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.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.DOI: 10.1109/wowmom.2013.6583396
Metrics:
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Recolector de Ciencia Abierta, RECOLECTA 
| doi.org 
| CNR IRIS | CNR IRIS
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.
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.
See at:
CNR IRIS | www.iadisportal.org | CNR IRIS
2011
Conference article
Restricted
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.
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.
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, vol. 16 (issue 3), pp. 513-534
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, vol. 16 (issue 3), pp. 513-534
DOI: 10.3233/ida-2012-0536
Metrics:
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Intelligent Data Analysis | CNR IRIS | CNR IRIS
2013
Contribution to book
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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: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE (PRINT), pp. 270-285
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: COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE (PRINT), pp. 270-285
DOI: 10.1007/978-3-642-36177-7_17
Metrics:
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doi.org | CNR IRIS | CNR IRIS | link.springer.com
2010
Journal article
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Improving the business plan evaluation process: the role of intangibles
Turini F, Baglioni M, Bellandi A, Furletti B, Pratesi C
One of the main objectives of the European MUSING project is to design and test software tools in order to support the activities of small and medium sized businesses. In this paper we examine financial risk management and, more specifically, the self-assessment of business plans. The role of intangible assets is discussed, and we report on how intangible assets can be collected, how they can be represented, taking into account their semantic relationships, and how they can be used to build an analytical tool for business plans. The basic technology embedded in the tool is the construction of classification trees, a well-known technique in inductive learning. We show how using knowledge of intangible assets can improve the construction of the classifier, as proved by the testing carried out so far.Source: QUALITY TECHNOLOGY & QUANTITATIVE MANAGEMENT, vol. 7 (issue 1), pp. 35-50
DOI: 10.1080/16843703.2010.11673217
Metrics:
Turini F, Baglioni M, Bellandi A, Furletti B, Pratesi C
One of the main objectives of the European MUSING project is to design and test software tools in order to support the activities of small and medium sized businesses. In this paper we examine financial risk management and, more specifically, the self-assessment of business plans. The role of intangible assets is discussed, and we report on how intangible assets can be collected, how they can be represented, taking into account their semantic relationships, and how they can be used to build an analytical tool for business plans. The basic technology embedded in the tool is the construction of classification trees, a well-known technique in inductive learning. We show how using knowledge of intangible assets can improve the construction of the classifier, as proved by the testing carried out so far.Source: QUALITY TECHNOLOGY & QUANTITATIVE MANAGEMENT, vol. 7 (issue 1), pp. 35-50
DOI: 10.1080/16843703.2010.11673217
Metrics:
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Quality Technology & Quantitative Management | CNR IRIS | CNR IRIS | www.tandfonline.com
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.
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.
See at:
dl.acm.org | CNR IRIS | CNR IRIS
2007
Conference article
Restricted
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.Project(s): MUlti-Industry, Semantic-based Next Generation Business INtelliGence
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.Project(s): MUlti-Industry, Semantic-based Next Generation Business INtelliGence
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.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.DOI: 10.1145/2346496.2346500
Project(s): DATA SIM
Metrics:
See at:
www.cs.uic.edu | doi.org | CNR IRIS | CNR IRIS
2013
Conference article
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.
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.
See at:
CNR IRIS | CNR IRIS | perso.uclouvain.be
2013
Conference article
Restricted
Inferring human activities from GPS tracks
Furletti B, Cintia P, Renso C, Spinsanti L
The collection of huge amount of tracking data made possi- bile by the widespread use of GPS devices, enabled the anal- ysis of such data for several applications domains, ranging from traffic management to advertisement and social stud- ies. However, the raw positioning data, as it is detected by GPS devices, lacks of semantic information since these data do not natively provide any additional contextual in- formation like the places that people visited or the activities performed. Traditionally, this information is collected by hand filled questionnaire where a limited number of users are asked to annotate their tracks whith the activities they have done. With the purpose of getting large amount of semantically rich trajectories, we propose an algorithm for automatically annotating raw trajectories with the activi- ties performed by the users. To do this, we analyse the stops points trying to infer the Point Of Interest (POI) the user has visited. Based on the category of the POI and a probability law, we infer the activity performed. We exper- imented and evaluated the method in a real case study of car trajectories, manually annotated by users with their ac- tivities. We exploit the Gravity law and the nearby POIs for inferring the most probable activity performed by a user during a stop. Experimental results are encouraging and will drive our future works.DOI: 10.1145/2505821.2505830
Project(s): DATA SIM
Metrics:
Furletti B, Cintia P, Renso C, Spinsanti L
The collection of huge amount of tracking data made possi- bile by the widespread use of GPS devices, enabled the anal- ysis of such data for several applications domains, ranging from traffic management to advertisement and social stud- ies. However, the raw positioning data, as it is detected by GPS devices, lacks of semantic information since these data do not natively provide any additional contextual in- formation like the places that people visited or the activities performed. Traditionally, this information is collected by hand filled questionnaire where a limited number of users are asked to annotate their tracks whith the activities they have done. With the purpose of getting large amount of semantically rich trajectories, we propose an algorithm for automatically annotating raw trajectories with the activi- ties performed by the users. To do this, we analyse the stops points trying to infer the Point Of Interest (POI) the user has visited. Based on the category of the POI and a probability law, we infer the activity performed. We exper- imented and evaluated the method in a real case study of car trajectories, manually annotated by users with their ac- tivities. We exploit the Gravity law and the nearby POIs for inferring the most probable activity performed by a user during a stop. Experimental results are encouraging and will drive our future works.DOI: 10.1145/2505821.2505830
Project(s): DATA SIM
Metrics:
See at:
dl.acm.org | doi.org | CNR IRIS | CNR IRIS
2014
Contribution to book
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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.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.DOI: 10.1007/978-3-319-04178-0_2
Metrics:
See at:
doi.org | CNR IRIS | CNR IRIS | link.springer.com
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.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.DOI: 10.1109/bigdata.2013.6691621
Project(s): DATA SIM
Metrics:
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doi.org | CNR IRIS | ieeexplore.ieee.org | CNR IRIS
2013
Conference article
Restricted
MP4A project: mobility planning for Africa
Nanni M, Trasarti R, Furletti B, Gabrielli L, Van Der Mede P, De Bruijn J, De Romph E, Bruil G
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.
Nanni M, Trasarti R, Furletti B, Gabrielli L, Van Der Mede P, De Bruijn J, De Romph E, Bruil G
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.
See at:
CNR IRIS | CNR IRIS | perso.uclouvain.be
2013
Conference article
Restricted
Discovering urban and country dynamics from mobile phone data with spatial correlation patterns
Trasarti R, Olteanuraimond A, Nanni M, Couronné T, Furletti B, Giannotti F, Smoreda Z, Ziemlicki C
Trasarti R, Olteanuraimond A, Nanni M, Couronné T, Furletti B, Giannotti F, Smoreda Z, Ziemlicki C
See at:
CNR IRIS | CNR IRIS | perso.uclouvain.be
2014
Journal article
Open Access
Discovering urban and country dynamics from mobile phone data with spatial correlation patterns
Trasarti R, Olteanuraimond 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
DOI: 10.1016/j.telpol.2013.12.002
Metrics:
Trasarti R, Olteanuraimond 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
DOI: 10.1016/j.telpol.2013.12.002
Metrics:
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CNR IRIS | ISTI Repository | www.sciencedirect.com | Telecommunications Policy | Hyper Article en Ligne | CNR IRIS | CNR IRIS
2014
Conference article
Restricted
Use of mobile phone data to estimate mobility flows. Measuring urban population and inter-city mobility using big data in an integrated approach
Furletti B, Gabrielli L, Giannotti F, Milli L, Nanni M, Pedreschi D, Vivio R, Garofalo G
The Big Data, originating from the digital breadcrumbs of human activities, sensed as a by-product of the technologies that we use for our daily ctivities, let us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data "proxies", as the mobile calls data for mobility. In this paper we investigate to what extent such "big data",in integration with administrative ones, could be a support in producing reliable and timely estimates of inter-city mobility. The study has been jointly developed by Istat, CNR, University of Pisa in the range of interest of the "Commssione di studio avente il compito di orientare le scelte dellIstat sul tema dei Big Data ". In an ongoing project at ISTAT, called "Persons and Places" - based on an integration of administrative data sources, it has been produced a first release of Origin Destination matrix - at municipality level - assuming that the places of residence and that of work (or study) be the terminal points of usual individual mobility for work or study. The coincidence between the city of residence and that of work (or study) - is considered as a proxy of the absence of intercity mobility for a person (we define him a static resident). The opposite case is considered as a proxy of presence of mobility (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).
Furletti B, Gabrielli L, Giannotti F, Milli L, Nanni M, Pedreschi D, Vivio R, Garofalo G
The Big Data, originating from the digital breadcrumbs of human activities, sensed as a by-product of the technologies that we use for our daily ctivities, let us to observe the individual and collective behavior of people at an unprecedented detail. Many dimensions of our social life have big data "proxies", as the mobile calls data for mobility. In this paper we investigate to what extent such "big data",in integration with administrative ones, could be a support in producing reliable and timely estimates of inter-city mobility. The study has been jointly developed by Istat, CNR, University of Pisa in the range of interest of the "Commssione di studio avente il compito di orientare le scelte dellIstat sul tema dei Big Data ". In an ongoing project at ISTAT, called "Persons and Places" - based on an integration of administrative data sources, it has been produced a first release of Origin Destination matrix - at municipality level - assuming that the places of residence and that of work (or study) be the terminal points of usual individual mobility for work or study. The coincidence between the city of residence and that of work (or study) - is considered as a proxy of the absence of intercity mobility for a person (we define him a static resident). The opposite case is considered as a proxy of presence of mobility (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).
See at:
CNR IRIS | CNR IRIS | www.sis2014.it
2015
Conference article
Open Access
Detecting and understanding big events in big cities
Furletti B, Trasarti R, Gabrielli L, Smoreda Z, Vanhoof M, Ziemlicki C
Recent studies have shown the great potential of big data such as mobile phone location data to model human behavior. Big data allow to analyze people presence in a territory in a fast and effective way with respect to the classical surveys (diaries or questionnaires). One of the drawbacks of these collection systems is incompleteness of the users' traces; people are localized only when they are using their phones. In this work we define a data mining method for identifying people presence and understanding the impact of big events in big cities. We exploit the ability of the Sociometer for classifying mobile phone users in mobility categories through their presence profile. The experiment in cooperation with Orange Telecom has been conduced in Paris during the event Fete de la Musique using a privacy preserving protocol.
Furletti B, Trasarti R, Gabrielli L, Smoreda Z, Vanhoof M, Ziemlicki C
Recent studies have shown the great potential of big data such as mobile phone location data to model human behavior. Big data allow to analyze people presence in a territory in a fast and effective way with respect to the classical surveys (diaries or questionnaires). One of the drawbacks of these collection systems is incompleteness of the users' traces; people are localized only when they are using their phones. In this work we define a data mining method for identifying people presence and understanding the impact of big events in big cities. We exploit the ability of the Sociometer for classifying mobile phone users in mobility categories through their presence profile. The experiment in cooperation with Orange Telecom has been conduced in Paris during the event Fete de la Musique using a privacy preserving protocol.
See at:
CNR IRIS | www.netmob.org | CNR IRIS
2016
Conference article
Restricted
Big data and public administration: a case study for Tuscany airports
Furletti B, Nanni M, Fadda D, Piccini L, Lattarulo P
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.Project(s): PETRA
Furletti B, Nanni M, Fadda D, Piccini L, Lattarulo P
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.Project(s): PETRA
2017
Journal article
Open Access
Discovering and understanding city events with big data: the case of Rome
Furletti B, Trasarti R, Cintia P, Gabrielli L
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.Source: INFORMATION, vol. 8 (issue 3)
DOI: 10.3390/info8030074
Metrics:
Furletti B, Trasarti R, Cintia P, Gabrielli L
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.Source: INFORMATION, vol. 8 (issue 3)
DOI: 10.3390/info8030074
Metrics:
See at:
Information | CNR IRIS | ISTI Repository | www.mdpi.com | Information | CNR IRIS