@conference {1323,
	title = {Digital Footprints of International Migration on Twitter},
	booktitle = {International Symposium on Intelligent Data Analysis},
	year = {2020},
	publisher = {Springer},
	organization = {Springer},
	abstract = {Studying migration using traditional data has some limitations. To date, there have been several studies proposing innovative methodologies to measure migration stocks and flows from social big data. Nevertheless, a uniform definition of a migrant is difficult to find as it varies from one work to another depending on the purpose of the study and nature of the dataset used. In this work, a generic methodology is developed to identify migrants within the Twitter population. This describes a migrant as a person who has the current residence different from the nationality. The residence is defined as the location where a user spends most of his/her time in a certain year. The nationality is inferred from linguistic and social connections to a migrant{\textquoteright}s country of origin. This methodology is validated first with an internal gold standard dataset and second with two official statistics, and shows strong performance scores and correlation coefficients. Our method has the advantage that it can identify both immigrants and emigrants, regardless of the origin/destination countries. The new methodology can be used to study various aspects of migration, including opinions, integration, attachment, stocks and flows, motivations for migration, etc. Here, we exemplify how trending topics across and throughout different migrant communities can be observed.},
	doi = {https://doi.org/10.1007/978-3-030-44584-3_22},
	url = {https://link.springer.com/chapter/10.1007/978-3-030-44584-3_22},
	author = {Jisu Kim and Alina Sirbu and Fosca Giannotti and Lorenzo Gabrielli}
}
@conference {1420,
	title = {Estimating countries{\textquoteright} peace index through the lens of the world news as monitored by GDELT},
	booktitle = {2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA)},
	year = {2020},
	month = {2020},
	abstract = {Peacefulness is a principal dimension of well-being, and its measurement has lately drawn the attention of researchers and policy-makers. During the last years, novel digital data streams have drastically changed research in this field. In the current study, we exploit information extracted from Global Data on Events, Location, and Tone (GDELT) digital news database, to capture peacefulness through the Global Peace Index (GPI). Applying machine learning techniques, we demonstrate that news media attention, sentiment, and social stability from GDELT can be used as proxies for measuring GPI at a monthly level. Additionally, through the variable importance analysis, we show that each country{\textquoteright}s socio-economic, political, and military profile emerges. This could bring added value to researchers interested in "Data Science for Social Good", to policy-makers, and peacekeeping organizations since they could monitor peacefulness almost real-time, and therefore facilitate timely and more efficient policy-making.
},
	doi = {https://doi.org/10.1109/DSAA49011.2020.00034},
	url = {https://ieeexplore.ieee.org/abstract/document/9260052},
	author = {V. Voukelatou and Luca Pappalardo and Lorenzo Gabrielli and Fosca Giannotti}
}
@article {1421,
	title = {PRIMULE: Privacy risk mitigation for user profiles},
	volume = {125},
	year = {2020},
	month = {2020/01/01/},
	pages = {101786},
	abstract = {The availability of mobile phone data has encouraged the development of different data-driven tools, supporting social science studies and providing new data sources to the standard official statistics. However, this particular kind of data are subject to privacy concerns because they can enable the inference of personal and private information. In this paper, we address the privacy issues related to the sharing of user profiles, derived from mobile phone data, by proposing PRIMULE, a privacy risk mitigation strategy. Such a method relies on PRUDEnce (Pratesi et~al., 2018), a privacy risk assessment framework that provides a methodology for systematically identifying risky-users in a set of data. An extensive experimentation on real-world data shows the effectiveness of PRIMULE strategy in terms of both quality of mobile user profiles and utility of these profiles for analytical services such as the Sociometer (Furletti et~al., 2013), a data mining tool for city users classification.},
	isbn = {0169-023X},
	doi = {https://doi.org/10.1016/j.datak.2019.101786},
	url = {https://www.sciencedirect.com/science/article/pii/S0169023X18305342},
	author = {Francesca Pratesi and Lorenzo Gabrielli and Paolo Cintia and Anna Monreale and Fosca Giannotti}
}
@conference {1046,
	title = {Discovering Mobility Functional Areas: A Mobility Data Analysis Approach},
	booktitle = {International Workshop on Complex Networks},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	abstract = {How do we measure the borders of urban areas and therefore decide which are the functional units of the territory? Nowadays, we typically do that just looking at census data, while in this work we aim to identify functional areas for mobility in a completely data-driven way. Our solution makes use of human mobility data (vehicle trajectories) and consists in an agglomerative process which gradually groups together those municipalities that maximize internal vehicular traffic while minimizing external one. The approach is tested against a dataset of trips involving individuals of an Italian Region, obtaining a new territorial division which allows us to identify mobility attractors. Leveraging such partitioning and external knowledge, we show that our method outperforms the state-of-the-art algorithms. Indeed, the outcome of our approach is of great value to public administrations for creating synergies within the aggregations of the territories obtained.},
	doi = {10.1007/978-3-319-73198-8_27},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-73198-8_27},
	author = {Lorenzo Gabrielli and Daniele Fadda and Giulio Rossetti and Mirco Nanni and Piccinini, Leonardo and Dino Pedreschi and Fosca Giannotti and Patrizia Lattarulo}
}
@article {1130,
	title = {Discovering temporal regularities in retail customers{\textquoteright} shopping behavior},
	journal = {EPJ Data Science},
	volume = {7},
	number = {1},
	year = {2018},
	month = {01/2018},
	pages = {6},
	abstract = {In this paper we investigate the regularities characterizing the temporal purchasing behavior of the customers of a retail market chain. Most of the literature studying purchasing behavior focuses on what customers buy while giving few importance to the temporal dimension. As a consequence, the state of the art does not allow capturing which are the temporal purchasing patterns of each customers. These patterns should describe the customer{\textquoteright}s temporal habits highlighting when she typically makes a purchase in correlation with information about the amount of expenditure, number of purchased items and other similar aggregates. This knowledge could be exploited for different scopes: set temporal discounts for making the purchases of customers more regular with respect the time, set personalized discounts in the day and time window preferred by the customer, provide recommendations for shopping time schedule, etc. To this aim, we introduce a framework for extracting from personal retail data a temporal purchasing profile able to summarize whether and when a customer makes her distinctive purchases. The individual profile describes a set of regular and characterizing shopping behavioral patterns, and the sequences in which these patterns take place. We show how to compare different customers by providing a collective perspective to their individual profiles, and how to group the customers with respect to these comparable profiles. By analyzing real datasets containing millions of shopping sessions we found that there is a limited number of patterns summarizing the temporal purchasing behavior of all the customers, and that they are sequentially followed in a finite number of ways. Moreover, we recognized regular customers characterized by a small number of temporal purchasing behaviors, and changing customers characterized by various types of temporal purchasing behaviors. Finally, we discuss on how the profiles can be exploited both by customers to enable personalized services, and by the retail market chain for providing tailored discounts based on temporal purchasing regularity.},
	doi = {10.1140/epjds/s13688-018-0133-0},
	url = {https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-018-0133-0},
	author = {Riccardo Guidotti and Lorenzo Gabrielli and Anna Monreale and Dino Pedreschi and Fosca Giannotti}
}
@article {1193,
	title = {Gravity and scaling laws of city to city migration},
	journal = {PLOS ONE},
	volume = {13},
	number = {7},
	year = {2018},
	month = {07},
	pages = {1-19},
	abstract = {Models of human migration provide powerful tools to forecast the flow of migrants, measure the impact of a policy, determine the cost of physical and political frictions and more. Here, we analyse the migration of individuals from and to cities in the US, finding that city to city migration follows scaling laws, so that the city size is a significant factor in determining whether, or not, an individual decides to migrate and the city size of both the origin and destination play key roles in the selection of the destination. We observe that individuals from small cities tend to migrate more frequently, tending to move to similar-sized cities, whereas individuals from large cities do not migrate so often, but when they do, they tend to move to other large cities. Building upon these findings we develop a scaling model which describes internal migration as a two-step decision process, demonstrating that it can partially explain migration fluxes based solely on city size. We then consider the impact of distance and construct a gravity-scaling model by combining the observed scaling patterns with the gravity law of migration. Results show that the scaling laws are a significant feature of human migration and that the inclusion of scaling can overcome the limits of the gravity and the radiation models of human migration.},
	doi = {10.1371/journal.pone.0199892},
	url = {https://doi.org/10.1371/journal.pone.0199892},
	author = {Prieto Curiel, Rafael and Luca Pappalardo and Lorenzo Gabrielli and Bishop, Steven Richard}
}
@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}
}
@conference {1038,
	title = {Recognizing Residents and Tourists with Retail Data Using Shopping Profiles},
	booktitle = {International Conference on Smart Objects and Technologies for Social Good},
	year = {2017},
	publisher = {Springer},
	organization = {Springer},
	abstract = {The huge quantity of personal data stored by service providers registering customers daily life enables the analysis of individual fingerprints characterizing the customers{\textquoteright} behavioral profiles. We propose
a framework for recognizing residents, tourists and occasional shoppers
among the customers of a retail market chain. We employ our recognition framework on a real massive dataset containing the shopping transactions of more than one million of customers, and we identify representative temporal shopping profiles for residents, tourists and occasional
customers. Our experiments show that even though residents are about
33\% of the customers they are responsible for more than 90\% of the expenditure. We statistically validate the number of residents and tourists
with national official statistics enabling in this way the adoption of our
recognition framework for the development of novel services and analysis.},
	doi = {10.1007/978-3-319-76111-4_35},
	url = {https://link.springer.com/chapter/10.1007/978-3-319-76111-4_35},
	author = {Riccardo Guidotti and Lorenzo Gabrielli}
}
@article {1029,
	title = {Scalable and flexible clustering solutions for mobile phone-based population indicators},
	journal = {I. J. Data Science and Analytics},
	volume = {4},
	number = {4},
	year = {2017},
	pages = {285{\textendash}299},
	doi = {10.1007/s41060-017-0065-y},
	url = {https://doi.org/10.1007/s41060-017-0065-y},
	author = {Alessandro Lulli and Lorenzo Gabrielli and Patrizio Dazzi and Matteo Dell{\textquoteright}Amico and Pietro Michiardi and Mirco Nanni and Laura Ricci}
}
@article {961,
	title = {An analytical framework to nowcast well-being using mobile phone data},
	journal = {International Journal of Data Science and Analytics},
	volume = {2},
	number = {1-2},
	year = {2016},
	pages = {75{\textendash}92},
	abstract = {An intriguing open question is whether measurements derived from Big Data recording human activities can yield high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users{\textquoteright} trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly {\textquotedblleft}nowcast{\textquotedblright} the well-being and the socio-economic development of a territory.},
	doi = {10.1007/s41060-016-0013-2},
	author = {Luca Pappalardo and Maarten Vanhoof and Lorenzo Gabrielli and Zbigniew Smoreda and Dino Pedreschi and Fosca Giannotti}
}
@inbook {964,
	title = {Understanding human mobility with big data},
	booktitle = {Solving Large Scale Learning Tasks. Challenges and Algorithms},
	year = {2016},
	pages = {208{\textendash}220},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {The paper illustrates basic methods of mobility data mining, designed to extract from the big mobility data the patterns of collective movement behavior, i.e., discover the subgroups of travelers characterized by a common purpose, profiles of individual movement activity, i.e., characterize the routine mobility of each traveler. We illustrate a number of concrete case studies where mobility data mining is put at work to create powerful analytical services for policy makers, businesses, public administrations, and individual citizens.},
	doi = {10.1007/978-3-319-41706-6_10},
	author = {Fosca Giannotti and Lorenzo Gabrielli and Dino Pedreschi and S Rinzivillo}
}
@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 {724,
	title = {Small Area Model-Based Estimators Using Big Data Sources},
	journal = {Journal of Official Statistics},
	volume = {31},
	number = {2},
	year = {2015},
	pages = {263{\textendash}281},
	author = {Stefano Marchetti and Caterina Giusti and Monica Pratesi and Nicola Salvati and Fosca Giannotti and Dino Pedreschi and S Rinzivillo and Luca Pappalardo and Lorenzo Gabrielli}
}
@inbook {777,
	title = {Use of Mobile Phone Data to Estimate Visitors Mobility Flows},
	booktitle = {Software Engineering and Formal Methods},
	volume = {8938},
	number = {Lecture Notes in Computer Science},
	year = {2015},
	pages = {214-226},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {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 {\textquotedblleft}proxies{\textquotedblright}, 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.},
	issn = {978-3-319-15200-4},
	doi = {10.1007/978-3-319-15201-1_14},
	url = {http://link.springer.com/chapter/10.1007\%2F978-3-319-15201-1_14},
	author = {Lorenzo Gabrielli and Barbara Furletti and Fosca Giannotti and Mirco Nanni and S Rinzivillo}
}
@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}
}
@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 {725,
	title = {The purpose of motion: Learning activities from Individual Mobility Networks},
	booktitle = {International Conference on Data Science and Advanced Analytics, {DSAA} 2014, Shanghai, China, October 30 - November 1, 2014},
	year = {2014},
	doi = {10.1109/DSAA.2014.7058090},
	url = {http://dx.doi.org/10.1109/DSAA.2014.7058090},
	author = {S Rinzivillo and Lorenzo Gabrielli and Mirco Nanni and Luca Pappalardo and Dino Pedreschi and Fosca Giannotti}
}
@conference {573,
	title = {Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach},
	booktitle = {47th SIS Scientific Meeting of the Italian Statistica Society},
	year = {2014},
	month = {06/2014},
	address = {Cagliari },
	abstract = {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 {\textquotedblleft}proxies{\textquotedblright}, as the mobile
calls data for mobility. In this paper we investigate to what extent such {\textquotedblright}big data{\textquotedblright},
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 {\textquotedblleft}Commssione di studio
avente il compito di orientare le scelte dellIstat sul tema dei Big Data {\textquotedblright}. In an on-
going project at ISTAT, called {\textquotedblleft}Persons and Places{\textquotedblright} {\textendash} based on an integration of
administrative data sources, it has been produced a first release of Origin Destina-
tion matrix {\textendash} at municipality level {\textendash} 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) {\textendash}
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)},
	isbn = {978-88-8467-874-4},
	url = {http://www.sis2014.it/proceedings/allpapers/3026.pdf},
	author = {Barbara Furletti and Lorenzo Gabrielli and Fosca Giannotti and Letizia Milli and Mirco Nanni and Dino Pedreschi}
}
@conference {MokMasd2014,
	title = {Use of mobile phone data to estimate visitors mobility flows},
	booktitle = {Proceedings of MoKMaSD},
	year = {2014},
	abstract = {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 {\textquotedblleft}proxies{\textquotedblright}, 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},
	url = {http://www.di.unipi.it/mokmasd/symposium-2014/preproceedings/GabrielliEtAl-mokmasd2014.pdf},
	author = {Lorenzo Gabrielli and Barbara Furletti and Fosca Giannotti and Mirco Nanni and S Rinzivillo}
}
@proceedings {528,
	title = {Analysis of GSM Calls Data for Understanding User Mobility Behavior},
	year = {2013},
	address = {Santa Clara, California},
	author = {Barbara Furletti and Lorenzo Gabrielli and Chiara Renso and S Rinzivillo}
}
@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}
}
@conference {535,
	title = {Pisa Tourism fluxes Observatory: deriving mobility indicators from GSM call habits},
	booktitle = {NetMob Conference 2013},
	year = {2013},
	author = {Barbara Furletti and Lorenzo Gabrielli and Chiara Renso and S Rinzivillo}
}
@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 {daytag2013,
	title = {Where Have You Been Today? Annotating Trajectories with DayTag},
	booktitle = {International Conference on Spatial and Spatio-temporal Databases (SSTD)},
	year = {2013},
	pages = {467-471},
	doi = {http://dx.doi.org/10.1007/978-3-642-40235-7_30},
	author = {S Rinzivillo and Fernando de Lucca Siqueira and Lorenzo Gabrielli and Chiara Renso and Vania Bogorny}
}
@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}
}
@conference {487,
	title = {Identifying users profiles from mobile calls habits},
	booktitle = {ACM SIGKDD International Workshop on Urban Computing},
	year = {2012},
	publisher = {ACM New York, NY, USA {\textcopyright}2012},
	organization = {ACM New York, NY, USA {\textcopyright}2012},
	address = {Beijing, China},
	abstract = {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.},
	isbn = {978-1-4503-1542-5},
	doi = {10.1145/2346496.2346500},
	url = {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},
	author = {Barbara Furletti and Lorenzo Gabrielli and Chiara Renso and S Rinzivillo}
}
