@article {1400,
	title = {Give more data, awareness and control to individual citizens, and they will help COVID-19 containment},
	year = {2021},
	month = {2021/02/02},
	abstract = {The rapid dynamics of COVID-19 calls for quick and effective tracking of virus transmission chains and early detection of outbreaks, especially in the {\textquotedblleft}phase 2{\textquotedblright} of the pandemic, when lockdown and other restriction measures are progressively withdrawn, in order to avoid or minimize contagion resurgence. For this purpose, contact-tracing apps are being proposed for large scale adoption by many countries. A centralized approach, where data sensed by the app are all sent to a nation-wide server, raises concerns about citizens{\textquoteright} privacy and needlessly strong digital surveillance, thus alerting us to the need to minimize personal data collection and avoiding location tracking. We advocate the conceptual advantage of a decentralized approach, where both contact and location data are collected exclusively in individual citizens{\textquoteright} {\textquotedblleft}personal data stores{\textquotedblright}, to be shared separately and selectively (e.g., with a backend system, but possibly also with other citizens), voluntarily, only when the citizen has tested positive for COVID-19, and with a privacy preserving level of granularity. This approach better protects the personal sphere of citizens and affords multiple benefits: it allows for detailed information gathering for infected people in a privacy-preserving fashion; and, in turn this enables both contact tracing, and, the early detection of outbreak hotspots on more finely-granulated geographic scale. The decentralized approach is also scalable to large populations, in that only the data of positive patients need be handled at a central level. Our recommendation is two-fold. First to extend existing decentralized architectures with a light touch, in order to manage the collection of location data locally on the device, and allow the user to share spatio-temporal aggregates{\textemdash}if and when they want and for specific aims{\textemdash}with health authorities, for instance. Second, we favour a longer-term pursuit of realizing a Personal Data Store vision, giving users the opportunity to contribute to collective good in the measure they want, enhancing self-awareness, and cultivating collective efforts for rebuilding society.},
	isbn = {1572-8439},
	doi = {https://doi.org/10.1007/s10676-020-09572-w},
	url = {https://link.springer.com/article/10.1007/s10676-020-09572-w},
	author = {Mirco Nanni and Andrienko, Gennady and Barabasi, Albert-Laszlo and Boldrini, Chiara and Bonchi, Francesco and Cattuto, Ciro and Chiaromonte, Francesca and Comand{\'e}, Giovanni and Conti, Marco and Cot{\'e}, Mark and Dignum, Frank and Dignum, Virginia and Domingo-Ferrer, Josep and Ferragina, Paolo and Fosca Giannotti and Riccardo Guidotti and Helbing, Dirk and Kaski, Kimmo and Kert{\'e}sz, J{\'a}nos and Lehmann, Sune and Lepri, Bruno and Lukowicz, Paul and Matwin, Stan and Jim{\'e}nez, David Meg{\'\i}as and Anna Monreale and Morik, Katharina and Oliver, Nuria and Passarella, Andrea and Passerini, Andrea and Dino Pedreschi and Pentland, Alex and Pianesi, Fabio and Francesca Pratesi and S Rinzivillo and Salvatore Ruggieri and Siebes, Arno and Torra, Vicenc and Roberto Trasarti and Hoven, Jeroen van den and Vespignani, Alessandro}
}
@article {1218,
	title = {Transparency in Algorithmic Decision Making},
	journal = {ERCIM News},
	number = {116},
	year = {2019},
	url = {https://ercim-news.ercim.eu/en116/special/transparency-in-algorithmic-decision-making-introduction-to-the-special-theme},
	author = {Andreas Rauber and Roberto Trasarti and Fosca Giannotti}
}
@article {1138,
	title = {PRUDEnce: a system for assessing privacy risk vs utility in data sharing ecosystems},
	journal = {Transactions on Data Privacy},
	volume = {11},
	number = {2},
	year = {2018},
	month = {08/2018},
	abstract = {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{\textquoteright}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.},
	url = {http://www.tdp.cat/issues16/tdp.a284a17.pdf},
	author = {Francesca Pratesi and Anna Monreale and Roberto Trasarti and Fosca Giannotti and Dino Pedreschi and Yanagihara, Tadashi}
}
@conference {1053,
	title = {SoBigData: Social Mining \& Big Data Ecosystem},
	booktitle = {Companion of the The Web Conference 2018 on The Web Conference 2018},
	year = {2018},
	publisher = {International World Wide Web Conferences Steering Committee},
	organization = {International World Wide Web Conferences Steering Committee},
	abstract = {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.},
	url = {http://www.sobigdata.eu/sites/default/files/www\%202018.pdf},
	author = {Fosca Giannotti and Roberto Trasarti and Bontcheva, Kalina and Valerio Grossi}
}
@article {1037,
	title = {Discovering and Understanding City Events with Big Data: The Case of Rome},
	journal = {Information},
	volume = {8},
	number = {3},
	year = {2017},
	month = {06/2017},
	pages = {74},
	abstract = {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.},
	doi = {10.3390/info8030074},
	url = {https://doi.org/10.3390/info8030074},
	author = {Barbara Furletti and Roberto Trasarti and Paolo Cintia and Lorenzo Gabrielli}
}
@article {896,
	title = {HyWare: a HYbrid Workflow lAnguage for Research E-infrastructures},
	journal = {D-Lib Magazine},
	volume = {23},
	number = {1/2},
	year = {2017},
	abstract = {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.},
	doi = {10.1045/january2017-candela},
	url = {http://dx.doi.org/10.1045/january2017-candela},
	author = {Leonardo Candela and Paolo Manghi and Fosca Giannotti and Valerio Grossi and Roberto Trasarti}
}
@conference {1030,
	title = {Movement Behaviour Recognition for Water Activities},
	booktitle = {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},
	year = {2017},
	doi = {10.1007/978-3-319-71970-2_7},
	url = {https://doi.org/10.1007/978-3-319-71970-2_7},
	author = {Mirco Nanni and Roberto Trasarti and Fosca Giannotti}
}
@article {821,
	title = {MyWay: Location prediction via mobility profiling},
	journal = {Information Systems},
	volume = {64},
	year = {2017},
	month = {03/2017},
	pages = {350{\textendash}367},
	abstract = {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.},
	author = {Roberto Trasarti and Riccardo Guidotti and Anna Monreale and Fosca Giannotti}
}
@conference {1031,
	title = {There{\textquoteright}s A Path For Everyone: A Data-Driven Personal Model Reproducing Mobility Agendas},
	booktitle = {4th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017)},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Tokyo},
	author = {Riccardo Guidotti and Roberto Trasarti and Mirco Nanni and Fosca Giannotti and Dino Pedreschi}
}
@article {875,
	title = {Driving Profiles Computation and Monitoring for Car Insurance CRM},
	journal = {Journal ACM Transactions on Intelligent Systems and Technology (TIST)},
	volume = {8},
	number = {1},
	year = {2016},
	pages = {14:1{\textendash}14:26},
	abstract = {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.},
	doi = {10.1145/2912148},
	url = {http://doi.acm.org/10.1145/2912148},
	author = {Mirco Nanni and Roberto Trasarti and Anna Monreale and Valerio Grossi and Dino Pedreschi}
}
@article {897,
	title = {Special Issue on Mobile Traffic Analytics},
	journal = {Computer Communications},
	volume = {95},
	year = {2016},
	pages = {1{\textendash}2},
	abstract = {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.},
	doi = {10.1016/j.comcom.2016.10.009},
	url = {http://dx.doi.org/10.1016/j.comcom.2016.10.009},
	author = {Marco Fiore and Zubair Shafiq and Zbigniew Smoreda and Razvan Stanica and Roberto Trasarti}
}
@conference {756,
	title = {City users{\textquoteright} classification with mobile phone data},
	booktitle = {IEEE Big Data},
	year = {2015},
	month = {11/2015},
	address = {Santa Clara (CA) - USA},
	abstract = {Nowadays mobile phone data are an actual proxy for studying the users{\textquoteright} 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.},
	author = {Lorenzo Gabrielli and Barbara Furletti and Roberto Trasarti and Fosca Giannotti and Dino Pedreschi}
}
@conference {689,
	title = {Detecting and understanding big events in big cities},
	booktitle = {NetMob},
	year = {2015},
	month = {04/2015},
	address = {Boston},
	abstract = {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{\textquoteright} 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.},
	url = {http://www.netmob.org/assets/img/netmob15_book_of_abstracts_posters.pdf},
	author = {Barbara Furletti and Lorenzo Gabrielli and Roberto Trasarti and Zbigniew Smoreda and Maarten Vanhoof and Cezary Ziemlicki}
}
@article {990,
	title = {A risk model for privacy in trajectory data},
	journal = {Journal of Trust Management},
	volume = {2},
	number = {1},
	year = {2015},
	pages = {9},
	abstract = {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.},
	doi = {10.1186/s40493-015-0020-6},
	author = {Anirban Basu and Anna Monreale and Roberto Trasarti and Juan Camilo Corena and Fosca Giannotti and Dino Pedreschi and Shinsaku Kiyomoto and Yutaka Miyake and Tadashi Yanagihara}
}
@conference {898,
	title = {{TOSCA:} two-steps clustering algorithm for personal locations detection},
	booktitle = {Proceedings of the 23rd {SIGSPATIAL} International Conference on Advances in Geographic Information Systems, Bellevue, WA, USA, November 3-6, 2015},
	year = {2015},
	doi = {10.1145/2820783.2820818},
	url = {http://doi.acm.org/10.1145/2820783.2820818},
	author = {Riccardo Guidotti and Roberto Trasarti and Mirco Nanni}
}
@conference {899,
	title = {Towards user-centric data management: individual mobility analytics for collective services},
	booktitle = {Proceedings of the 4th {ACM} {SIGSPATIAL} International Workshop on Mobile Geographic Information Systems, MobiGIS 2015, Bellevue, WA, USA, November 3-6, 2015},
	year = {2015},
	doi = {10.1145/2834126.2834132},
	url = {http://doi.acm.org/10.1145/2834126.2834132},
	author = {Riccardo Guidotti and Roberto Trasarti and Mirco Nanni and Fosca Giannotti}
}
@conference {574,
	title = {Big data analytics for smart mobility: a case study},
	booktitle = {EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD)},
	year = {2014},
	month = {03/2014},
	address = {Athens, Greece},
	url = {http://ceur-ws.org/Vol-1133/paper-57.pdf},
	author = {Barbara Furletti and Roberto Trasarti and Lorenzo Gabrielli and Mirco Nanni and Dino Pedreschi}
}
@article {Trasarti2014,
	title = {Discovering urban and country dynamics from mobile phone data with spatial correlation patterns},
	journal = {Telecommunications Policy},
	year = {2014},
	pages = {-},
	abstract = {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.},
	keywords = {Urban dynamics},
	issn = {0308-5961},
	doi = {http://dx.doi.org/10.1016/j.telpol.2013.12.002},
	url = {http://www.sciencedirect.com/science/article/pii/S0308596113002012},
	author = {Roberto Trasarti and Ana-Maria Olteanu-Raimond and Mirco Nanni and Thomas Couronn{\'e} and Barbara Furletti and Fosca Giannotti and Zbigniew Smoreda and Cezary Ziemlicki}
}
@inbook {575,
	title = {Mobility Profiling},
	booktitle = {Data Science and Simulation in Transportation Research},
	year = {2014},
	pages = {1-29},
	publisher = {IGI Global},
	organization = {IGI Global},
	chapter = {1},
	abstract = {The ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network. },
	doi = {10.4018/978-1-4666-4920-0.ch001},
	author = {Mirco Nanni and Roberto Trasarti and Paolo Cintia and Barbara Furletti and Chiara Renso and Lorenzo Gabrielli and S Rinzivillo and Fosca Giannotti}
}
@conference {565,
	title = {A Privacy Risk Model for Trajectory Data},
	booktitle = {Trust Management {VIII} - 8th {IFIP} {WG} 11.11 International Conference, {IFIPTM} 2014, Singapore, July 7-10, 2014. Proceedings},
	year = {2014},
	pages = {125{\textendash}140},
	abstract = {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.},
	doi = {10.1007/978-3-662-43813-8_9},
	url = {http://dx.doi.org/10.1007/978-3-662-43813-8_9},
	author = {Anirban Basu and Anna Monreale and Juan Camilo Corena and Fosca Giannotti and Dino Pedreschi and Shinsaku Kiyomoto and Yutaka Miyake and Tadashi Yanagihara and Roberto Trasarti}
}
@conference {684,
	title = {A Gravity Model for Speed Estimation over Road Network},
	booktitle = {2013 {IEEE} 14th International Conference on Mobile Data Management, Milan, Italy, June 3-6, 2013 - Volume 2},
	year = {2013},
	doi = {10.1109/MDM.2013.83},
	url = {http://dx.doi.org/10.1109/MDM.2013.83},
	author = {Paolo Cintia and Roberto Trasarti and Jos{\'e} Ant{\^o}nio Fernandes de Mac{\^e}do and Livia Almada and Camila Fereira}
}
@article {681,
	title = {How you move reveals who you are: understanding human behavior by analyzing trajectory data},
	journal = {Knowl. Inf. Syst.},
	volume = {37},
	number = {2},
	year = {2013},
	pages = {331{\textendash}362},
	doi = {10.1007/s10115-012-0511-z},
	url = {http://dx.doi.org/10.1007/s10115-012-0511-z},
	author = {Chiara Renso and Miriam Baglioni and Jos{\'e} Ant{\^o}nio Fernandes de Mac{\^e}do and Roberto Trasarti and Monica Wachowicz}
}
@conference {539,
	title = {Mob-Warehouse: A semantic approach for mobility analysis with a Trajectory Data Ware- house},
	booktitle = {SecoGIS 2013 - International Workshop on Semantic Aspects of GIS, Joint to ER conference 2013},
	year = {2013},
	address = {Hong Kong},
	author = {Ricardo Wagner and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Alessandra Raffaet{\`a} and Chiara Renso and Alessandro Roncato and Roberto Trasarti}
}
@conference {704,
	title = {MP4-A Project: Mobility Planning For Africa},
	booktitle = {In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013)},
	year = {2013},
	address = {Cambridge, USA},
	abstract = {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.},
	url = {http://perso.uclouvain.be/vincent.blondel/netmob/2013/D4D-book.pdf},
	author = {Mirco Nanni and Roberto Trasarti and Barbara Furletti and Lorenzo Gabrielli and Peter Van Der Mede and Joost De Bruijn and Erik de Romph and Gerard Bruil}
}
@article {682,
	title = {Towards mega-modeling: a walk through data analysis experiences},
	journal = {{SIGMOD} Record},
	volume = {42},
	number = {3},
	year = {2013},
	pages = {19{\textendash}27},
	doi = {10.1145/2536669.2536673},
	url = {http://doi.acm.org/10.1145/2536669.2536673},
	author = {Stefano Ceri and Themis Palpanas and Emanuele Della Valle and Dino Pedreschi and Johann-Christoph Freytag and Roberto Trasarti}
}
@conference {683,
	title = {Transportation Planning Based on {GSM} Traces: {A} Case Study on Ivory Coast},
	booktitle = {Citizen in Sensor Networks - Second International Workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, Revised Selected Papers},
	year = {2013},
	doi = {10.1007/978-3-319-04178-0_2},
	url = {http://dx.doi.org/10.1007/978-3-319-04178-0_2},
	author = {Mirco Nanni and Roberto Trasarti and Barbara Furletti and Lorenzo Gabrielli and Peter Van Der Mede and Joost De Bruijn and Erik de Romph and Gerard Bruil}
}
@conference {687,
	title = {An Agent-Based Model to Evaluate Carpooling at Large Manufacturing Plants},
	booktitle = {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},
	year = {2012},
	doi = {10.1016/j.procs.2012.08.001},
	url = {http://dx.doi.org/10.1016/j.procs.2012.08.001},
	author = {Tom Bellemans and Sebastian Bothe and Sungjin Cho and Fosca Giannotti and Davy Janssens and Luk Knapen and Christine K{\"o}rner and Michael May and Mirco Nanni and Dino Pedreschi and Hendrik Stange and Roberto Trasarti and Ansar-Ul-Haque Yasar and Geert Wets}
}
@article {488,
	title = {Analisi di Mobilita{\textquoteright} con dati eterogenei},
	year = {2012},
	institution = {ISTI - CNR},
	address = {Pisa},
	author = {Barbara Furletti and Roberto Trasarti and Lorenzo Gabrielli and S Rinzivillo and Luca Pappalardo and Fosca Giannotti}
}
@proceedings {545,
	title = {ComeTogether: Discovering Communities of Places in Mobility Data},
	year = {2012},
	month = {2012},
	pages = { 268-273},
	author = {Igo Brilhante and Michele Berlingerio and Roberto Trasarti and Chiara Renso and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Marco A. Casanova}
}
@conference {686,
	title = {Individual Mobility Profiles: Methods and Application on Vehicle Sharing},
	booktitle = {Twentieth Italian Symposium on Advanced Database Systems, {SEBD} 2012, Venice, Italy, June 24-27, 2012, Proceedings},
	year = {2012},
	url = {http://sebd2012.dei.unipd.it/documents/188475/32d00b8a-8ead-4d97-923f-bd2f2cf6ddcb},
	author = {Roberto Trasarti and Fabio Pinelli and Mirco Nanni and Fosca Giannotti}
}
@conference {685,
	title = {Mega-modeling for Big Data Analytics},
	booktitle = {Conceptual Modeling - 31st International Conference {ER} 2012, Florence, Italy, October 15-18, 2012. Proceedings},
	year = {2012},
	doi = {10.1007/978-3-642-34002-4_1},
	url = {http://dx.doi.org/10.1007/978-3-642-34002-4_1},
	author = {Stefano Ceri and Emanuele Della Valle and Dino Pedreschi and Roberto Trasarti}
}
@article {MonrealeTPRB11,
	title = {C-safety: a framework for the anonymization of semantic trajectories},
	journal = {Transactions on Data Privacy},
	volume = {4},
	number = {2},
	year = {2011},
	pages = {73-101},
	abstract = {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{\textquoteright}s inference succeeds is much lower than the theoretical upper bound
established.},
	url = {http://dl.acm.org/citation.cfm?id=2019319\&CFID=803961971\&CFTOKEN=35994039},
	author = {Anna Monreale and Roberto Trasarti and Dino Pedreschi and Chiara Renso and Vania Bogorny}
}
@conference {TrasartiPNG11,
	title = {Mining mobility user profiles for car pooling},
	booktitle = {KDD},
	year = {2011},
	pages = {1190-1198},
	author = {Roberto Trasarti and Fabio Pinelli and Mirco Nanni and Fosca Giannotti}
}
@article {TrasartiGNPR11,
	title = {A Query Language for Mobility Data Mining},
	journal = {IJDWM},
	volume = {7},
	number = {1},
	year = {2011},
	pages = {24-45},
	author = {Roberto Trasarti and Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Chiara Renso}
}
@conference {OngPTNRRG11,
	title = {Traffic Jams Detection Using Flock Mining},
	booktitle = {ECML/PKDD (3)},
	year = {2011},
	pages = {650-653},
	author = {Rebecca Ong and Fabio Pinelli and Roberto Trasarti and Mirco Nanni and Chiara Renso and S Rinzivillo and Fosca Giannotti}
}
@article {vlbdjMatlas,
	title = {Unveiling the complexity of human mobility by querying and mining massive trajectory data},
	journal = {VLDB J.},
	volume = {20},
	number = {5},
	year = {2011},
	pages = {695-719},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@conference {NanniTRGP10,
	title = {Advanced knowledge discovery on movement data with the GeoPKDD system},
	booktitle = {EDBT},
	year = {2010},
	pages = {693-696},
	author = {Mirco Nanni and Roberto Trasarti and Chiara Renso and Fosca Giannotti and Dino Pedreschi}
}
@conference {NanniTRGP10,
	title = {Advanced knowledge discovery on movement data with the GeoPKDD system},
	booktitle = {EDBT},
	year = {2010},
	pages = {693-696},
	author = {Mirco Nanni and Roberto Trasarti and Chiara Renso and Fosca Giannotti and Dino Pedreschi}
}
@conference {TrasartiRPNM10,
	title = {Exploring Real Mobility Data with M-Atlas},
	booktitle = {ECML/PKDD (3)},
	year = {2010},
	pages = {624-627},
	abstract = {Research on moving-object data analysis has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing location aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks. These have made available massive repositories of spatio-temporal data recording human mobile activities, that call for suitable analytical methods, capable of enabling the development of innovative, location-aware applications.},
	doi = {10.1007/978-3-642-15939-8_48},
	author = {Roberto Trasarti and S Rinzivillo and Fabio Pinelli and Mirco Nanni and Anna Monreale and Chiara Renso and Dino Pedreschi and Fosca Giannotti}
}
@conference {MonrealePTG10,
	title = {Location Prediction through Trajectory Pattern Mining (Extended Abstract)},
	booktitle = {SEBD},
	year = {2010},
	pages = {134-141},
	author = {Anna Monreale and Fabio Pinelli and Roberto Trasarti and Fosca Giannotti}
}
@conference {GiannottiNPPR10,
	title = {Mobility data mining: discovering movement patterns from trajectory data},
	booktitle = {Computational Transportation Science},
	year = {2010},
	pages = {7-10},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Fabio Pinelli and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@conference {MonrealeTRPB10,
	title = {Preserving privacy in semantic-rich trajectories of human mobility},
	booktitle = {SPRINGL},
	year = {2010},
	pages = {47-54},
	abstract = {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.},
	doi = {10.1145/1868470.1868481},
	author = {Anna Monreale and Roberto Trasarti and Chiara Renso and Dino Pedreschi and Vania Bogorny}
}
@conference {NanniT10,
	title = {Querying and mining trajectories with gaps: a multi-path reconstruction approach (Extended Abstract)},
	booktitle = {SEBD},
	year = {2010},
	pages = {126-133},
	author = {Mirco Nanni and Roberto Trasarti}
}
@article {BonchiGLOPT09,
	title = {A constraint-based querying system for exploratory pattern discovery},
	journal = {Inf. Syst.},
	volume = {34},
	number = {1},
	year = {2009},
	pages = {3-27},
	author = {Francesco Bonchi and Fosca Giannotti and Claudio Lucchese and Salvatore Orlando and Raffaele Perego and Roberto Trasarti}
}
@article {BonchiGLOPT09,
	title = {A constraint-based querying system for exploratory pattern discovery},
	journal = {Inf. Syst.},
	volume = {34},
	number = {1},
	year = {2009},
	pages = {3-27},
	author = {Francesco Bonchi and Fosca Giannotti and Claudio Lucchese and Salvatore Orlando and Raffaele Perego and Roberto Trasarti}
}
@conference {TrasartiBR09,
	title = {DAMSEL: A System for Progressive Querying and Reasoning on Movement Data},
	booktitle = {DEXA Workshops},
	year = {2009},
	pages = {452-456},
	author = {Roberto Trasarti and Miriam Baglioni and Chiara Renso}
}
@conference {fet2009,
	title = {GeoPKDD {\textendash} Geographic Privacy-aware Knowledge Discovery},
	booktitle = {The European Future Technologies Conference (FET 2009)},
	year = {2009},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Chiara Renso and S Rinzivillo and Roberto Trasarti}
}
@conference {NanniT09,
	title = {K-BestMatch Reconstruction and Comparison of Trajectory Data},
	booktitle = {ICDM Workshops},
	year = {2009},
	pages = {610-615},
	author = {Mirco Nanni and Roberto Trasarti}
}
@conference {NanniT09,
	title = {K-BestMatch Reconstruction and Comparison of Trajectory Data},
	booktitle = {ICDM Workshops},
	year = {2009},
	pages = {610-615},
	author = {Mirco Nanni and Roberto Trasarti}
}
@conference {GiannottiNPRT09,
	title = {Mining Mobility Behavior from Trajectory Data},
	booktitle = {CSE (4)},
	year = {2009},
	pages = {948-951},
	author = {Fosca Giannotti and Mirco Nanni and Dino Pedreschi and Chiara Renso and Roberto Trasarti}
}
@conference {TrasartiBG09,
	title = {A new technique for sequential pattern mining under regular expressions},
	booktitle = {SEBD},
	year = {2009},
	pages = {325-332},
	author = {Roberto Trasarti and Francesco Bonchi and Bart Goethals}
}
@conference {BaglioniMRTW09,
	title = {Towards Semantic Interpretation of Movement Behavior},
	booktitle = {AGILE Conf.},
	year = {2009},
	pages = {271-288},
	author = {Miriam Baglioni and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Chiara Renso and Roberto Trasarti and Monica Wachowicz}
}
@conference {BaglioniMRTW09,
	title = {Towards Semantic Interpretation of Movement Behavior},
	booktitle = {AGILE Conf.},
	year = {2009},
	pages = {271-288},
	author = {Miriam Baglioni and de Jos{\'e} Ant{\^o}nio Fernandes Mac{\^e}do and Chiara Renso and Roberto Trasarti and Monica Wachowicz}
}
@proceedings {243,
	title = {WhereNext: a Location Predictor on Trajectory Pattern Mining},
	year = {2009},
	abstract = {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.},
	doi = {10.1145/1557019.1557091},
	author = {Anna Monreale and Fabio Pinelli and Roberto Trasarti and Fosca Giannotti}
}
@conference {DBLP:conf/sebd/O,
	title = {DAEDALUS: A knowledge discovery analysis framework for movement data},
	booktitle = {SEBD},
	year = {2008},
	pages = {191-198},
	author = {Riccardo Ortale and E Ritacco and N. Pelekisy and Roberto Trasarti and Gianni Costa and Fosca Giannotti and Giuseppe Manco and Chiara Renso and Yannis Theodoridis}
}
@conference {RPTCGMRT08,
	title = {The DAEDALUS framework: progressive querying and mining of movement data},
	booktitle = {GIS},
	year = {2008},
	pages = {52},
	author = {Riccardo Ortale and E Ritacco and Nikos Pelekis and Roberto Trasarti and Gianni Costa and Fosca Giannotti and Giuseppe Manco and Chiara Renso and Yannis Theodoridis}
}
@proceedings {241,
	title = {Location prediction within the mobility data analysis environment Daedalus},
	year = {2008},
	address = {Dublin, Ireland},
	abstract = {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.},
	doi = {10.4108/ICST.MOBIQUITOUS2008.3894},
	author = {Fabio Pinelli and Anna Monreale and Roberto Trasarti and Fosca Giannotti}
}
@conference {DBLP:conf/icde/BonchiGLOPT06,
	title = {ConQueSt: a Constraint-based Querying System for Exploratory Pattern Discovery},
	booktitle = {ICDE},
	year = {2006},
	pages = {159},
	author = {Francesco Bonchi and Fosca Giannotti and Claudio Lucchese and Salvatore Orlando and Raffaele Perego and Roberto Trasarti}
}
@conference {DBLP:conf/sebd/LuccheseBGOPT06,
	title = {On Interactive Pattern Mining from Relational Databases},
	booktitle = {SEBD},
	year = {2006},
	pages = {329-338},
	author = {Claudio Lucchese and Francesco Bonchi and Fosca Giannotti and Salvatore Orlando and Raffaele Perego and Roberto Trasarti}
}
@conference {DBLP:conf/kdid/BonchiGLOPT06,
	title = {On Interactive Pattern Mining from Relational Databases},
	booktitle = {KDID},
	year = {2006},
	pages = {42-62},
	author = {Francesco Bonchi and Fosca Giannotti and Claudio Lucchese and Salvatore Orlando and Raffaele Perego and Roberto Trasarti}
}
