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Efficiently Retrieving Longest Common Route Patterns of Moving Objects By Summarizing Turning Regions

Huang, Guangyan, Zhang, Yanchun, He, Jing and Ding, Zhiming (2011) Efficiently Retrieving Longest Common Route Patterns of Moving Objects By Summarizing Turning Regions. In: Advances in Knowledge Discovery and Data Mining: 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings, Part I. Huang, Joshua Zhexue, Cao, Longbing and Srivastava, Jaideep, eds. Lecture Notes in Computer Science . Springer, Heidelberg, pp. 375-386.

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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.

Item Type: Book Section
ISBN: 9783642208409 (print) 9783642208416 (online) 0302-9743 (Series ISSN)
Uncontrolled Keywords: ResPubID22802, spatial temporal data mining, trajectories of moving objects, longest common route patterns
Subjects: Faculty/School/Research Centre/Department > School of Engineering and Science
FOR Classification > 0806 Information Systems
SEO Classification > 8903 Information Services
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Depositing User: VUIR
Date Deposited: 27 Aug 2013 03:54
Last Modified: 27 Aug 2013 03:54
URI: http://vuir.vu.edu.au/id/eprint/9817
DOI: https://doi.org/10.1007/978-3-642-20841-6_31
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Citations in Scopus: 6 - View on Scopus

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