2010
Software
Unknown
M-Atlas - Atlas of the Urban Mobility
Trasarti R., Pinelli F., Rinzivillo S.
A software tool for analysis mobility data.
Trasarti R., Pinelli F., Rinzivillo S.
A software tool for analysis mobility data.
See at: CNR ExploRA
2013
Contribution to book
Unknown
Understanding human mobility using mobility data mining
Renso C., Trasarti R.
Source: Mobility Data - Modeling, Management, and Understanding, edited by Chiara Renso, Stefano Spaccapietra, Esteban Zimányi, pp. 127–147, 2013
Renso C., Trasarti R.
Source: Mobility Data - Modeling, Management, and Understanding, edited by Chiara Renso, Stefano Spaccapietra, Esteban Zimányi, pp. 127–147, 2013
See at: CNR ExploRA
2014
Contribution to book
Restricted
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
2016
Report
Open Access
SoBigData - Data processing workflow specification language
Candela L., Giannotti F., Grossi V., Manghi P., Trasarti R.
This document contains a general overview of the workflow language definition status.Source: Project report, SoBigData, Deliverable D10.11, 2016
Project(s): SoBigData
Candela L., Giannotti F., Grossi V., Manghi P., Trasarti R.
This document contains a general overview of the workflow language definition status.Source: Project report, SoBigData, Deliverable D10.11, 2016
Project(s): SoBigData
See at:
ISTI Repository 
| CNR ExploRA
2018
Contribution to journal
Open Access
Guest editorial special issue on knowledge discovery from mobility data for intelligent transportation systems
Moreira-Matias L., Gama J., Olaverri Monreal C., Nair R., Trasarti R.
Source: IEEE transactions on intelligent transportation systems (Print) 19 (2018): 3626–3629. doi:10.1109/TITS.2018.2877063
DOI: 10.1109/tits.2018.2877063
Metrics:
Moreira-Matias L., Gama J., Olaverri Monreal C., Nair R., Trasarti R.
Source: IEEE transactions on intelligent transportation systems (Print) 19 (2018): 3626–3629. doi:10.1109/TITS.2018.2877063
DOI: 10.1109/tits.2018.2877063
Metrics:
See at:
ieeexplore.ieee.org | IEEE Transactions on Intelligent Transportation Systems | ISTI Repository | IEEE Transactions on Intelligent Transportation Systems | 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
2016
Report
Unknown
PETRA - The simulation framework for crowd mobility behaviour
Nanni M., Trasarti R., Romano V.
This document presents the tools and framework developed within the PETRA project for simulating the mobility behaviour of crowds. The tools are mainly based on the modeling of individual users - possibly derived from real data - and allow to realize various kinds of simulations, from simple predictions over current traffic/crowd status to more involved what-if analyses. This document provides some preliminaries and the rationale of the methods, highlighting their usability over the PETRA showcases.Source: Project report, PETRA, Deliverable D3.5, 2016
Project(s): PETRA
Nanni M., Trasarti R., Romano V.
This document presents the tools and framework developed within the PETRA project for simulating the mobility behaviour of crowds. The tools are mainly based on the modeling of individual users - possibly derived from real data - and allow to realize various kinds of simulations, from simple predictions over current traffic/crowd status to more involved what-if analyses. This document provides some preliminaries and the rationale of the methods, highlighting their usability over the PETRA showcases.Source: Project report, PETRA, Deliverable D3.5, 2016
Project(s): PETRA
See at: CNR ExploRA
2016
Report
Unknown
PETRA - Methods for computing collective mobility indicator from individual patterns
Nanni M., Trasarti R., Romano V.
This document presents a set of methods for exploiting individual patterns and measures developed within WP3, and described in D3.3, to produce collective indicators. Such indicators will be used for various applications purposes, some of which are described as representative examples. This document provides preliminaries and the rationale of the methods, highlighting how they are used (or can be used) for the showcases of PETRA.Source: Project report, PETRA, Deliverable D3.6, 2016
Project(s): PETRA
Nanni M., Trasarti R., Romano V.
This document presents a set of methods for exploiting individual patterns and measures developed within WP3, and described in D3.3, to produce collective indicators. Such indicators will be used for various applications purposes, some of which are described as representative examples. This document provides preliminaries and the rationale of the methods, highlighting how they are used (or can be used) for the showcases of PETRA.Source: Project report, PETRA, Deliverable D3.6, 2016
Project(s): PETRA
See at: CNR ExploRA
2016
Report
Unknown
PETRA - Transit monitor for detecting changes in predictions
Nanni M., Trasarti R., Romano V.
This document presents the algorithms developed within PETRA to monitor the performances of the main predictive models built in the project, in order to provide feedbacks and possibly trigger updates or re-computation of the models. The algorithms are based on the comparison of the models against GPS mobility data. The document briefly summarizes the interfaces and the functionalities provided.Source: Project report, PETRA, Deliverable D5.4, 2016
Project(s): PETRA
Nanni M., Trasarti R., Romano V.
This document presents the algorithms developed within PETRA to monitor the performances of the main predictive models built in the project, in order to provide feedbacks and possibly trigger updates or re-computation of the models. The algorithms are based on the comparison of the models against GPS mobility data. The document briefly summarizes the interfaces and the functionalities provided.Source: Project report, PETRA, Deliverable D5.4, 2016
Project(s): PETRA
See at: CNR ExploRA
2016
Report
Unknown
ASAP - Telecommunication Data Analytics (TDA) specification and early prototype
Bertoldi R., Cintia P., Trasarti R.
The main objective of this Work Package (WP) is the design and development of an analytics application on WIND Telecommunications customer data, targeted towards tourism and mobility scenarios. The envisaged use cases will be integrated into the ASAP framework and will be evaluated using several measurement methods. At the end of the project's second year (M24) the tasks involved are three: the end of the task T9.2, the task T9.3 and the beginning of task T9.4.Source: Project report, ASAP, Deliverable D9.3, 2016
Project(s): ASAP
Bertoldi R., Cintia P., Trasarti R.
The main objective of this Work Package (WP) is the design and development of an analytics application on WIND Telecommunications customer data, targeted towards tourism and mobility scenarios. The envisaged use cases will be integrated into the ASAP framework and will be evaluated using several measurement methods. At the end of the project's second year (M24) the tasks involved are three: the end of the task T9.2, the task T9.3 and the beginning of task T9.4.Source: Project report, ASAP, Deliverable D9.3, 2016
Project(s): ASAP
See at: CNR ExploRA
2017
Contribution to book
Closed Access
Movement behaviour recognition for water activities
Nanni M., Trasarti R., Giannotti F.
This work describes an analysis process for the movement traces of users during water activities. The data is collected by a mobile phone app that the Navionics company developed to provide to its users sea maps and navigation services. The final objective of the project is to recognize the prevalent activity types of the users (fishing, sailing, cruising, canoeing), in order to personalize services and advertising.Source: Personal Analytics and Privacy. An Individual and Collective Perspective, edited by Guidati R.; Monreale A.; Pedreschi D.; Abiteboul S., pp. 64–75, 2017
DOI: 10.1007/978-3-319-71970-2_7
Metrics:
Nanni M., Trasarti R., Giannotti F.
This work describes an analysis process for the movement traces of users during water activities. The data is collected by a mobile phone app that the Navionics company developed to provide to its users sea maps and navigation services. The final objective of the project is to recognize the prevalent activity types of the users (fishing, sailing, cruising, canoeing), in order to personalize services and advertising.Source: Personal Analytics and Privacy. An Individual and Collective Perspective, edited by Guidati R.; Monreale A.; Pedreschi D.; Abiteboul S., pp. 64–75, 2017
DOI: 10.1007/978-3-319-71970-2_7
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2011
Report
Open Access
Types and operators in M-Atlas system
Trasarti Roberto, Rinzivillo Salvatore, Nanni Mirco, Giannotti Fosca
In this technical report we illustrate the types managed by the M-Atlas system and the operators defined among them. Due to the complexity of spatio-temporal data, models and patterns, the system is be based on a rich formalism, capable to representing the specificity of movement data. We choose the object-relational model, which combines the simplicity of the relational data model and SQL with the basic object oriented capabilities. The main feature of the object- relational database model is that objects and classes are directly supported in database schemas supporting the extension of the original types with custom types representing complex structures. Moreover using a pre-existing GIS technology developed on the database the new types and operators can be integrated easily.Source: ISTI Technical reports, 2011
Trasarti Roberto, Rinzivillo Salvatore, Nanni Mirco, Giannotti Fosca
In this technical report we illustrate the types managed by the M-Atlas system and the operators defined among them. Due to the complexity of spatio-temporal data, models and patterns, the system is be based on a rich formalism, capable to representing the specificity of movement data. We choose the object-relational model, which combines the simplicity of the relational data model and SQL with the basic object oriented capabilities. The main feature of the object- relational database model is that objects and classes are directly supported in database schemas supporting the extension of the original types with custom types representing complex structures. Moreover using a pre-existing GIS technology developed on the database the new types and operators can be integrated easily.Source: ISTI Technical reports, 2011
See at:
ISTI Repository | CNR ExploRA
2009
Conference article
Restricted
Mining Mobility Behavior from Trajectory Data
Fosca Giannotti And Mirco Nanni And Dino Pedreschi And Chiara Renso And Roberto Trasarti
The technologies of mobile communications and ubiquitous computing pervade our society, and wireless networks sense the movement of people and vehicles, generating large volumes of mobility data: location data from mobile phones, GPS tracks from mobile devices receiving geo-positions from satellites, etc. The GeoPKDD project, a large European research initiative, has studied how to discover useful knowledge about human movement behavior from mobility data, while preserving the privacy of the people under observation. A new exciting multidisciplinary research area has thus started, at the crossroads of mobility, data mining, and privacy.Source: Proceedings IEEE CSE'09, 12th IEEE International Conference on Computational Science and Engineering, pp. 948–951, Vancouver, Canada, 29-31 August 2009
DOI: 10.1109/cse.2009.542
Metrics:
Fosca Giannotti And Mirco Nanni And Dino Pedreschi And Chiara Renso And Roberto Trasarti
The technologies of mobile communications and ubiquitous computing pervade our society, and wireless networks sense the movement of people and vehicles, generating large volumes of mobility data: location data from mobile phones, GPS tracks from mobile devices receiving geo-positions from satellites, etc. The GeoPKDD project, a large European research initiative, has studied how to discover useful knowledge about human movement behavior from mobility data, while preserving the privacy of the people under observation. A new exciting multidisciplinary research area has thus started, at the crossroads of mobility, data mining, and privacy.Source: Proceedings IEEE CSE'09, 12th IEEE International Conference on Computational Science and Engineering, pp. 948–951, Vancouver, Canada, 29-31 August 2009
DOI: 10.1109/cse.2009.542
Metrics:
See at:
doi.org | CNR ExploRA
2006
Conference article
Unknown
ConQueSt: a constraint-based querying system for exploratory pattern discovery
Bonchi F., Giannotti F., Lucchese C., Orlando S., Perego R., Trasarti R.
ConQueSt is a constraint-based querying system devised with the aim of supporting the intrinsically exploratory nature of pattern discovery. It provides users with an expressive constraint-based query language which allows the discovery process to be effectively driven toward potentially interesting patterns. Constraints are also exploited to reduce the cost of pattern mining. The system is built around an efficient constraint-based mining engine which entails several data and search space reduction techniques, and allows new user-defined constraints to be easily added.Source: International Conference on Data Engineering (ICDE'06), pp. 159–160, Atlanta, USA, 03-07/04/2006
Bonchi F., Giannotti F., Lucchese C., Orlando S., Perego R., Trasarti R.
ConQueSt is a constraint-based querying system devised with the aim of supporting the intrinsically exploratory nature of pattern discovery. It provides users with an expressive constraint-based query language which allows the discovery process to be effectively driven toward potentially interesting patterns. Constraints are also exploited to reduce the cost of pattern mining. The system is built around an efficient constraint-based mining engine which entails several data and search space reduction techniques, and allows new user-defined constraints to be easily added.Source: International Conference on Data Engineering (ICDE'06), pp. 159–160, Atlanta, USA, 03-07/04/2006
See at: CNR ExploRA
2009
Conference article
Restricted
K-BestMatch reconstruction and comparison of trajectory data
Nanni M., Trasarti R.
In this paper we propose a map matching method to overcoming the limitations of standard best-match recon- struction strategies. We use a more flexible approach which consider the k-optimal alternative paths to reconstruct the trajectories from the GPS raw data. The preliminary results, obtained on a real dataset of car users in Milan area, suggest that our method leads to beneficial effects on the successive analysis to be performed such as KNN and clusteringSource: International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM-09) In Cooperation with IEEE ICDM 2009, pp. 610, Miami, Florida, USA, 6 December 2009
DOI: 10.1109/icdmw.2009.62
Metrics:
Nanni M., Trasarti R.
In this paper we propose a map matching method to overcoming the limitations of standard best-match recon- struction strategies. We use a more flexible approach which consider the k-optimal alternative paths to reconstruct the trajectories from the GPS raw data. The preliminary results, obtained on a real dataset of car users in Milan area, suggest that our method leads to beneficial effects on the successive analysis to be performed such as KNN and clusteringSource: International Workshop on Spatial and Spatiotemporal Data Mining (SSTDM-09) In Cooperation with IEEE ICDM 2009, pp. 610, Miami, Florida, USA, 6 December 2009
DOI: 10.1109/icdmw.2009.62
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
2009
Conference article
Restricted
Mining social mobility behaviors from GPS data
Trasarti R., Nanni M., Renso C., Giannotti F., Pedreschi D.
The technologies of mobile communications and ubiquitous computing pervade our society, and wireless networks sense the movement of people and vehicles, generating large volumes of mobility data: location data from mobile phones, GPS tracks from mobile devices receiving geo-positions from satellites, etc. The GeoPKDD project, a large European research initiative, has studied how to discover useful knowledge about human movement behavior from mobility data, while preserving the privacy of the people under observation. A new exciting multi- disciplinary research area has thus started, at the crossroads of mobility, data mining, and privacy.Source: Workshop on Social Computing with Mobile Phones & Sensors: Modeling, Sensing and Sharing, Vancouver, Canada, 29-31 August 2009
Trasarti R., Nanni M., Renso C., Giannotti F., Pedreschi D.
The technologies of mobile communications and ubiquitous computing pervade our society, and wireless networks sense the movement of people and vehicles, generating large volumes of mobility data: location data from mobile phones, GPS tracks from mobile devices receiving geo-positions from satellites, etc. The GeoPKDD project, a large European research initiative, has studied how to discover useful knowledge about human movement behavior from mobility data, while preserving the privacy of the people under observation. A new exciting multi- disciplinary research area has thus started, at the crossroads of mobility, data mining, and privacy.Source: Workshop on Social Computing with Mobile Phones & Sensors: Modeling, Sensing and Sharing, Vancouver, Canada, 29-31 August 2009
See at:
hd.media.mit.edu | CNR ExploRA
2009
Conference article
Restricted
DAMSEL: a system for progressive querying and reasoning on movement data
Trasarti R., Baglioni M., Renso C.
In this paper we present DAMSEL a querying and reasoning system to support the knowledge discovery process over movement data. DAMSEL is the integration of a data mining query language system, called Daedalus, with a reasoning system, called Athena. The synergic integration of the two produces an advanced flexible querying system, specialized on movement data. The query language of the integrated system allows for mixing data mining and semantic enhanced queries. Furthermore, the support for the iterative knowledge discovery process provides the user with a powerful reasoning tool. The architecture of DAMSEL and its query language is outlined in the paper along with an application scenario that expresses the advanced querying capability of the system.Source: 20th International Workshop on Database and Expert Systems Application, pp. 452–456, Linz, Austria, 31 August - 4 September 2009
DOI: 10.1109/dexa.2009.27
Metrics:
Trasarti R., Baglioni M., Renso C.
In this paper we present DAMSEL a querying and reasoning system to support the knowledge discovery process over movement data. DAMSEL is the integration of a data mining query language system, called Daedalus, with a reasoning system, called Athena. The synergic integration of the two produces an advanced flexible querying system, specialized on movement data. The query language of the integrated system allows for mixing data mining and semantic enhanced queries. Furthermore, the support for the iterative knowledge discovery process provides the user with a powerful reasoning tool. The architecture of DAMSEL and its query language is outlined in the paper along with an application scenario that expresses the advanced querying capability of the system.Source: 20th International Workshop on Database and Expert Systems Application, pp. 452–456, Linz, Austria, 31 August - 4 September 2009
DOI: 10.1109/dexa.2009.27
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
2009
Conference article
Restricted
Towards semantic interpretation of movement behavior
Baglioni M., Macedo J., Renso C., Trasarti R., Wachowicz M.
In this paper we aim at providing a model for the conceptual representation and deductive reasoning of trajectory patterns obtained from mining raw trajectories. This has been achieved by means of a semantic enrichment process, where raw trajectories are enhanced with semantic information and integrated with geographical knowledge encoded in an ontology. The reasoning mechanisms provided by the chosen ontology formalism are exploited to accomplish a further semantic enrichment step that gives a possible interpretation of discovered patterns in terms of movement behaviour. A sketch of the realised system, called Athena, is given, along with some examples to demonstrate the feasibility of the approach.Source: Advances in GIScience. 12th AGILE Conference, pp. 271–288, Hannover, germania, 3 giugno 2009
DOI: 10.1007/978-3-642-00318-9_14
Metrics:
Baglioni M., Macedo J., Renso C., Trasarti R., Wachowicz M.
In this paper we aim at providing a model for the conceptual representation and deductive reasoning of trajectory patterns obtained from mining raw trajectories. This has been achieved by means of a semantic enrichment process, where raw trajectories are enhanced with semantic information and integrated with geographical knowledge encoded in an ontology. The reasoning mechanisms provided by the chosen ontology formalism are exploited to accomplish a further semantic enrichment step that gives a possible interpretation of discovered patterns in terms of movement behaviour. A sketch of the realised system, called Athena, is given, along with some examples to demonstrate the feasibility of the approach.Source: Advances in GIScience. 12th AGILE Conference, pp. 271–288, Hannover, germania, 3 giugno 2009
DOI: 10.1007/978-3-642-00318-9_14
Metrics:
See at:
NARCIS | link.springer.com | NARCIS | CNR ExploRA
2010
Conference article
Unknown
Querying and mining trajectories with gaps: a multi-path reconstruction approach (Extended Abstract)
Nanni M., Trasarti R.
In this paper we propose a map matching method to overcoming the limitations of standard best-match reconstruction strategies. We use a more flex- ible approach which consider the k-optimal alternative paths to reconstruct the trajectories from the GPS raw data. The preliminary results, obtained on a real dataset of car users in Milan area, suggest that our method leads to beneficial effects on the successive analysis to be performed such as KNN and clustering.Source: 18th Italian Symposium on Advanced Database Systems, pp. 126–133, Rimini, Italy, 20-23 June 2010
Nanni M., Trasarti R.
In this paper we propose a map matching method to overcoming the limitations of standard best-match reconstruction strategies. We use a more flex- ible approach which consider the k-optimal alternative paths to reconstruct the trajectories from the GPS raw data. The preliminary results, obtained on a real dataset of car users in Milan area, suggest that our method leads to beneficial effects on the successive analysis to be performed such as KNN and clustering.Source: 18th Italian Symposium on Advanced Database Systems, pp. 126–133, Rimini, Italy, 20-23 June 2010
See at: CNR ExploRA