PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Trasarti R., Rinzivillo S., Pinelli F., Nanni M., Monreale A., Renso C., Pedreschi D., Giannotti F. Exploring real mobility data with M-Atlas. In: ECML PKDD 2010 - Machine Learning and Knowledge Discovery in Databases. European Conference (Barcelona, Spain, 20-24 September 2010). Proceedings, vol. III pp. 624 - 627. José Luis Balcázar, Francesco Bonchi, Aristides Gionis, Michèle Sebag (eds.). (Lecture Notes in Artificial Intelligence, vol. 6323). Springer, 2010.
 
 
Abstract
(English)
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.
URL: http://www.springerlink.com/content/31522g426qq80q11/
DOI: 10.1007/978-3-642-15939-8_48
Subject Data mining
Applications and ExpertSystems
computational transportation science (CTS)
H.2.8 Database Management. Data mining


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