<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Paolo Cintia</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discovering and Understanding City Events with Big Data: The Case of Rome</style></title><secondary-title><style face="normal" font="default" size="100%">Information</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2017</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2017</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://doi.org/10.3390/info8030074</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">3</style></number><volume><style face="normal" font="default" size="100%">8</style></volume><pages><style face="normal" font="default" size="100%">74</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Daniele Fadda</style></author><author><style face="normal" font="default" size="100%">Leonardo Piccini</style></author><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Patrizia Lattarulo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Big Data and Public Administration: a case study for Tuscany Airports</style></title><secondary-title><style face="normal" font="default" size="100%">SEBD - Italian 	Symposium on  Advanced Database Systems </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2016</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://sebd2016.unisalento.it/grid/SEBD2016-proceedings.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Matematicamente.it</style></publisher><pub-location><style face="normal" font="default" size="100%">Ugento, Lecce (Italy)</style></pub-location><isbn><style face="normal" font="default" size="100%">9788896354889</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Sonia Bergamaschi</style></author><author><style face="normal" font="default" size="100%">Emanuele Carlini</style></author><author><style face="normal" font="default" size="100%">Michelangelo Ceci</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Donato Malerba</style></author><author><style face="normal" font="default" size="100%">Mario Mezzanzanica</style></author><author><style face="normal" font="default" size="100%">Anna Monreale</style></author><author><style face="normal" font="default" size="100%">Gabriella Pasi</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author><author><style face="normal" font="default" size="100%">Raffaele Perego</style></author><author><style face="normal" font="default" size="100%">Salvatore Ruggieri</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Big Data Research in Italy: A Perspective</style></title><secondary-title><style face="normal" font="default" size="100%">Engineering</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2016</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://engineering.org.cn/EN/abstract/article_12288.shtml</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">2</style></number><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">163</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><call-num><style face="normal" font="default" size="100%">10-1244/N</style></call-num></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">City users’ classification with mobile phone data</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Big Data</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/2015</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Santa Clara (CA) - USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Zbigniew Smoreda</style></author><author><style face="normal" font="default" size="100%">Maarten Vanhoof</style></author><author><style face="normal" font="default" size="100%">Cezary Ziemlicki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Detecting and understanding big events in big cities</style></title><secondary-title><style face="normal" font="default" size="100%">NetMob</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.netmob.org/assets/img/netmob15_book_of_abstracts_posters.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Boston</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Use of Mobile Phone Data to Estimate Visitors Mobility Flows</style></title><secondary-title><style face="normal" font="default" size="100%">Software Engineering and Formal Methods</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/chapter/10.1007%2F978-3-319-15201-1_14</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">Lecture Notes in Computer Science</style></number><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><volume><style face="normal" font="default" size="100%">8938</style></volume><pages><style face="normal" font="default" size="100%">214-226</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Big data analytics for smart mobility: a case study</style></title><secondary-title><style face="normal" font="default" size="100%">EDBT/ICDT 2014 Workshops - Mining Urban Data (MUD)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-1133/paper-57.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Athens, Greece</style></pub-location><accession-num><style face="normal" font="default" size="100%">ISSN - 1613-0073</style></accession-num></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Ana-Maria Olteanu-Raimond</style></author><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Thomas Couronné</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Zbigniew Smoreda</style></author><author><style face="normal" font="default" size="100%">Cezary Ziemlicki</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discovering urban and country dynamics from mobile phone data with spatial correlation patterns</style></title><secondary-title><style face="normal" font="default" size="100%">Telecommunications Policy</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Urban dynamics</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0308596113002012</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">-</style></pages><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Paolo Cintia</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mobility Profiling</style></title><secondary-title><style face="normal" font="default" size="100%">Data Science and Simulation in Transportation Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><publisher><style face="normal" font="default" size="100%">IGI Global</style></publisher><pages><style face="normal" font="default" size="100%">1-29</style></pages><abstract><style face="normal" font="default" size="100%">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. </style></abstract><section><style face="normal" font="default" size="100%">1</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Letizia Milli</style></author><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Dino Pedreschi</style></author></authors><subsidiary-authors><author><style face="normal" font="default" size="100%">Roberta Vivio</style></author><author><style face="normal" font="default" size="100%">Giuseppe Garofalo</style></author></subsidiary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach</style></title><secondary-title><style face="normal" font="default" size="100%">47th SIS Scientific Meeting of the Italian Statistica Society</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2014</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sis2014.it/proceedings/allpapers/3026.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Cagliari </style></pub-location><isbn><style face="normal" font="default" size="100%">978-88-8467-874-4</style></isbn><abstract><style face="normal" font="default" size="100%">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)</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Use of mobile phone data to estimate visitors mobility flows</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of MoKMaSD</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2014</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.di.unipi.it/mokmasd/symposium-2014/preproceedings/GabrielliEtAl-mokmasd2014.pdf</style></url></web-urls></urls><abstract><style face="normal" font="default" size="100%">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</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>10</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of GSM Calls Data for Understanding User Mobility Behavior</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Big Data</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Santa Clara, California</style></pub-location></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Paolo Cintia</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Inferring human activities from GPS tracks UrbComp</style></title><secondary-title><style face="normal" font="default" size="100%">Workshop at KDD 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><pub-location><style face="normal" font="default" size="100%">Chicago USA</style></pub-location></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Peter Van Der Mede</style></author><author><style face="normal" font="default" size="100%">Joost De Bruijn</style></author><author><style face="normal" font="default" size="100%">Erik de Romph</style></author><author><style face="normal" font="default" size="100%">Gerard Bruil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">MP4-A Project: Mobility Planning For Africa</style></title><secondary-title><style face="normal" font="default" size="100%">In D4D Challenge @ 3rd Conf. on the Analysis of Mobile Phone datasets (NetMob 2013)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://perso.uclouvain.be/vincent.blondel/netmob/2013/D4D-book.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Cambridge, USA</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Pisa Tourism fluxes Observatory: deriving mobility indicators from GSM call habits</style></title><secondary-title><style face="normal" font="default" size="100%">NetMob Conference 2013</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mirco Nanni</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Peter Van Der Mede</style></author><author><style face="normal" font="default" size="100%">Joost De Bruijn</style></author><author><style face="normal" font="default" size="100%">Erik de Romph</style></author><author><style face="normal" font="default" size="100%">Gerard Bruil</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transportation Planning Based on {GSM} Traces: {A} Case Study on Ivory Coast</style></title><secondary-title><style face="normal" font="default" size="100%">Citizen in Sensor Networks - Second International Workshop, CitiSens 2013, Barcelona, Spain, September 19, 2013, Revised Selected Papers</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-319-04178-0_2</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Roberto Trasarti</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author><author><style face="normal" font="default" size="100%">Luca Pappalardo</style></author><author><style face="normal" font="default" size="100%">Fosca Giannotti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analisi di Mobilita' con dati eterogenei</style></title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">ISTI - CNR</style></publisher><pub-location><style face="normal" font="default" size="100%">Pisa</style></pub-location></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Lorenzo Gabrielli</style></author><author><style face="normal" font="default" size="100%">Chiara Renso</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Identifying users profiles from mobile calls habits</style></title><secondary-title><style face="normal" font="default" size="100%">ACM SIGKDD International Workshop on Urban Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://delivery.acm.org/10.1145/2350000/2346500/p17-furletti.pdf?ip=146.48.83.121&amp;acc=ACTIVE%20SERVICE&amp;CFID=166768290&amp;CFTOKEN=58719386&amp;__acm__=1357648050_e23771c2f6bd8feb96bd66b39294175d</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM New York, NY, USA ©2012</style></publisher><pub-location><style face="normal" font="default" size="100%">Beijing, China</style></pub-location><isbn><style face="normal" font="default" size="100%">978-1-4503-1542-5</style></isbn><abstract><style face="normal" font="default" size="100%">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.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Franco Turini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Knowledge Discovery in Ontologies</style></title><secondary-title><style face="normal" font="default" size="100%">Intelligent Data Analysis</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://iospress.metapress.com/content/765h53w41286p578/fulltext.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">16</style></volume><section><style face="normal" font="default" size="100%">513</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Franco Turini</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">What else can be extracted from ontologies? Influence Rules</style></title><secondary-title><style face="normal" font="default" size="100%">Software and Data Technologies</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Communications in Computer and Information Science</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2012</style></year></dates><publisher><style face="normal" font="default" size="100%">Springer</style></publisher></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Franco Turini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mining Influence Rules out of Ontologies</style></title><secondary-title><style face="normal" font="default" size="100%">International Conference on Software and Data Technologies (ICSOFT)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2011</style></date></pub-dates></dates><pub-location><style face="normal" font="default" size="100%">Siviglia, Spagna</style></pub-location></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Franco Turini</style></author><author><style face="normal" font="default" size="100%">Andrea Bellandi</style></author><author><style face="normal" font="default" size="100%">Miriam Baglioni</style></author><author><style face="normal" font="default" size="100%">Chiara Pratesi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improving the Business Plan Evaluation Process: the Role of Intangibles</style></title><secondary-title><style face="normal" font="default" size="100%">Quality Technology &amp; Quantitative Management</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://web.it.nctu.edu.tw/~qtqm/upcomingpapers/2010V7N1/2010V7N1_F3.pdf</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">1</style></number><volume><style face="normal" font="default" size="100%">7</style></volume><section><style face="normal" font="default" size="100%">35</style></section></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>32</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology Driven Knowledge Discovery</style></title><secondary-title><style face="normal" font="default" size="100%">IMT - Lucca</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><publisher><style face="normal" font="default" size="100%">IMT - Lucca</style></publisher><pub-location><style face="normal" font="default" size="100%">Lucca - Italy</style></pub-location></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Davide Bacciu</style></author><author><style face="normal" font="default" size="100%">Andrea 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font="default" size="100%">978-953-7619-16-9</style></isbn></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Francesco Fornasari</style></author><author><style face="normal" font="default" size="100%">Claudio Montanari</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">AN EXTENSIBLE AND INTERACTIVE SOFTWARE AGENT FOR MOBILE DEVICES BASED ON GPS DATA</style></title><secondary-title><style face="normal" font="default" size="100%">IADIS International Conference Applied Computing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.iadisportal.org/digital-library/mdownload/an-extensible-and-interactive-software-agent-for-mobile-devices-based-on-gps-data</style></url></web-urls></urls><isbn><style face="normal" font="default" size="100%">978-972-8924-56-0</style></isbn></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Andrea Bellandi</style></author><author><style face="normal" font="default" size="100%">Valerio Grossi</style></author><author><style face="normal" font="default" size="100%">Andrea Romei</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontological Support for Association Rule Mining</style></title><secondary-title><style face="normal" font="default" size="100%">IASTED International Conference on Artificial Intelligence and Applications (AIA)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pub-location><style face="normal" font="default" size="100%">Innsbruck, Austria </style></pub-location></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Miriam Baglioni</style></author><author><style face="normal" font="default" size="100%">Andrea Bellandi</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Laura Spinsanti</style></author><author><style face="normal" font="default" size="100%">Franco Turini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology-Based Business Plan Classification</style></title><secondary-title><style face="normal" font="default" size="100%">EDOC</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><pages><style face="normal" font="default" size="100%">365-371</style></pages></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Franco Turini</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Miriam Baglioni</style></author><author><style face="normal" font="default" size="100%">Laura Spinsanti</style></author><author><style face="normal" font="default" size="100%">Andrea Bellandi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology-Based Business Plan Classification</style></title><secondary-title><style face="normal" font="default" size="100%">Enterprise Distributed Object Computing Conference (EDOC)</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&amp;arnumber=4634789</style></url></web-urls></urls><isbn><style face="normal" font="default" size="100%">978-0-7695-3373-5</style></isbn></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Andrea Bellandi</style></author><author><style face="normal" font="default" size="100%">Valerio Grossi</style></author><author><style face="normal" font="default" size="100%">Andrea Romei</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Ontology-Driven Association Rule Extraction: A Case Study</style></title><secondary-title><style face="normal" font="default" size="100%">International Workshop on Contexts and Ontologies: Representation and Reasoning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ceur-ws.org/Vol-298/paper1.pdf</style></url></web-urls></urls><pub-location><style face="normal" font="default" size="100%">Roskilde, Denmark</style></pub-location></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Andrea Bellandi</style></author><author><style face="normal" font="default" size="100%">Andrea Romei</style></author><author><style face="normal" font="default" size="100%">Valerio Grossi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">PUSHING CONSTRAINTS IN ASSOCIATION RULE MINING: AN ONTOLOGY-BASED APPROACH </style></title><secondary-title><style face="normal" font="default" size="100%"> IADIS International Conference WWW/Internet 2007</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.iadisportal.org/digital-library/mdownload/pushing-constraints-in-association-rule-mining-an-ontology-based-approach</style></url></web-urls></urls><isbn><style face="normal" font="default" size="100%">978-972-8924-44-7</style></isbn></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Franco Turini</style></author><author><style face="normal" font="default" size="100%">Miriam Baglioni</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Examples of Integration of Induction and Deduction in Knowledge Discovery</style></title><secondary-title><style face="normal" font="default" size="100%">Reasoning, Action and Interaction in AI Theories and Systems</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">LNAI</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/content/m400v4507476n18g/fulltext.pdf</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4155</style></volume><pages><style face="normal" font="default" size="100%">307-326</style></pages></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Franco Turini</style></author><author><style face="normal" font="default" size="100%">Miriam Baglioni</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">S Rinzivillo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Examples of Integration of Induction and Deduction in Knowledge Discovery</style></title><secondary-title><style face="normal" font="default" size="100%">Reasoning, Action and Interaction in AI Theories and Systems</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><pages><style face="normal" font="default" size="100%">307-326</style></pages></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Miriam Baglioni</style></author><author><style face="normal" font="default" size="100%">Barbara Furletti</style></author><author><style face="normal" font="default" size="100%">Franco Turini</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Tool for Economic Plans analysis based on expert knowledge and data mining techniques</style></title><secondary-title><style face="normal" font="default" size="100%"> IADIS International Conference Applied Computing 2006 </style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2006</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.iadisportal.org/digital-library/mdownload/a-tool-for-economic-plans-analysis-based-on-expert-knowledge-and-data-mining-techniques</style></url></web-urls></urls><isbn><style face="normal" font="default" size="100%">972-8924-09-7</style></isbn></record><record><source-app 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