Advertisement

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

  • Guangyan Huang
  • Yanchun Zhang
  • Jing He
  • Zhiming Ding
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6634)

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.

Keywords

spatial temporal data mining trajectories of moving objects longest common route patterns 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Gidofalvi, G., Pedersen, T.B.: Mining Long, Sharable Patterns in Trajectories of Moving Objects. GeoInformatica 13(1), 27–55 (2009)CrossRefGoogle Scholar
  2. 2.
    Cao, H., Mamoulis, N., Cheung, D.W.: Mining frequent spatio-temporal sequential patterns. In: ICDM 2005, pp. 82–89 (2005)Google Scholar
  3. 3.
    Jeung, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.: Discovery of convoys in trajectory databases. In: VLDB, pp. 1068–1080 (2008)Google Scholar
  4. 4.
    Gudmundsson, J., Kreveld, M.V., Speckmann, B.: Efficient detection of patterns in 2D trajectories of moving points. Geoinformatica 11, 195–215 (2007)CrossRefGoogle Scholar
  5. 5.
    Giannotti, F., Nanni, M., Pedreschi, D., Pinelli, F.: Trajectory pattern mining. In: SIGKDD 2007, pp. 330–339 (2007)Google Scholar
  6. 6.
    Douglas, D.H., Peucker, T.K.: Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. The Canadian Cartographer 10(2), 112–122 (1973)CrossRefGoogle Scholar
  7. 7.
    White, E.R.: Assessment of line generalization algorithms using characteristic points. The American Cartographer 12, 17–27 (1985)CrossRefGoogle Scholar
  8. 8.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: SIGKDD 1996, pp. 226–231 (1996)Google Scholar
  9. 9.
    He, J., Huang, G., Zhang, Y., Shi, Y.: Cluster analysis and optimization in color-based clustering for image abstract. In: ICDM Workshops, pp. 213–218 (2007)Google Scholar
  10. 10.
    McCreight, E.M.: HA space-economical suffix tree construction algorithmH. Journal of the ACM 23(2), 262–272 (1976)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Dan, G.: Algorithms on Strings, Trees and Sequences: Computer Science and Computational Biology. Cambridge University Press, USA (1997)zbMATHGoogle Scholar
  12. 12.
    Lee, J.-G., Han, J., Whang, K.-Y.: Trajectory clustering: a partition-and-group framework. In: SIGMOD 2007, pp. 593–604 (2007)Google Scholar
  13. 13.
    Brinkhoff, T.: A framework for generating network-based moving objects. GeoInformatica 6(2) (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Guangyan Huang
    • 1
  • Yanchun Zhang
    • 1
  • Jing He
    • 1
  • Zhiming Ding
    • 2
  1. 1.Centre for Applied Informatics, School of Engineering & ScienceVictoria UniversityAustralia
  2. 2.Institute of Software Chinese Academy of SciencesChina

Personalised recommendations