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2014
Contribution to book
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Mobility profiling
Nanni M., Trasarti R., Cintia P., Furletti B., Gabrielli L., Rinzivillo S., Giannotti F.
An abstract is not availableSource: Data Science and Simulation in Transportation Research, edited by Davy Janssens, Ansar-Ul-Haque Yasar, Luk Knapen, pp. 1–29. Hershey: IGI Global, 2014
DOI: 10.4018/978-1-4666-4920-0.ch001
Metrics:
Nanni M., Trasarti R., Cintia P., Furletti B., Gabrielli L., Rinzivillo S., Giannotti F.
An abstract is not availableSource: Data Science and Simulation in Transportation Research, edited by Davy Janssens, Ansar-Ul-Haque Yasar, Luk Knapen, pp. 1–29. Hershey: IGI Global, 2014
DOI: 10.4018/978-1-4666-4920-0.ch001
Metrics:
See at:
www.igi-global.com 
| www.igi-global.com | CNR ExploRA
2013
Contribution to conference
Restricted
Discovering urban and country dynamics from mobile phone data with spatial correlation patterns
Trasarti R., Olteanu-Raimond A., Nanni M., Couronné T., Furletti B., Giannotti F., Smoreda Z., Ziemlicki C.
Source: NetMob 2013 - Third International Conference on the Analysis of Mobile Phone Datasets, pp. 10–12, MIT Media Lab, Cambridge, USA, 1-3 May 2013
Trasarti R., Olteanu-Raimond A., Nanni M., Couronné T., Furletti B., Giannotti F., Smoreda Z., Ziemlicki C.
Source: NetMob 2013 - Third International Conference on the Analysis of Mobile Phone Datasets, pp. 10–12, MIT Media Lab, Cambridge, USA, 1-3 May 2013
See at:
perso.uclouvain.be | CNR ExploRA
2012
Report
Unknown
Analisi di mobilità con dati eterogenei
Furletti B., Trasarti R., Gabrielli L., Rinzivillo S., Pappalardo L., Giannotti F.
Technical report about mobility data analysis, studies and experiments in Tuscany, by using mobility data, as: variable message signs data, gps and gsm data, and demographic data. These analysis and methods are the results of several projects and researches of KDDLab.Source: ISTI Technical reports, 2012
Furletti B., Trasarti R., Gabrielli L., Rinzivillo S., Pappalardo L., Giannotti F.
Technical report about mobility data analysis, studies and experiments in Tuscany, by using mobility data, as: variable message signs data, gps and gsm data, and demographic data. These analysis and methods are the results of several projects and researches of KDDLab.Source: ISTI Technical reports, 2012
See at: CNR ExploRA
2014
Report
Unknown
Valutazione del rischio di privacy nel processo di costruzione dei modelli di call habit che sottostanno al sociometro = Assessing the Privacy Risk in the Process of Building Call Habit Models that Underlie the Sociometer
Furletti B., Gabrielli L., Monreale A., Nanni M., Pratesi F., Rinzivillo S., Giannotti F., Pedreschi D.
The paper discusses in detail the problem of the privacy of the users of the original phone data, demonstrating the possibility to measure the risk of identification from the compact representation of the profiles.Source: ISTI Technical reports, 2014
Furletti B., Gabrielli L., Monreale A., Nanni M., Pratesi F., Rinzivillo S., Giannotti F., Pedreschi D.
The paper discusses in detail the problem of the privacy of the users of the original phone data, demonstrating the possibility to measure the risk of identification from the compact representation of the profiles.Source: ISTI Technical reports, 2014
See at: CNR ExploRA
2011
Conference article
Unknown
Mining influence rules out of ontologies
Furletti B., Turini F.
A method for extracting new implicit knowledge starting from the ontology schema by using an inductive/ deductive approach is presented. By giving a new interpretation to relationships that already exist in an ontology, we are able to return the extracted knowledge as weighted If-Then Rules among concepts. The technique, that combines data mining and link analysis, is completely general and applicable to whatever domain. Since the output is a set of "standard" If-Then Rules, it can be used to integrate existing knowledge or for supporting any other data mining process. An application of the method to an ontology representing companies and their activities is included.Source: ICSOFT 2011 - International Conference on Software and Data Technologies, pp. 323–333, Seville, Spain, 18-21 July 2011
Furletti B., Turini F.
A method for extracting new implicit knowledge starting from the ontology schema by using an inductive/ deductive approach is presented. By giving a new interpretation to relationships that already exist in an ontology, we are able to return the extracted knowledge as weighted If-Then Rules among concepts. The technique, that combines data mining and link analysis, is completely general and applicable to whatever domain. Since the output is a set of "standard" If-Then Rules, it can be used to integrate existing knowledge or for supporting any other data mining process. An application of the method to an ontology representing companies and their activities is included.Source: ICSOFT 2011 - International Conference on Software and Data Technologies, pp. 323–333, Seville, Spain, 18-21 July 2011
See at: CNR ExploRA
2013
Contribution to book
Restricted
What else can be extracted from ontologies? Influence rules
Furletti B., Turini F.
A method for extracting new implicit knowledge from ontologies by using an inductive/deductive approach is presented. The new extracted knowledge takes the form of If-Then rules annotated with a weight. Such rules, termed Influence Rules, specify how the values of the properties bound to a collection of concepts may influence the values of the properties of another concept.The technique, that combines data mining and link analysis, is completely general and applicable to whatever domain. The paper reports the methods and the algorithms supporting the process of mining the rules out of the ontology, and discusses its application to real data from the economic field.Source: Software and Data Technologies. Revised selected papers, edited by María José Escalona, José Cordeiro, Boris Shishkov, pp. 270–285. Heidelberg: Springer, 2013
DOI: 10.1007/978-3-642-36177-7_17
Metrics:
Furletti B., Turini F.
A method for extracting new implicit knowledge from ontologies by using an inductive/deductive approach is presented. The new extracted knowledge takes the form of If-Then rules annotated with a weight. Such rules, termed Influence Rules, specify how the values of the properties bound to a collection of concepts may influence the values of the properties of another concept.The technique, that combines data mining and link analysis, is completely general and applicable to whatever domain. The paper reports the methods and the algorithms supporting the process of mining the rules out of the ontology, and discusses its application to real data from the economic field.Source: Software and Data Technologies. Revised selected papers, edited by María José Escalona, José Cordeiro, Boris Shishkov, pp. 270–285. Heidelberg: Springer, 2013
DOI: 10.1007/978-3-642-36177-7_17
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2011
Report
Unknown
DATA SIM - Semantic-enriched data-driven theory of mobility demand and final framework for integration (D2.2)
Giannotti F., Andrienko G., Andrienko N., Furletti B., Kertesz J., Liu F., Nanni M., Pappalardo L., Pelekis N., Renso C., Rinzivillo S.
This report presents the work pursued during the second year of the project by the partners participating in the Workpackage 2. The document propose an abstract simulation pipeline to discuss how the methods and patterns investigated in the WP may be integrated in a new generation of agent-based simulation systems.Source: Project report, DATA SIM, Deliverable D2.2, 2011
Project(s): DATA SIM
Giannotti F., Andrienko G., Andrienko N., Furletti B., Kertesz J., Liu F., Nanni M., Pappalardo L., Pelekis N., Renso C., Rinzivillo S.
This report presents the work pursued during the second year of the project by the partners participating in the Workpackage 2. The document propose an abstract simulation pipeline to discuss how the methods and patterns investigated in the WP may be integrated in a new generation of agent-based simulation systems.Source: Project report, DATA SIM, Deliverable D2.2, 2011
Project(s): DATA SIM
See at: CNR ExploRA
2015
Contribution to book
Restricted
Use of mobile phone data to estimate visitors mobility flows
Gabrielli L., Furletti B., Giannotti F., Nanni M., Rinzivillo S.
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.Source: Software Engineering and Formal Methods, edited by Carlos Canal, Akram Idani, pp. 214–226, 2015
DOI: 10.1007/978-3-319-15201-1_14
Project(s): DATA SIM
Metrics:
Gabrielli L., Furletti B., Giannotti F., Nanni M., Rinzivillo S.
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.Source: Software Engineering and Formal Methods, edited by Carlos Canal, Akram Idani, pp. 214–226, 2015
DOI: 10.1007/978-3-319-15201-1_14
Project(s): DATA SIM
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2015
Conference article
Restricted
City users' classification with mobile phone data
Furletti B., Gabrielli L., Trasarti R., Giannotti F., Pedreschi D.
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.Source: IEEE International Conference on Big Data, pp. 1007–1012, Santa Clara, CA, USA, 29/10/2015-01/11/2015
DOI: 10.1109/bigdata.2015.7363852
Project(s): PETRA
Metrics:
Furletti B., Gabrielli L., Trasarti R., Giannotti F., Pedreschi D.
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.Source: IEEE International Conference on Big Data, pp. 1007–1012, Santa Clara, CA, USA, 29/10/2015-01/11/2015
DOI: 10.1109/bigdata.2015.7363852
Project(s): PETRA
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
2008
Conference article
Restricted
Ontological Support For Association Rule Mining
Bellandi A., Furletti B., Grossi V., Romei A.
This paper describes some improvements of our previous work that realizes an integrated framework for extracting constraint based multilevel association rules with an ontology support. The ontology is not the repository of the data, but it models the application domain describing the metadata. Furthermore, it permits to focus the analysis only on a subset of data and to express multi level constraints on them. In this context, we report some theoretical notion already introduced and a detailed description of the recent improvements: the introduction of the object properties in the framework, and the implementation of an user interface.Source: 26th IASTED International Conference on Artificial Intelligence and Applications, 2008, pp. 110–115, Innsbruck, Austria, 11-13 February 2008
Bellandi A., Furletti B., Grossi V., Romei A.
This paper describes some improvements of our previous work that realizes an integrated framework for extracting constraint based multilevel association rules with an ontology support. The ontology is not the repository of the data, but it models the application domain describing the metadata. Furthermore, it permits to focus the analysis only on a subset of data and to express multi level constraints on them. In this context, we report some theoretical notion already introduced and a detailed description of the recent improvements: the introduction of the object properties in the framework, and the implementation of an user interface.Source: 26th IASTED International Conference on Artificial Intelligence and Applications, 2008, pp. 110–115, Innsbruck, Austria, 11-13 February 2008
See at:
dl.acm.org | CNR ExploRA
2007
Conference article
Unknown
Ontology-Driven Association Rule Extraction: A Case Study
Bellandi A., Furletti B., Grossi V., Romei A.
This paper proposes an integrated framework for extracting Constraint-based Multi-level Association Rules with an ontology support. The system permits the definition of a set of domain-specific constraints on a specific domain ontology, and to query the ontology for filtering the instances used in the association rule mining process. This method can improve the quality of the extracted associations rules in terms of relevance and understandability.Source: C&O:RR-2007 - International Workshop on Contexts and Ontologies: Representation and Reasoning, pp. 10–19, Roskilde University, Denmark, 21 August 2007
Project(s): MUSING
Bellandi A., Furletti B., Grossi V., Romei A.
This paper proposes an integrated framework for extracting Constraint-based Multi-level Association Rules with an ontology support. The system permits the definition of a set of domain-specific constraints on a specific domain ontology, and to query the ontology for filtering the instances used in the association rule mining process. This method can improve the quality of the extracted associations rules in terms of relevance and understandability.Source: C&O:RR-2007 - International Workshop on Contexts and Ontologies: Representation and Reasoning, pp. 10–19, Roskilde University, Denmark, 21 August 2007
Project(s): MUSING
See at: CNR ExploRA
2008
Conference article
Open Access
An extensible and interactive software agent for mobile devices based on GPS data
Furletti B., Fornasari F., Montanari C.
This paper presents a real-time software agent for monitoring and supporting users that move along predefined paths. We investigated appropriate models for geographical data and communications (based on cellular networks), also an ad-hoc localization algorithm based on GPS technologies has been implemented. We developed an Interactive Road System by installing the whole application on a mobile device and we customized it for supporting drivers. The power of the system is based on its flexibility and the possibility to extend the set of action, the agent has to perform.Source: IADIS International Conference on Applied Computing 2008, pp. 299–306, Algarve, Portugal, 10-13 April 2008
Furletti B., Fornasari F., Montanari C.
This paper presents a real-time software agent for monitoring and supporting users that move along predefined paths. We investigated appropriate models for geographical data and communications (based on cellular networks), also an ad-hoc localization algorithm based on GPS technologies has been implemented. We developed an Interactive Road System by installing the whole application on a mobile device and we customized it for supporting drivers. The power of the system is based on its flexibility and the possibility to extend the set of action, the agent has to perform.Source: IADIS International Conference on Applied Computing 2008, pp. 299–306, Algarve, Portugal, 10-13 April 2008
See at:
www.iadisportal.org 
| CNR ExploRA
2016
Journal article
Open Access
Big data research in Italy: a perspective
Bergamaschi S., Carlini E., Ceci M., Furletti B., Giannotti F., Malerba D., Mezzanzanica M., Monreale A., Pasi G., Pedreschi D., Perego R., Ruggieri S.
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.Source: Engineering (Beijing) 2 (2016): 163–170. doi:10.1016/J.ENG.2016.02.011
DOI: 10.1016/j.eng.2016.02.011
Metrics:
Bergamaschi S., Carlini E., Ceci M., Furletti B., Giannotti F., Malerba D., Mezzanzanica M., Monreale A., Pasi G., Pedreschi D., Perego R., Ruggieri S.
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.Source: Engineering (Beijing) 2 (2016): 163–170. doi:10.1016/J.ENG.2016.02.011
DOI: 10.1016/j.eng.2016.02.011
Metrics:
See at:
doi.org | ISTI Repository | Engineering | CNR ExploRA
2012
Journal article
Restricted
Knowledge discovery in ontologies
Furletti B., Turini F.
Ontologies allow us to represent knowledge and data in implicit and explicit ways. Implicit knowledge can be derived by means of several deductive logic-based processes. This paper introduces a new way for extracting implicit knowledge from ontologies by means of a sort of link analysis of the T-box of the ontology integrated with a data mining step on the A-box. The implicit extracted knowledge has the form of In uence Rules" i.e. rules structured as: if the property p1 of concept c1 has value v1, then the property p2 of concept c2 has value v2 with probability . The technique is completely general and applicable to whatever domain. The In uence Rules can be used to integrate existing knowledge or for supporting any other data mining process. A case study about an ontology describing intrusion detection is used to illustrate the result of the method.Source: Intelligent data analysis 16 (2012): 513–534. doi:10.3233/IDA-2012-0536
DOI: 10.3233/ida-2012-0536
Metrics:
Furletti B., Turini F.
Ontologies allow us to represent knowledge and data in implicit and explicit ways. Implicit knowledge can be derived by means of several deductive logic-based processes. This paper introduces a new way for extracting implicit knowledge from ontologies by means of a sort of link analysis of the T-box of the ontology integrated with a data mining step on the A-box. The implicit extracted knowledge has the form of In uence Rules" i.e. rules structured as: if the property p1 of concept c1 has value v1, then the property p2 of concept c2 has value v2 with probability . The technique is completely general and applicable to whatever domain. The In uence Rules can be used to integrate existing knowledge or for supporting any other data mining process. A case study about an ontology describing intrusion detection is used to illustrate the result of the method.Source: Intelligent data analysis 16 (2012): 513–534. doi:10.3233/IDA-2012-0536
DOI: 10.3233/ida-2012-0536
Metrics:
See at:
Intelligent Data Analysis | CNR ExploRA
2012
Conference article
Open Access
Identifying users profiles from mobile calls habits
Furletti B., Gabrielli L., Rinzivillo S., Renso C.
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.Source: ACM SIGKDD International Workshop on Urban Computing, pp. 17–24, Beijing, China, 12-16 August 2012
DOI: 10.1145/2346496.2346500
Project(s): DATA SIM
Metrics:
Furletti B., Gabrielli L., Rinzivillo S., Renso C.
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.Source: ACM SIGKDD International Workshop on Urban Computing, pp. 17–24, Beijing, China, 12-16 August 2012
DOI: 10.1145/2346496.2346500
Project(s): DATA SIM
Metrics:
See at:
www.cs.uic.edu | doi.org | CNR ExploRA
2013
Contribution to conference
Restricted
Pisa tourism fluxes observatory: deriving mobility indicators from GSM calls habits
Furletti B., Gabrielli L., Rinzivillo S., Renso C.
The necessity to improve the management of the resources, urged many local governments to adhere to European initiatives in the context of competitiveness and sustainability, for creating the right balance between the welfare of tourists, the needs of the natural and cultural environment and the development and competitiveness of destinations and businesses. For many Italian Municipalities, this requirements become concrete with the establishment of a tourism monitoring systems that aims at survey these phenomenon through the analysis of heterogeneous data ranging from information of the territory, energy consumption, use of the land, and linked data (arrival and departure from the airport, bus, hotels etc). We describe the permanent observatory of touristic fluxes we realized in the town of Pisa where the standard indicators have been extended with an indicator of people presence extracted from mobile GSM call data and other exploratory analyses made by using the mobile phone data.we developed a method to partition the users into residents, commuters, in transit and visitors starting from a spatio-temporal profile inferred from people call habits.Source: NetMob 2013 - Third International Conference on the Analysis of Mobile Phone Datasets, pp. 107–109, MIT Media Lab, Cambridge, MA, 1-3 Maggio 2013
Furletti B., Gabrielli L., Rinzivillo S., Renso C.
The necessity to improve the management of the resources, urged many local governments to adhere to European initiatives in the context of competitiveness and sustainability, for creating the right balance between the welfare of tourists, the needs of the natural and cultural environment and the development and competitiveness of destinations and businesses. For many Italian Municipalities, this requirements become concrete with the establishment of a tourism monitoring systems that aims at survey these phenomenon through the analysis of heterogeneous data ranging from information of the territory, energy consumption, use of the land, and linked data (arrival and departure from the airport, bus, hotels etc). We describe the permanent observatory of touristic fluxes we realized in the town of Pisa where the standard indicators have been extended with an indicator of people presence extracted from mobile GSM call data and other exploratory analyses made by using the mobile phone data.we developed a method to partition the users into residents, commuters, in transit and visitors starting from a spatio-temporal profile inferred from people call habits.Source: NetMob 2013 - Third International Conference on the Analysis of Mobile Phone Datasets, pp. 107–109, MIT Media Lab, Cambridge, MA, 1-3 Maggio 2013
See at:
perso.uclouvain.be | CNR ExploRA
2013
Conference article
Open Access
QoS-aware greening of interference-limited cellular networks
Rengarajan B., Rizzo G., Ajmone Marsan M., Furletti B.
We consider the problem of minimizing the energy consumed in a cellular access network, under loads that slowly vary over space and time, while guaranteeing quality of service (QoS). In particular, we formalize the problem of jointly optimizing the base stations (BS) power levels and the association of users to BSs, while guaranteeing a minimum throughput to each user, and a target value of blocking probability. We propose abstractions that enable tracking of long-term spatial load distributions, and a practical algorithm for energy efficient user association and base station power allocation. Our algorithm is applicable to arbitrary (planar) BS layouts, to settings with interference, to different BS energy models, and to arbitrary user distributions over the service area. Through extensive simulations using measured data, and realistic BS deployments, we show that our algorithm leads to substantial energy savings both with traditional BS designs and with energy-proportional equipment, and we demonstrate the potential of BS sleep modes to achieve network-level energy proportionality.Source: WoWMoM 2013 - IEEE 14th International Symposium and Workshops on a World of Wireless, Mobile and Multimedia Networks, pp. 1–9, Spain, 4-7 June 2013
DOI: 10.1109/wowmom.2013.6583396
Metrics:
Rengarajan B., Rizzo G., Ajmone Marsan M., Furletti B.
We consider the problem of minimizing the energy consumed in a cellular access network, under loads that slowly vary over space and time, while guaranteeing quality of service (QoS). In particular, we formalize the problem of jointly optimizing the base stations (BS) power levels and the association of users to BSs, while guaranteeing a minimum throughput to each user, and a target value of blocking probability. We propose abstractions that enable tracking of long-term spatial load distributions, and a practical algorithm for energy efficient user association and base station power allocation. Our algorithm is applicable to arbitrary (planar) BS layouts, to settings with interference, to different BS energy models, and to arbitrary user distributions over the service area. Through extensive simulations using measured data, and realistic BS deployments, we show that our algorithm leads to substantial energy savings both with traditional BS designs and with energy-proportional equipment, and we demonstrate the potential of BS sleep modes to achieve network-level energy proportionality.Source: WoWMoM 2013 - IEEE 14th International Symposium and Workshops on a World of Wireless, Mobile and Multimedia Networks, pp. 1–9, Spain, 4-7 June 2013
DOI: 10.1109/wowmom.2013.6583396
Metrics:
See at:
Recolector de Ciencia Abierta, RECOLECTA | doi.org | CNR ExploRA
2014
Contribution to book
Restricted
Transportation planning based on GSM traces: a case study on Ivory Coast
Nanni M., Trasarti R., Furletti B., Gabrielli L., Van Der Mede P., De Brujin J., De Romph E., Bruil G.
In this work we present an analysis process that exploits mobile phone transaction (trajectory) data to infer a transport demand model for the territory under monitoring. In particular, long-term analysis of individual call traces are performed to reconstruct systematic movements, and to infer an origin-destination matrix. We will show a case study on Ivory Coast, with emphasis on its major urbanization Abidjan. The case study includes the exploitation of the inferred mobility demand model in the construction of a transport model that projects the demand onto the transportation network (obtained from open data), and thus allows an understanding of current and future infrastructure requirements of the country.Source: Citizen in Sensor Networks, edited by Jordi Nin, Daniel Villatoro, pp. 15–25, 2014
DOI: 10.1007/978-3-319-04178-0_2
Metrics:
Nanni M., Trasarti R., Furletti B., Gabrielli L., Van Der Mede P., De Brujin J., De Romph E., Bruil G.
In this work we present an analysis process that exploits mobile phone transaction (trajectory) data to infer a transport demand model for the territory under monitoring. In particular, long-term analysis of individual call traces are performed to reconstruct systematic movements, and to infer an origin-destination matrix. We will show a case study on Ivory Coast, with emphasis on its major urbanization Abidjan. The case study includes the exploitation of the inferred mobility demand model in the construction of a transport model that projects the demand onto the transportation network (obtained from open data), and thus allows an understanding of current and future infrastructure requirements of the country.Source: Citizen in Sensor Networks, edited by Jordi Nin, Daniel Villatoro, pp. 15–25, 2014
DOI: 10.1007/978-3-319-04178-0_2
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2013
Conference article
Restricted
Analysis of GSM calls data for understanding user mobility behavior
Furletti B., Gabrielli L., Renso C., Rinzivillo S.
This information about our GSM calls is stored by the TelCo operator in large volumes and with strict privacy constraints making it challenging the analysis of these fingerprints for inferring mobility behavior. This paper proposes a strategy for mobility behavior identification based on aggregated calling profiles of mobile phone users. This compact representation of the user call profiles is the input of the mining algorithm for automatically classifying various kinds of mobility behavior. A further advantage of having defined the call profiles is that the analysis phase is based on summarized privacy-preserving representation of the original data. We show how these call profiles permit to design a two step process - implemented into a system - based on a bootstrap phase and a running phase for classifying users into behavior categories. We evaluated the system in two case studies where individuals are classified into residents, commuters and visitors. We conclude the paper with a discussion which emphasizes the role of the call profiles for the design of a new collaboration model between data provider and data analyst.Source: Big Data 2013 - 2013 IEEE International Conference on Big Data, pp. 550–555, Santa Clara Marriott, CA, USA, 6-9 October 2013
DOI: 10.1109/bigdata.2013.6691621
Project(s): DATA SIM
Metrics:
Furletti B., Gabrielli L., Renso C., Rinzivillo S.
This information about our GSM calls is stored by the TelCo operator in large volumes and with strict privacy constraints making it challenging the analysis of these fingerprints for inferring mobility behavior. This paper proposes a strategy for mobility behavior identification based on aggregated calling profiles of mobile phone users. This compact representation of the user call profiles is the input of the mining algorithm for automatically classifying various kinds of mobility behavior. A further advantage of having defined the call profiles is that the analysis phase is based on summarized privacy-preserving representation of the original data. We show how these call profiles permit to design a two step process - implemented into a system - based on a bootstrap phase and a running phase for classifying users into behavior categories. We evaluated the system in two case studies where individuals are classified into residents, commuters and visitors. We conclude the paper with a discussion which emphasizes the role of the call profiles for the design of a new collaboration model between data provider and data analyst.Source: Big Data 2013 - 2013 IEEE International Conference on Big Data, pp. 550–555, Santa Clara Marriott, CA, USA, 6-9 October 2013
DOI: 10.1109/bigdata.2013.6691621
Project(s): DATA SIM
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
2014
Journal article
Open Access
Discovering urban and country dynamics from mobile phone data with spatial correlation patterns
Trasarti R., Olteanu-Raimond A., Nanni M., Couronné T., Furletti B., Giannotti F., Smoreda Z., Ziemlicki C.
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 FranceSource: Telecommunications policy (2014). doi:10.1016/j.telpol.2013.12.002
DOI: 10.1016/j.telpol.2013.12.002
Metrics:
Trasarti R., Olteanu-Raimond A., Nanni M., Couronné T., Furletti B., Giannotti F., Smoreda Z., Ziemlicki C.
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 FranceSource: Telecommunications policy (2014). doi:10.1016/j.telpol.2013.12.002
DOI: 10.1016/j.telpol.2013.12.002
Metrics:
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