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
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
2010
Conference article
Open Access
Exploring real mobility data with M-Atlas
Trasarti R., Rinzivillo S., Pinelli F., Nanni M., Monreale A., Renso C., Pedreschi D., Giannotti F.
Research on moving-object data analysis has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing loca- tion aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks. These have made available massive repositories of spatio-temporal data recording human mobile activities, that call for suitable analytical methods, capable of enabling the development of innovative, location-aware applica- tions [3]. The M-Atlas is the evolution of the system presented in [5] allows to handle the whole knowledge discovery process from mobility data. The analysis capabilities of M-Atlas system have been applied onto a massive real life GPS dataset, obtained from 17,000 vehicles with on-board GPS receivers under a specific car insurance contract, tracked during one week of ordinary mobile activity in the urban area of the city of Milan; the dataset contains more than 2 million observations leading to a set of more than 200,000 trajectories.Source: ECML PKDD 2010 - Machine Learning and Knowledge Discovery in Databases. European Conference, pp. 624–627, Barcelona, Spain, 20-24 September 2010
DOI: 10.1007/978-3-642-15939-8_48
Metrics:
Trasarti R., Rinzivillo S., Pinelli F., Nanni M., Monreale A., Renso C., Pedreschi D., Giannotti F.
Research on moving-object data analysis has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing loca- tion aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks. These have made available massive repositories of spatio-temporal data recording human mobile activities, that call for suitable analytical methods, capable of enabling the development of innovative, location-aware applica- tions [3]. The M-Atlas is the evolution of the system presented in [5] allows to handle the whole knowledge discovery process from mobility data. The analysis capabilities of M-Atlas system have been applied onto a massive real life GPS dataset, obtained from 17,000 vehicles with on-board GPS receivers under a specific car insurance contract, tracked during one week of ordinary mobile activity in the urban area of the city of Milan; the dataset contains more than 2 million observations leading to a set of more than 200,000 trajectories.Source: ECML PKDD 2010 - Machine Learning and Knowledge Discovery in Databases. European Conference, pp. 624–627, Barcelona, Spain, 20-24 September 2010
DOI: 10.1007/978-3-642-15939-8_48
Metrics:
See at:
link.springer.com 
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2010
Conference article
Unknown
Location prediction through trajectory pattern mining
Monreale A., Pinelli F., Trasarti R., Giannotti F.
The pervasiveness of mobile devices and location based services produces as side effects an increasing volume of mobility data which in turn create the opportunity for a novel generation of analysis methods of movements behaviors. In this paper, we propose a method WhereNext aimed at predicting with a certain accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Pattern which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends by the movement of all available objects in a certain area instead by the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties emerged from the data and this allows to define matching methods strongly depending on such movement properties. Finally an exhaustive set of experiments and results on the real dataset are presented.Source: 18th Italian Symposium on Advanced Database Systems, Rimini, Italy, 20-23 June 2010
Monreale A., Pinelli F., Trasarti R., Giannotti F.
The pervasiveness of mobile devices and location based services produces as side effects an increasing volume of mobility data which in turn create the opportunity for a novel generation of analysis methods of movements behaviors. In this paper, we propose a method WhereNext aimed at predicting with a certain accuracy the next location of a moving object. The prediction uses previously extracted movement patterns named Trajectory Pattern which are a concise representation of behaviors of moving objects as sequences of regions frequently visited with typical travel time. A decision tree, named T-pattern Tree, is built and evaluated with a formal training and test process. Using Trajectory Patterns as predictive rules has the following implications: (I) the learning depends by the movement of all available objects in a certain area instead by the individual history of an object; (II) the prediction tree intrinsically contains the spatio-temporal properties emerged from the data and this allows to define matching methods strongly depending on such movement properties. Finally an exhaustive set of experiments and results on the real dataset are presented.Source: 18th Italian Symposium on Advanced Database Systems, Rimini, Italy, 20-23 June 2010
See at: CNR ExploRA
2010
Conference article
Open Access
Preserving privacy in semantic-rich trajectories of human mobility
Monreale A., Trasarti R., Renso C., Pedreschi D., Bogorny V.
The increasing abundance of data about the trajectories of personal movement is opening up new opportunities for an- alyzing and mining human mobility, but new risks emerge since it opens new ways of intruding into personal privacy. Representing the personal movements as sequences of places visited by a person during her/his movements - semantic trajectory - poses even greater privacy threats w.r.t. raw geometric location data. In this paper we propose a pri- vacy model defining the attack model of semantic trajectory linking, together with a privacy notion, called c-safety. This method provides an upper bound to the probability of in- ferring that a given person, observed in a sequence of non- sensitive places, has also stopped in any sensitive location. Coherently with the privacy model, we propose an algorithm for transforming any dataset of semantic trajectories into a c-safe one. We report a study on a real-life GPS trajec- tory dataset to show how our algorithm preserves interesting quality/utility measures of the original trajectories, such as sequential pattern mining results.Source: 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, pp. 47, San Jose, CA, USA, 3-5 November 2010
DOI: 10.1145/1868470.1868481
Metrics:
Monreale A., Trasarti R., Renso C., Pedreschi D., Bogorny V.
The increasing abundance of data about the trajectories of personal movement is opening up new opportunities for an- alyzing and mining human mobility, but new risks emerge since it opens new ways of intruding into personal privacy. Representing the personal movements as sequences of places visited by a person during her/his movements - semantic trajectory - poses even greater privacy threats w.r.t. raw geometric location data. In this paper we propose a pri- vacy model defining the attack model of semantic trajectory linking, together with a privacy notion, called c-safety. This method provides an upper bound to the probability of in- ferring that a given person, observed in a sequence of non- sensitive places, has also stopped in any sensitive location. Coherently with the privacy model, we propose an algorithm for transforming any dataset of semantic trajectories into a c-safe one. We report a study on a real-life GPS trajec- tory dataset to show how our algorithm preserves interesting quality/utility measures of the original trajectories, such as sequential pattern mining results.Source: 3rd ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS, pp. 47, San Jose, CA, USA, 3-5 November 2010
DOI: 10.1145/1868470.1868481
Metrics:
See at:
www.inf.ufsc.br | dl.acm.org | doi.org | CNR ExploRA
2010
Conference article
Restricted
Advanced knowledge discovery on movement data with the GeoPKDD system
Trasarti R., Giannotti F., Pedreschi D., Nanni M., Renso C.
The growing availability of mobile devices produces an enor- mous quantity of personal tracks which calls for advanced analysis methods capable of extracting knowledge out of massive trajectories datasets. In this paper we present an experiment on a real world scenario that demonstrates the strong analytical power of massive, raw trajectory data made available as a by-product of telecom services, in unveiling the complexity of urban mobility. The experiment has been made possible by the GeoPKDD system, an integrated plat- form for complex analysis of mobility data. The system com- bines spatio-temporal querying capabilities with data min- ing and semantic technologies, thus providing a full support for the Mobility Knowledge Discovery process.Source: 13th International Conference on Extending Database Technology, pp. 693–696, Lausanne, Switzerland, 22-26 March 2010
DOI: 10.1145/1739041.1739129
Metrics:
Trasarti R., Giannotti F., Pedreschi D., Nanni M., Renso C.
The growing availability of mobile devices produces an enor- mous quantity of personal tracks which calls for advanced analysis methods capable of extracting knowledge out of massive trajectories datasets. In this paper we present an experiment on a real world scenario that demonstrates the strong analytical power of massive, raw trajectory data made available as a by-product of telecom services, in unveiling the complexity of urban mobility. The experiment has been made possible by the GeoPKDD system, an integrated plat- form for complex analysis of mobility data. The system com- bines spatio-temporal querying capabilities with data min- ing and semantic technologies, thus providing a full support for the Mobility Knowledge Discovery process.Source: 13th International Conference on Extending Database Technology, pp. 693–696, Lausanne, Switzerland, 22-26 March 2010
DOI: 10.1145/1739041.1739129
Metrics:
See at:
dl.acm.org | doi.org | CNR ExploRA
2010
Report
Unknown
Towards anonymous semantic trajectories
Monreale A., Trasarti R., Renso C., Bogorny V., Pedreschi D.
In recent years, spatio-temporal and moving objects databases have gained consi-derable interest, due to the diffusion of mobile devices and of new applications, where the discovery of consumable, concise, and applicable knowledge is the key step. Recent advances in spatio-temporal data analysis focused on the semantic aspects of the movement data, thus leading to the definition of semantic trajectory concept. However, the analysis of this kind of data can compromise the privacy of users because the location data allows inferences which may help an attacker to discovery personal and sensitive information, like habits and preferences of individuals. In this paper we briefly present an approach for the generalization of semantic tra-jectories that can be adopted for obtaining datasets satisfying the k-anonymity property; specifically, this method exploits ontologies to realize a framework for publishing semantic trajectories while preserving privacy of the tracked users. We show that this generalization method is able to preserve the semantic tagging obtained by the analysis of the resulting dataset.Source: ISTI Technical reports, 2010
Monreale A., Trasarti R., Renso C., Bogorny V., Pedreschi D.
In recent years, spatio-temporal and moving objects databases have gained consi-derable interest, due to the diffusion of mobile devices and of new applications, where the discovery of consumable, concise, and applicable knowledge is the key step. Recent advances in spatio-temporal data analysis focused on the semantic aspects of the movement data, thus leading to the definition of semantic trajectory concept. However, the analysis of this kind of data can compromise the privacy of users because the location data allows inferences which may help an attacker to discovery personal and sensitive information, like habits and preferences of individuals. In this paper we briefly present an approach for the generalization of semantic tra-jectories that can be adopted for obtaining datasets satisfying the k-anonymity property; specifically, this method exploits ontologies to realize a framework for publishing semantic trajectories while preserving privacy of the tracked users. We show that this generalization method is able to preserve the semantic tagging obtained by the analysis of the resulting dataset.Source: ISTI Technical reports, 2010
See at: CNR ExploRA
2010
Conference article
Restricted
Mobility data mining: discovering movement patterns from trajectory data
Giannotti F., Nanni M., Pedreschi D., Pinelli F., Renso C., Rinzivillo S., Trasarti R.
The analysis of movement data has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing location-aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks [2]. These have made available massive repositories of spatio-temporal data recording human mobile activities, such as location data from mobile phones, GPS tracks from mobile devices, etc.: is it possible to discover from these data use- ful and timely knowledge about human mobility? The GeoPKDD project [1], since 2005, investigated this direction of research; the lesson learned is that there is a long way to go from raw data of individual trajectories up to high-level collective mobility knowledge, capable of supporting the decisions of mobility and transportation managers. Such analysts reason about semantically rich concepts, such as systematic vs. occasional movement behavior and home- work commuting patterns; accordingly, the mainstream analytical tools of transportation engineering, such as origin/destination ma- trices, are based on semantically rich data collected by means of field surveys and interviews. Clearly, the price to pay for this rich- ness is hard: mass surveys are very expensive, so that their peri- odicity is very broad and obsolescence is rapid; poor data quality is also a plague: people tend to respond elusively and inaccurately. On the other extreme, automatically sensed mobility data record in- dividual trajectories at mass level, in real time. Clearly, the price topay here is exactly the lack of semantics in raw data: How to bridgeFigure 1: The steps of the mobility knowledge discovery pro- cess.Source: International Workshop on Computational Transportation Science, pp. 7–10, San Jose, CA, USA, 3-5 November 2010
DOI: 10.1145/1899441.1899444
Metrics:
Giannotti F., Nanni M., Pedreschi D., Pinelli F., Renso C., Rinzivillo S., Trasarti R.
The analysis of movement data has been recently fostered by the widespread diffusion of new techniques and systems for monitoring, collecting and storing location-aware data, generated by a wealth of technological infrastructures, such as GPS positioning and wireless networks [2]. These have made available massive repositories of spatio-temporal data recording human mobile activities, such as location data from mobile phones, GPS tracks from mobile devices, etc.: is it possible to discover from these data use- ful and timely knowledge about human mobility? The GeoPKDD project [1], since 2005, investigated this direction of research; the lesson learned is that there is a long way to go from raw data of individual trajectories up to high-level collective mobility knowledge, capable of supporting the decisions of mobility and transportation managers. Such analysts reason about semantically rich concepts, such as systematic vs. occasional movement behavior and home- work commuting patterns; accordingly, the mainstream analytical tools of transportation engineering, such as origin/destination ma- trices, are based on semantically rich data collected by means of field surveys and interviews. Clearly, the price to pay for this rich- ness is hard: mass surveys are very expensive, so that their peri- odicity is very broad and obsolescence is rapid; poor data quality is also a plague: people tend to respond elusively and inaccurately. On the other extreme, automatically sensed mobility data record in- dividual trajectories at mass level, in real time. Clearly, the price topay here is exactly the lack of semantics in raw data: How to bridgeFigure 1: The steps of the mobility knowledge discovery pro- cess.Source: International Workshop on Computational Transportation Science, pp. 7–10, San Jose, CA, USA, 3-5 November 2010
DOI: 10.1145/1899441.1899444
Metrics:
See at:
dl.acm.org | doi.org | CNR ExploRA