@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 {834,
	title = {Big Data and Public Administration: a case study for Tuscany Airports},
	booktitle = {SEBD - Italian 	Symposium on  Advanced Database Systems },
	year = {2016},
	month = {06/2016},
	publisher = {Matematicamente.it},
	organization = {Matematicamente.it},
	address = {Ugento, Lecce (Italy)},
	abstract = {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.},
	isbn = {9788896354889},
	url = {http://sebd2016.unisalento.it/grid/SEBD2016-proceedings.pdf},
	author = {Barbara Furletti and Daniele Fadda and Leonardo Piccini and Mirco Nanni and Patrizia Lattarulo}
}
@article {852,
	title = {Big Data Research in Italy: A Perspective},
	journal = {Engineering},
	volume = {2},
	number = {2},
	year = {2016},
	month = {06/2016},
	pages = {163},
	abstract = {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.},
	issn = {print: 2095-8099 / online: 2096-0026},
	doi = {10.1016/J.ENG.2016.02.011},
	url = {http://engineering.org.cn/EN/abstract/article_12288.shtml},
	author = {Sonia Bergamaschi and Emanuele Carlini and Michelangelo Ceci and Barbara Furletti and Fosca Giannotti and Donato Malerba and Mario Mezzanzanica and Anna Monreale and Gabriella Pasi and Dino Pedreschi and Raffaele Perego and Salvatore Ruggieri}
}
@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}
}
@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}
}
@article {Trasarti2014,
	title = {Discovering urban and country dynamics from mobile phone data with spatial correlation patterns},
	journal = {Telecommunications Policy},
	year = {2014},
	pages = {-},
	abstract = {Abstract Mobile communication technologies pervade our society and existing wireless networks are able to sense the movement of people, generating large volumes of data related to human activities, such as mobile phone call records. At the present, this kind of data is collected and stored by telecom operators infrastructures mainly for billing reasons, yet it represents a major source of information in the study of human mobility. In this paper, we propose an analytical process aimed at extracting interconnections between different areas of the city that emerge from highly correlated temporal variations of population local densities. To accomplish this objective, we propose a process based on two analytical tools: (i) a method to estimate the presence of people in different geographical areas; and (ii) a method to extract time- and space-constrained sequential patterns capable to capture correlations among geographical areas in terms of significant co-variations of the estimated presence. The methods are presented and combined in order to deal with two real scenarios of different spatial scale: the Paris Region and the whole France.},
	keywords = {Urban dynamics},
	issn = {0308-5961},
	doi = {http://dx.doi.org/10.1016/j.telpol.2013.12.002},
	url = {http://www.sciencedirect.com/science/article/pii/S0308596113002012},
	author = {Roberto Trasarti and Ana-Maria Olteanu-Raimond and Mirco Nanni and Thomas Couronn{\'e} and Barbara Furletti and Fosca Giannotti and Zbigniew Smoreda and Cezary Ziemlicki}
}
@inbook {575,
	title = {Mobility Profiling},
	booktitle = {Data Science and Simulation in Transportation Research},
	year = {2014},
	pages = {1-29},
	publisher = {IGI Global},
	organization = {IGI Global},
	chapter = {1},
	abstract = {The ability to understand the dynamics of human mobility is crucial for tasks like urban planning and transportation management. The recent rapidly growing availability of large spatio-temporal datasets gives us the possibility to develop sophisticated and accurate analysis methods and algorithms that can enable us to explore several relevant mobility phenomena: the distinct access paths to a territory, the groups of persons that move together in space and time, the regions of a territory that contains a high density of traffic demand, etc. All these paradigmatic perspectives focus on a collective view of the mobility where the interesting phenomenon is the result of the contribution of several moving objects. In this chapter, the authors explore a different approach to the topic and focus on the analysis and understanding of relevant individual mobility habits in order to assign a profile to an individual on the basis of his/her mobility. This process adds a semantic level to the raw mobility data, enabling further analyses that require a deeper understanding of the data itself. The studies described in this chapter are based on two large datasets of spatio-temporal data, originated, respectively, from GPS-equipped devices and from a mobile phone network. },
	doi = {10.4018/978-1-4666-4920-0.ch001},
	author = {Mirco Nanni and Roberto Trasarti and Paolo Cintia and Barbara Furletti and Chiara Renso and Lorenzo Gabrielli and S Rinzivillo and Fosca Giannotti}
}
@conference {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 {537,
	title = {Inferring human activities from GPS tracks UrbComp},
	booktitle = {Workshop at KDD 2013},
	year = {2013},
	address = {Chicago USA},
	author = {Paolo Cintia and Barbara Furletti and Chiara Renso}
}
@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}
}
@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}
}
@article {475,
	title = {Knowledge Discovery in Ontologies},
	journal = {Intelligent Data Analysis},
	volume = {16},
	year = {2012},
	chapter = {513},
	issn = {1571-4128},
	doi = {10.3233/IDA-2012-0536},
	url = {http://iospress.metapress.com/content/765h53w41286p578/fulltext.pdf},
	author = {Barbara Furletti and Franco Turini}
}
@inbook {476,
	title = {What else can be extracted from ontologies? Influence Rules},
	booktitle = {Software and Data Technologies},
	series = {Communications in Computer and Information Science},
	year = {2012},
	publisher = {Springer},
	organization = {Springer},
	author = {Franco Turini and Barbara Furletti}
}
@conference {474,
	title = {Mining Influence Rules out of Ontologies},
	booktitle = {International Conference on Software and Data Technologies (ICSOFT)},
	year = {2011},
	month = {2011},
	address = {Siviglia, Spagna},
	author = {Barbara Furletti and Franco Turini}
}
@article {473,
	title = {Improving the Business Plan Evaluation Process: the Role of Intangibles},
	journal = {Quality Technology \& Quantitative Management},
	volume = {7},
	number = {1},
	year = {2010},
	month = {2010},
	chapter = {35},
	issn = {1684-3703},
	url = {http://web.it.nctu.edu.tw/~qtqm/upcomingpapers/2010V7N1/2010V7N1_F3.pdf},
	author = {Barbara Furletti and Franco Turini and Andrea Bellandi and Miriam Baglioni and Chiara Pratesi}
}
@mastersthesis {448,
	title = {Ontology Driven Knowledge Discovery},
	year = {2009},
	school = {IMT - Lucca},
	address = {Lucca - Italy},
	author = {Barbara Furletti}
}
@inbook {472,
	title = {Discovering Strategic Behaviour in Multi- Agent Scenarios by Ontology-Driven Mining},
	booktitle = {Advances in Robotics, Automation and Control},
	year = {2008},
	isbn = {978-953-7619-16-9},
	url = {http://www.intechopen.com/books/advances_in_robotics_automation_and_control/discovering_strategic_behaviors_in_multi-agent_scenarios_by_ontology-driven_mining},
	author = {Davide Bacciu and Andrea Bellandi and Barbara Furletti and Valerio Grossi and Andrea Romei}
}
@conference {470,
	title = {AN EXTENSIBLE AND INTERACTIVE SOFTWARE AGENT FOR MOBILE DEVICES BASED ON GPS DATA},
	booktitle = {IADIS International Conference Applied Computing},
	year = {2008},
	month = {2008},
	isbn = {978-972-8924-56-0},
	url = {http://www.iadisportal.org/digital-library/mdownload/an-extensible-and-interactive-software-agent-for-mobile-devices-based-on-gps-data},
	author = {Barbara Furletti and Francesco Fornasari and Claudio Montanari}
}
@conference {469,
	title = {Ontological Support for Association Rule Mining},
	booktitle = {IASTED International Conference on Artificial Intelligence and Applications (AIA)},
	year = {2008},
	address = {Innsbruck, Austria },
	author = {Barbara Furletti and Andrea Bellandi and Valerio Grossi and Andrea Romei}
}
@conference {BaglioniBFST08,
	title = {Ontology-Based Business Plan Classification},
	booktitle = {EDOC},
	year = {2008},
	pages = {365-371},
	author = {Miriam Baglioni and Andrea Bellandi and Barbara Furletti and Laura Spinsanti and Franco Turini}
}
@conference {471,
	title = {Ontology-Based Business Plan Classification},
	booktitle = {Enterprise Distributed Object Computing Conference (EDOC)},
	year = {2008},
	month = {2008},
	isbn = {978-0-7695-3373-5},
	doi = {http://dx.doi.org/10.1109/EDOC.2008.30},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=4634789},
	author = {Franco Turini and Barbara Furletti and Miriam Baglioni and Laura Spinsanti and Andrea Bellandi}
}
@conference {468,
	title = {Ontology-Driven Association Rule Extraction: A Case Study},
	booktitle = {International Workshop on Contexts and Ontologies: Representation and Reasoning},
	year = {2007},
	month = {2007},
	address = {Roskilde, Denmark},
	url = {http://ceur-ws.org/Vol-298/paper1.pdf},
	author = {Barbara Furletti and Andrea Bellandi and Valerio Grossi and Andrea Romei}
}
@conference {467,
	title = {PUSHING CONSTRAINTS IN ASSOCIATION RULE MINING: AN ONTOLOGY-BASED APPROACH },
	booktitle = { IADIS International Conference WWW/Internet 2007},
	year = {2007},
	month = {2007},
	isbn = {978-972-8924-44-7},
	url = {http://www.iadisportal.org/digital-library/mdownload/pushing-constraints-in-association-rule-mining-an-ontology-based-approach},
	author = {Barbara Furletti and Andrea Bellandi and Andrea Romei and Valerio Grossi}
}
@inbook {466,
	title = {Examples of Integration of Induction and Deduction in Knowledge Discovery},
	booktitle = {Reasoning, Action and Interaction in AI Theories and Systems},
	series = {LNAI},
	volume = {4155},
	year = {2006},
	pages = {307-326},
	doi = {10.1007/11829263_17},
	url = {http://www.springerlink.com/content/m400v4507476n18g/fulltext.pdf},
	author = {Franco Turini and Miriam Baglioni and Barbara Furletti and S Rinzivillo}
}
@conference {TuriniBFR06,
	title = {Examples of Integration of Induction and Deduction in Knowledge Discovery},
	booktitle = {Reasoning, Action and Interaction in AI Theories and Systems},
	year = {2006},
	pages = {307-326},
	author = {Franco Turini and Miriam Baglioni and Barbara Furletti and S Rinzivillo}
}
@conference {465,
	title = {A Tool for Economic Plans analysis based on expert knowledge and data mining techniques},
	booktitle = { IADIS International Conference Applied Computing 2006 },
	year = {2006},
	month = {2006},
	isbn = {972-8924-09-7},
	url = {http://www.iadisportal.org/digital-library/mdownload/a-tool-for-economic-plans-analysis-based-on-expert-knowledge-and-data-mining-techniques},
	author = {Miriam Baglioni and Barbara Furletti and Franco Turini}
}
@conference {464,
	title = {DrC4.5: Improving C4.5 by means of Prior Knowledge},
	booktitle = {ACM Symposium on Applied Computing},
	year = {2005},
	publisher = {ACM},
	organization = {ACM},
	address = {Santa Fe, New Mexico, USA},
	isbn = {1-58113-964-0},
	doi = {http://dx.doi.org/10.1145/1066677.1066787},
	url = {http://dl.acm.org/ft_gateway.cfm?id=1066787\&ftid=311609\&dwn=1\&CFID=96873366\&CFTOKEN=59233511},
	author = {Miriam Baglioni and Barbara Furletti and Franco Turini}
}
