Multiplexing Trajectories of Moving Objects

  • Kostas Patroumpas
  • Kyriakos Toumbas
  • Timos Sellis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7338)


Continuously tracking mobility of humans, vehicles or merchandise not only provides streaming, real-time information about their current whereabouts, but can also progressively assemble historical traces, i.e., their evolving trajectories. In this paper, we outline a framework for online detection of groups of moving objects with approximately similar routes over the recent past. Further, we propose an encoding scheme for synthesizing an indicative trajectory that collectively represents movement features pertaining to objects in the same group. Preliminary experimentation with this multiplexing scheme shows encouraging results in terms of both maintenance cost and compression accuracy.


Encode Scheme Symbolic Sequence Trajectory Segment Execution Cycle Trajectory Cluster 
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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  1. 1.
    Bakalov, P., Hadjieleftheriou, M., Keogh, E., Tsotras, V.J.: Efficient Trajectory Joins using Symbolic Representations. In: MDM, pp. 86–93 (2005)Google Scholar
  2. 2.
    Chen, Z., Shen, H.T., Zhou, X.: Discovering Popular Routes from Trajectories. In: ICDE, pp. 900–911 (2011)Google Scholar
  3. 3.
    Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: KDD, pp. 226–231 (1996)Google Scholar
  4. 4.
    Jeungy, H., Yiu, M.L., Zhou, X., Jensen, C.S., Shen, H.T.: Discovery of Convoys in Trajectory Databases. PVLDB 1(1), 1068–1080 (2008)Google Scholar
  5. 5.
    Lee, J., Han, J., Whang, K.: Trajectory Clustering: a Partition-and-Group Framework. In: ACM SIGMOD, pp. 593–604 (2007)Google Scholar
  6. 6.
    Patroumpas, K., Sellis, T.: Maintaining Consistent Results of Continuous Queries under Diverse Window Specifications. Information Systems 36(1), 42–61 (2011)CrossRefGoogle Scholar
  7. 7.
    Potamias, M., Patroumpas, K., Sellis, T.: Sampling Trajectory Streams with Spatiotemporal Criteria. In: SSDBM, pp. 275–284 (2006)Google Scholar
  8. 8.
    Sacharidis, D., Patroumpas, K., Terrovitis, M., Kantere, V., Potamias, M., Mouratidis, K., Sellis, T.: Online Discovery of Hot Motion Paths. In: EDBT, pp. 392–403 (2008)Google Scholar
  9. 9.
    Vieira, M., Bakalov, P., Tsotras, V.J.: On-line Discovery of Flock Patterns in Spatio-temporal Data. In: ACM GIS, pp. 286–295 (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kostas Patroumpas
    • 1
  • Kyriakos Toumbas
    • 1
  • Timos Sellis
    • 1
    • 2
  1. 1.School of Electrical and Computer EngineeringNational Technical University of AthensHellas
  2. 2.Institute for the Management of Information Systems, R.C. “Athena”Hellas

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