PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Pinelli F., Giannotti F., Pedreschi D., Nanni M. Trajectory Pattern Mining. In: The 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (San Jose, California, USA, 12-15 August 2007). Proceedings, pp. 330 - 339. Pavel Berkhin, Rich Caruana & Xindong Wu, Scott Gaffney (eds.). ACM New York, NY, USA, 2007.
 
 
Abstract
(English)
The increasing pervasiveness of location-acquisition tech- nologies (GPS, GSM networks, etc.) is leading to the collec- tion of large spatio-temporal datasets and to the opportunity of discovering usable knowledge about movement behaviour, which fosters novel applications and services. In this paper, we move towards this direction and develop an extension of the sequential pattern mining paradigm that analyzes the trajectories of moving objects. We introduce trajectory patterns as concise descriptions of frequent behaviours, in terms of both space (i.e., the regions of space visited during movements) and time (i.e., the duration of movements). In this setting, we provide a general formal statement of the novel mining problem and then study several different in- stantiations of different complexity. The various approaches are then empirically evaluated over real data and synthetic benchmarks, comparing their strengths and weaknesses.
Subject data mining
H.2.8 Database Applications


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