Efficiently retrieving longest common route patterns of moving objects by summarizing turning regions

Authors:
Guangyan Huang;Yanchun Zhang;Jing He;Zhiming Ding
Affiliations:
Centre for Applied Informatics, School of Engineering & Science, Victoria University, Australia;Centre for Applied Informatics, School of Engineering & Science, Victoria University, Australia;Centre for Applied Informatics, School of Engineering & Science, Victoria University, Australia;Institute of Software Chinese Academy of Sciences
Venue:
PAKDD'11 Proceedings of the 15th Pacific-Asia conference on Advances in knowledge discovery and data mining - Volume Part I
Year:
2011

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Abstract

The popularity of online location services provides opportunities to discover useful knowledge from trajectories of moving objects. This paper addresses the problem of mining longest common route (LCR) patterns. As a trajectory of a moving object is generally represented by a sequence of discrete locations sampled with an interval, the different trajectory instances along the same route may be denoted by different sequences of points (location, timestamp). Thus, the most challenging task in the mining process is to abstract trajectories by the right points. We propose a novel mining algorithm for LCR patterns based on turning regions (LCRTurning), which discovers a sequence of turning regions to abstract a trajectory and then maps the problem into the traditional problem of mining longest common subsequences (LCS). Effectiveness of LCRTurning algorithm is validated by an experimental study based on various sizes of simulated moving objects datasets.