Accuracy and Resource Consumption in Tracking and Location Prediction

  • Ouri Wolfson
  • Huabei Yin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2750)


Tracking is an enabling technology for many location based services. Given that the location of a moving object changes continuously but the database cannot be updated continuously, the research issue is how to accurately maintain the current location of a large number of moving objects while minimizing the number of updates. The traditional approach used in existing commercial transportation systems is for the moving object or the cellular network to periodically update the location database; e.g. every 2 miles. We introduce a new location update policy, and show experimentally that it is superior to the simplistic policy currently used for tracking; the superiority is up to 43% depending on the uncertainty threshold. We also introduce a method of generating realistic synthetic spatio-temporal information, namely pseudo trajectories of moving objects. The method selects a random route, and superimposes on it speed patterns that were recorded during actual driving trips.


Global Position System Global Position System Receiver Database Location Location Prediction Location Update 
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.
    Wolfson, O., Prasad Sistla, A., Chamberlain, S., Yesha, Y.: Updating and Querying Databases that Track Mobile Units. Special issue of the Distributed and Parallel Databases Journal on Mobile Data Management and Applications 7(3) (1999)Google Scholar
  2. 2.
  3. 3.
    Theodoridis, Y., Silva, J.R.O., Nascimento, M.A.: On the Generation of Spatiotemporal Datasets. In: Güting, R.H., Papadias, D., Lochovsky, F.H. (eds.) SSD 1999. LNCS, vol. 1651, pp. 147–164. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  4. 4.
    Pfoser, D., Theodoridis, Y.: Generating Semantics-Based Trajectories of Moving Objects. In: Intern. Workshop on Emerging Technologies for Geo-Based Applications, Ascona (2000)Google Scholar
  5. 5.
    Thomas, B.: Generating Network-Based Moving Objects. In: Proc. of the 12th Inter. Conf. on Scientific and Statistical Database Management, July 26-28 (2000)Google Scholar
  6. 6.
    Thomas, B.: A Framework for Generating Network-Based Moving Objects. Tech. Report of the IAPG,
  7. 7.
    Saglio, J.-M., Moreira, J.: Oporto: A Realistic Scenario Generator for Moving Objects. In: Proc. of 10th Inter. Workshop on Database and Expert Systems Applications, pp. 426–432. IEEE Computer Society, Florence (1999) ISBN 0-7695-0281-4CrossRefGoogle Scholar
  8. 8.
    Kollios, G., Gunopulos, D., Tsotras, V., Dellis, A., Hadjieleftheriou, M.: Indexing Animated Objects Using Spatiotemporal Access Methods. IEEE Transactions on Knowledge and Data Engineering 13(5), 758–777 (2001)CrossRefGoogle Scholar
  9. 9.
    Tao, Y., Papadias, D.: The MV3R-tree: A Spatio-Temporal Access Method for Timestamp and Interval Queries. In: VLDB (2001)Google Scholar
  10. 10.
    Pfoser, D., Jensen, C.S., Theodoridis, Y.: Novel Approaches to the Indexing of Moving Object Trajectories. In: VLDB 2000, Cairo, Egypt (September 2000)Google Scholar
  11. 11.
    Trajcevski, G., Wolfson, O., Zhang, F., Chamberlian, S.: The Geometry of Uncertainty in Moving Objects Databases. In: Jensen, C.S., Jeffery, K., Pokorný, J., Šaltenis, S., Bertino, E., Böhm, K., Jarke, M. (eds.) EDBT 2002. LNCS, vol. 2287, p. 233. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  12. 12.
    Vlachos, M., Kollios, G., Gunopulos, D.: Discovering Similar Multidimensional Trajectories. In: ICDE (2002)Google Scholar
  13. 13.
    Trimble Navigation Limited. Differential GPS Sources,
  14. 14.
    Myllymaki, J., Kaufman, J.: LOCUS: A Testbed for Dynamic Spatial Indexing. IEEE Data Engineering Bulletin 25(2), 48–55 (2002)Google Scholar
  15. 15.
  16. 16.
    Song, Z., Roussopoulos, N.: Hashing Moving Objects. MDM (2001)Google Scholar
  17. 17.
    Lam, K.-Y., Ulusoy, O., Lee, T.S.H., Chan, E., Li, G.: Generating Location Updates for Processing of Location-Dependent Continuous Queries. In: DASFAA 2001, Hong Kong (2001)Google Scholar
  18. 18.
    Snodgrass, R.T., Ahn, I.: The Temporal Databases. IEEE Computer 19(9), 35–42 (1986)Google Scholar
  19. 19.
    QUALCOMM Inc.,
  20. 20.
    At Road Inc.,
  21. 21.
    Jensen, C.S., Saltenis, S.: Towards Increasingly Update Efficient Moving-Object Indexing. Bulletin of the IEEE on Data Engineering (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Ouri Wolfson
    • 1
  • Huabei Yin
    • 1
  1. 1.Department of Computer ScienceChicagoUSA

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