Compressing Spatio-temporal Trajectories

  • Joachim Gudmundsson
  • Jyrki Katajainen
  • Damian Merrick
  • Cahya Ong
  • Thomas Wolle
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4835)


Trajectory data is becoming increasingly available and the size of the trajectories is getting larger. In this paper we study the problem of compressing spatio-temporal trajectories such that the most common queries can still be answered approximately after the compression step has taken place. In the process we develop an O(n log k n)-time implementation of the Douglas-Peucker algorithm in the case when the polygonal path of n vertices given as input is allowed to self-intersect.


Line Segment Distance Function Convex Hull Line Model Query Point 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Joachim Gudmundsson
    • 1
  • Jyrki Katajainen
    • 2
  • Damian Merrick
    • 1
    • 3
  • Cahya Ong
    • 4
  • Thomas Wolle
    • 1
  1. 1.NICTA, SydneyAustralia
  2. 2.Department of Computing, University of CopenhagenDenmark
  3. 3.School of Information Technologies, University of SydneyAustralia
  4. 4.Department of Engineering, University of New South WalesAustralia

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