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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 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings, Part I, Springer, Berlin, Germany, pp. 375-386, doi: 10.1007/978-3-642-20841-6_31.

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Title Efficiently retrieving longest common route patterns of moving objects by summarizing turning regions
Author(s) Huang, Guangyan
Zhang, Yanchun
He, Jing
Ding, Zhiming
Conference name Pacific-Asia Conference on Knowledge Discovery and Data Mining (15th : 2011 : Shenzheng, China)
Conference location Shenzheng, China
Conference dates 24-27 May 2011
Title of proceedings 15th Pacific-Asia Conference, PAKDD 2011, Shenzhen, China, May 24-27, 2011, Proceedings, Part I
Editor(s) Huang, J. Z.
Cao, L.
Srivastava, J.
Publication date 2011
Series Lecture Notes in Artificial Intelligence v.6634
Start page 375
End page 386
Total pages 12
Publisher Springer
Place of publication Berlin, Germany
Keyword(s) spatial temporal data mining
trajectories of moving objects
longest common route patterns
Summary 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. © 2011 Springer-Verlag.
ISBN 9783642208416
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-642-20841-6_31
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1.1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2011, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083687

Document type: Conference Paper
Collection: School of Information Technology
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