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 - CONF T1 - Big Data and Public Administration: a case study for Tuscany Airports T2 - SEBD - Italian Symposium on Advanced Database Systems Y1 - 2016 A1 - Barbara Furletti A1 - Daniele Fadda A1 - Leonardo Piccini A1 - Mirco Nanni A1 - Patrizia Lattarulo AB - In the last decade, the fast development of Information and Communication Technologies led to the wide diffusion of sensors able to track various aspects of human activity, as well as the storage and computational capabilities needed to record and analyze them. The so-called Big Data promise to improve the effectiveness of businesses, the quality of urban life, as well as many other fields, including the functioning of public administrations. Yet, translating the wealth of potential information hidden in Big Data to consumable intelligence seems to be still a difficult task, with a limited basis of success stories. This paper reports a project activity centered on a public administration - IRPET, the Regional Institute for Economic Planning of Tuscany (Italy). The paper deals, among other topics, with human mobility and public transportation at a regional scale, summarizing the open questions posed by the Public Administration (PA), the envisioned role that Big Data might have in answering them, the actual challenges that emerged in trying to implement them, and finally the results we obtained, the limitations that emerged and the lessons learned. JF - SEBD - Italian Symposium on Advanced Database Systems PB - Matematicamente.it CY - Ugento, Lecce (Italy) SN - 9788896354889 UR - http://sebd2016.unisalento.it/grid/SEBD2016-proceedings.pdf ER - TY - JOUR T1 - Big Data Research in Italy: A Perspective JF - Engineering Y1 - 2016 A1 - Sonia Bergamaschi A1 - Emanuele Carlini A1 - Michelangelo Ceci A1 - Barbara Furletti A1 - Fosca Giannotti A1 - Donato Malerba A1 - Mario Mezzanzanica A1 - Anna Monreale A1 - Gabriella Pasi A1 - Dino Pedreschi A1 - Raffaele Perego A1 - Salvatore Ruggieri AB - The aim of this article is to synthetically describe the research projects that a selection of Italian universities is undertaking in the context of big data. Far from being exhaustive, this article has the objective of offering a sample of distinct applications that address the issue of managing huge amounts of data in Italy, collected in relation to diverse domains. VL - 2 UR - http://engineering.org.cn/EN/abstract/article_12288.shtml 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 - CHAP T1 - Use of Mobile Phone Data to Estimate Visitors Mobility Flows T2 - Software Engineering and Formal Methods Y1 - 2015 A1 - Lorenzo Gabrielli A1 - Barbara Furletti A1 - Fosca Giannotti A1 - Mirco Nanni A1 - S Rinzivillo AB - Big Data originating from the digital breadcrumbs of human activities, sensed as by-product of the technologies that we use for our daily activities, allows us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data “proxies”, such as the mobile calls data for mobility. In this paper we investigate to what extent data coming from mobile operators could be a support in producing reliable and timely estimates of intra-city mobility flows. The idea is to define an estimation method based on calling data to characterize the mobility habits of visitors at the level of a single municipality. JF - Software Engineering and Formal Methods PB - Springer International Publishing VL - 8938 UR - http://link.springer.com/chapter/10.1007%2F978-3-319-15201-1_14 ER - TY - CONF T1 - 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 - Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach T2 - 47th SIS Scientific Meeting of the Italian Statistica Society Y1 - 2014 A1 - Barbara Furletti A1 - Lorenzo Gabrielli A1 - Fosca Giannotti A1 - Letizia Milli A1 - Mirco Nanni A1 - Dino Pedreschi AB - The Big Data, originating from the digital breadcrumbs of human activi- ties, sensed as a by-product of the technologies that we use for our daily activities, let us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data “proxies”, as the mobile calls data for mobility. In this paper we investigate to what extent such ”big data”, in integration with administrative ones, could be a support in producing reliable and timely estimates of inter-city mobility. The study has been jointly developed by Is- tat, CNR, University of Pisa in the range of interest of the “Commssione di studio avente il compito di orientare le scelte dellIstat sul tema dei Big Data ”. In an on- going project at ISTAT, called “Persons and Places” – based on an integration of administrative data sources, it has been produced a first release of Origin Destina- tion matrix – at municipality level – assuming that the places of residence and that of work (or study) be the terminal points of usual individual mobility for work or study. The coincidence between the city of residence and that of work (or study) – is considered as a proxy of the absence of intercity mobility for a person (we define him a static resident). The opposite case is considered as a proxy of presence of mo- bility (the person is a dynamic resident: commuter or embedded). As administrative data do not contain information on frequency of the mobility, the idea is to specify an estimate method, using calling data as support, to define for each municipality the stock of standing residents, embedded city users and daily city users (commuters) JF - 47th SIS Scientific Meeting of the Italian Statistica Society CY - Cagliari SN - 978-88-8467-874-4 UR - http://www.sis2014.it/proceedings/allpapers/3026.pdf ER - TY - CONF T1 - Use of mobile phone data to estimate visitors mobility flows T2 - Proceedings of MoKMaSD Y1 - 2014 A1 - Lorenzo Gabrielli A1 - Barbara Furletti A1 - Fosca Giannotti A1 - Mirco Nanni A1 - S Rinzivillo AB - Big Data originating from the digital breadcrumbs of human activities, sensed as by-product of the technologies that we use for our daily activities, allows us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data “proxies”, such as the mo- bile calls data for mobility. In this paper we investigate to what extent data coming from mobile operators could be a support in producing reliable and timely estimates of intra-city mobility flows. The idea is to define an estimation method based on calling data to characterize the mobility habits of visitors at the level of a single municipality JF - Proceedings of MoKMaSD UR - http://www.di.unipi.it/mokmasd/symposium-2014/preproceedings/GabrielliEtAl-mokmasd2014.pdf ER - TY - Generic T1 - Analysis of GSM Calls Data for Understanding User Mobility Behavior T2 - IEEE Big Data Y1 - 2013 A1 - Barbara Furletti A1 - Lorenzo Gabrielli A1 - Chiara Renso A1 - S Rinzivillo JF - IEEE Big Data CY - Santa Clara, California ER - TY - CONF T1 - Inferring human activities from GPS tracks UrbComp T2 - Workshop at KDD 2013 Y1 - 2013 A1 - Paolo Cintia A1 - Barbara Furletti A1 - Chiara Renso JF - Workshop at KDD 2013 CY - Chicago USA 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 - CONF T1 - Pisa Tourism fluxes Observatory: deriving mobility indicators from GSM call habits T2 - NetMob Conference 2013 Y1 - 2013 A1 - Barbara Furletti A1 - Lorenzo Gabrielli A1 - Chiara Renso A1 - S Rinzivillo JF - NetMob Conference 2013 ER - TY - CONF T1 - 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 - 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 - CONF T1 - Identifying users profiles from mobile calls habits T2 - ACM SIGKDD International Workshop on Urban Computing Y1 - 2012 A1 - Barbara Furletti A1 - Lorenzo Gabrielli A1 - Chiara Renso A1 - S Rinzivillo AB - The huge quantity of positioning data registered by our mobile phones stimulates several research questions, mainly originating from the combination of this huge quantity of data with the extreme heterogeneity of the tracked user and the low granularity of the data. We propose a methodology to partition the users tracked by GSM phone calls into profiles like resident, commuters, in transit and tourists. The methodology analyses the phone calls with a combination of top-down and bottom up techniques where the top-down phase is based on a sequence of queries that identify some behaviors. The bottom-up is a machine learning phase to find groups of similar call behavior, thus refining the previous step. The integration of the two steps results in the partitioning of mobile traces into these four user categories that can be deeper analyzed, for example to understand the tourist movements in city or the traffic effects of commuters. An experiment on the identification of user profiles on a real dataset collecting call records from one month in the city of Pisa illustrates the methodology. JF - ACM SIGKDD International Workshop on Urban Computing PB - ACM New York, NY, USA ©2012 CY - Beijing, China SN - 978-1-4503-1542-5 UR - http://delivery.acm.org/10.1145/2350000/2346500/p17-furletti.pdf?ip=146.48.83.121&acc=ACTIVE%20SERVICE&CFID=166768290&CFTOKEN=58719386&__acm__=1357648050_e23771c2f6bd8feb96bd66b39294175d ER - TY - JOUR T1 - Knowledge Discovery in Ontologies JF - Intelligent Data Analysis Y1 - 2012 A1 - Barbara Furletti A1 - Franco Turini VL - 16 UR - http://iospress.metapress.com/content/765h53w41286p578/fulltext.pdf ER - TY - CHAP T1 - What else can be extracted from ontologies? Influence Rules T2 - Software and Data Technologies Y1 - 2012 A1 - Franco Turini A1 - Barbara Furletti JF - Software and Data Technologies T3 - Communications in Computer and Information Science PB - Springer ER - TY - CONF T1 - Mining Influence Rules out of Ontologies T2 - International Conference on Software and Data Technologies (ICSOFT) Y1 - 2011 A1 - Barbara Furletti A1 - Franco Turini JF - International Conference on Software and Data Technologies (ICSOFT) CY - Siviglia, Spagna ER - TY - JOUR T1 - Improving the Business Plan Evaluation Process: the Role of Intangibles JF - Quality Technology & Quantitative Management Y1 - 2010 A1 - Barbara Furletti A1 - Franco Turini A1 - Andrea Bellandi A1 - Miriam Baglioni A1 - Chiara Pratesi VL - 7 UR - http://web.it.nctu.edu.tw/~qtqm/upcomingpapers/2010V7N1/2010V7N1_F3.pdf ER - TY - THES T1 - Ontology Driven Knowledge Discovery T2 - IMT - Lucca Y1 - 2009 A1 - Barbara Furletti JF - IMT - Lucca PB - IMT - Lucca CY - Lucca - Italy ER - TY - CHAP T1 - Discovering Strategic Behaviour in Multi- Agent Scenarios by Ontology-Driven Mining T2 - Advances in Robotics, Automation and Control Y1 - 2008 A1 - Davide Bacciu A1 - Andrea Bellandi A1 - Barbara Furletti A1 - Valerio Grossi A1 - Andrea Romei JF - Advances in Robotics, Automation and Control SN - 978-953-7619-16-9 UR - http://www.intechopen.com/books/advances_in_robotics_automation_and_control/discovering_strategic_behaviors_in_multi-agent_scenarios_by_ontology-driven_mining ER - TY - CONF T1 - AN EXTENSIBLE AND INTERACTIVE SOFTWARE AGENT FOR MOBILE DEVICES BASED ON GPS DATA T2 - IADIS International Conference Applied Computing Y1 - 2008 A1 - Barbara Furletti A1 - Francesco Fornasari A1 - Claudio Montanari JF - IADIS International Conference Applied Computing SN - 978-972-8924-56-0 UR - http://www.iadisportal.org/digital-library/mdownload/an-extensible-and-interactive-software-agent-for-mobile-devices-based-on-gps-data ER - TY - CONF T1 - Ontological Support for Association Rule Mining T2 - IASTED International Conference on Artificial Intelligence and Applications (AIA) Y1 - 2008 A1 - Barbara Furletti A1 - Andrea Bellandi A1 - Valerio Grossi A1 - Andrea Romei JF - IASTED International Conference on Artificial Intelligence and Applications (AIA) CY - Innsbruck, Austria ER - TY - CONF T1 - Ontology-Based Business Plan Classification T2 - EDOC Y1 - 2008 A1 - Miriam Baglioni A1 - Andrea Bellandi A1 - Barbara Furletti A1 - Laura Spinsanti A1 - Franco Turini JF - EDOC ER - TY - CONF T1 - Ontology-Based Business Plan Classification T2 - Enterprise Distributed Object Computing Conference (EDOC) Y1 - 2008 A1 - Franco Turini A1 - Barbara Furletti A1 - Miriam Baglioni A1 - Laura Spinsanti A1 - Andrea Bellandi JF - Enterprise Distributed Object Computing Conference (EDOC) SN - 978-0-7695-3373-5 UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4634789 ER - TY - CONF T1 - Ontology-Driven Association Rule Extraction: A Case Study T2 - International Workshop on Contexts and Ontologies: Representation and Reasoning Y1 - 2007 A1 - Barbara Furletti A1 - Andrea Bellandi A1 - Valerio Grossi A1 - Andrea Romei JF - International Workshop on Contexts and Ontologies: Representation and Reasoning CY - Roskilde, Denmark UR - http://ceur-ws.org/Vol-298/paper1.pdf ER - TY - CONF T1 - PUSHING CONSTRAINTS IN ASSOCIATION RULE MINING: AN ONTOLOGY-BASED APPROACH T2 - IADIS International Conference WWW/Internet 2007 Y1 - 2007 A1 - Barbara Furletti A1 - Andrea Bellandi A1 - Andrea Romei A1 - Valerio Grossi JF - IADIS International Conference WWW/Internet 2007 SN - 978-972-8924-44-7 UR - http://www.iadisportal.org/digital-library/mdownload/pushing-constraints-in-association-rule-mining-an-ontology-based-approach ER - TY - CONF T1 - Examples of Integration of Induction and Deduction in Knowledge Discovery T2 - Reasoning, Action and Interaction in AI Theories and Systems Y1 - 2006 A1 - Franco Turini A1 - Miriam Baglioni A1 - Barbara Furletti A1 - S Rinzivillo JF - Reasoning, Action and Interaction in AI Theories and Systems ER - TY - CHAP T1 - Examples of Integration of Induction and Deduction in Knowledge Discovery T2 - Reasoning, Action and Interaction in AI Theories and Systems Y1 - 2006 A1 - Franco Turini A1 - Miriam Baglioni A1 - Barbara Furletti A1 - S Rinzivillo JF - Reasoning, Action and Interaction in AI Theories and Systems T3 - LNAI VL - 4155 UR - http://www.springerlink.com/content/m400v4507476n18g/fulltext.pdf ER - TY - CONF T1 - A Tool for Economic Plans analysis based on expert knowledge and data mining techniques T2 - IADIS International Conference Applied Computing 2006 Y1 - 2006 A1 - Miriam Baglioni A1 - Barbara Furletti A1 - Franco Turini JF - IADIS International Conference Applied Computing 2006 SN - 972-8924-09-7 UR - http://www.iadisportal.org/digital-library/mdownload/a-tool-for-economic-plans-analysis-based-on-expert-knowledge-and-data-mining-techniques ER - TY - CONF T1 - DrC4.5: Improving C4.5 by means of Prior Knowledge T2 - ACM Symposium on Applied Computing Y1 - 2005 A1 - Miriam Baglioni A1 - Barbara Furletti A1 - Franco Turini JF - ACM Symposium on Applied Computing PB - ACM CY - Santa Fe, New Mexico, USA SN - 1-58113-964-0 UR - http://dl.acm.org/ft_gateway.cfm?id=1066787&ftid=311609&dwn=1&CFID=96873366&CFTOKEN=59233511 ER -