Trajectory pattern mining

作者：Fosca Giannotti, Mirco Nanni, Fabio Pinelli, Dino Pedreschi · 年份：2007 · DOI：10.1145/1281192.1281230 · 被引用次数：1010 · 研究领域：Data Management and Algorithms、Data Mining Algorithms and Applications、Advanced Database Systems and Queries

The increasing pervasiveness of location-acquisition technologies (GPS, GSM networks, etc.) is leading to the collection 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 instantiations of different complexity. The various approaches are then empirically evaluated over real data and synthetic benchmarks, comparing their strengths and weaknesses.