mobility data analysis environment Daedalus T2 - First International Workshop on Computational Transportation Science Y1 - 2008 A1 - Fabio Pinelli A1 - Anna Monreale A1 - Roberto Trasarti A1 - Fosca Giannotti AB - 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. JF - First International Workshop on Computational Transportation Science CY - Dublin, Ireland ER - TY - CONF T1 - ConQueSt: a Constraint-based Querying System for Exploratory Pattern Discovery T2 - ICDE Y1 - 2006 A1 - Francesco Bonchi A1 - Fosca Giannotti A1 - Claudio Lucchese A1 - Salvatore Orlando A1 - Raffaele Perego A1 - Roberto Trasarti JF - ICDE ER - TY - CONF T1 - On Interactive Pattern Mining from Relational Databases T2 - SEBD Y1 - 2006 A1 - Claudio Lucchese A1 - Francesco Bonchi A1 - Fosca Giannotti A1 - Salvatore Orlando A1 - Raffaele Perego A1 - Roberto Trasarti JF - SEBD ER - TY - CONF T1 - On Interactive Pattern Mining from Relational Databases T2 - KDID Y1 - 2006 A1 - Francesco Bonchi A1 - Fosca Giannotti A1 - Claudio Lucchese A1 - Salvatore Orlando A1 - Raffaele Perego A1 - Roberto Trasarti JF - KDID ER -