The COST Benchmark—Comparison and Evaluation of Spatio-temporal Indexes

  • Christian S. Jensen
  • Dalia Tiešytė
  • Nerius Tradišauskas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3882)


An infrastructure is emerging that enables the positioning of populations of on-line, mobile service users. In step with this, research in the management of moving objects has attracted substantial attention. In particular, quite a few proposals now exist for the indexing of moving objects, and more are underway. As a result, there is an increasing need for an independent benchmark for spatio-temporal indexes.

This paper characterizes the spatio-temporal indexing problem and proposes a benchmark for the performance evaluation and comparison of spatio-temporal indexes. Notably, the benchmark takes into account that the available positions of the moving objects are inaccurate, an aspect largely ignored in previous indexing research. The concepts of data and query enlargement are introduced for addressing inaccuracy. As proof of concepts of the benchmark, the paper covers the application of the benchmark to three spatio-temporal indexes—the TPR-, TPR*-, and Bx-trees. Representative experimental results and consequent guidelines for the usage of these indexes are reported.


Global Position System Query Enlargement Query Performance Indexing Technique Query Type 
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.
    Blewitt, G.: Basics of the GPS technique: observation equations. Geodetic Applications of GPS, 10–54 (1997)Google Scholar
  2. 2.
    Wikipedia: GPRS (2001–2005),
  3. 3.
    Šaltenis, S., Jensen, C.S., Leutenegger, S.T., Lopez, M.A.: Indexing the positions of continuously moving objects. In: Proc. ACM SIGMOD, pp. 331–342 (2000)Google Scholar
  4. 4.
    Tao, Y., Papadias, D., Sun, J.: The TPR*-tree: an optimized spatio-temporal access method for predictive queries. In: Aberer, K., Koubarakis, M., Kalogeraki, V. (eds.) VLDB 2003. LNCS, vol. 2944, pp. 790–801. Springer, Heidelberg (2004)Google Scholar
  5. 5.
    Procopiuc, C.M., Agarwal, P.K., Har-Peled, S.: STAR-tree: an efficient self-adjusting index for moving objects. In: Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments, pp. 178–193 (2002)Google Scholar
  6. 6.
    Šaltenis, S., Jensen, C.S.: Indexing of Moving Objects for Location-Based Services. In: Proc. ICDE, pp. 463–472 (2002)Google Scholar
  7. 7.
    Patel, J.M., Arbor, A., Chen, Y., Chakka, V.P.: STRIPES: an efficient index for predicted trajectories. In: Proc. ACM SIGMOD, pp. 635–646 (2004)Google Scholar
  8. 8.
    Jensen, C.S., Lin, D., Ooi, B.C.: Query and update efficient B+-tree based indexing of moving objects. In: Proc. VLDB, pp. 768–779 (2004)Google Scholar
  9. 9.
    Zobel, J., Moffat, A., Ramamohanarao, K.: Guidelines for presentation and comparison of indexing techniques. SIGMOD Rec. 25, 10–15 (1996)CrossRefGoogle Scholar
  10. 10.
    Gray, J. (ed.): The Benchmark Handbook for Database and Transaction Processing Systems. Morgan Kaufmann Publishers, Inc, San Francisco (1993)MATHGoogle Scholar
  11. 11.
    Theodoridis, Y.: Ten benchmark database queries for location-based services. The Computer Journal 46, 713–725 (2003)CrossRefMATHGoogle Scholar
  12. 12.
    Myllymaki, J., Kaufman, J.: DynaMark: A Benchmark for Dynamic Spatial Indexing. In: Chen, M.-S., Chrysanthis, P.K., Sloman, M., Zaslavsky, A. (eds.) MDM 2003. LNCS, vol. 2574, pp. 92–105. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Werstein, P.F.: A performance benchmark for spatiotemporal databases. In: Proc. of the 10th Annual Colloquium of the Spatial Information Research Centre, pp. 365–373 (1998)Google Scholar
  14. 14.
    Tzouramanis, T., Vassilakopoulos, M., Manolopoulos, Y.: Benchmarking access methods for time-evolving regional data. Data Knowl. Eng. 49, 243–286 (2004)CrossRefMATHGoogle Scholar
  15. 15.
    Cheng, R., Kalashnikov, D.V., Prabhakar, S.: Querying imprecise data in moving object environments. IEEE Trans. on Knowl. and Data Eng. 16, 1112–1127 (2004)CrossRefGoogle Scholar
  16. 16.
    Tao, Y., Cheng, R., Xiao, X., Ngai, W.K., Kao, B., Prabhakar, S.: Indexing multi-dimensional uncertain data with arbitrary probability density functions. In: Proc. VLDB, pp. 922–933 (2005)Google Scholar
  17. 17.
    Čivilis, A., Jensen, C.S.: Efficient tracking of moving objects with precision guarantees. In: Proc. MobiQuitous, pp. 164–173 (2004)Google Scholar
  18. 18.
    Wolfson, O., Sistla, A.P., Chamberlain, S., Yesha, Y.: Updating and querying databases that track mobile units. Distrib. Parallel Databases 7, 257–387 (1999)CrossRefGoogle Scholar
  19. 19.
    Pfoser, D., Jensen, C.S.: Capturing the uncertainty of moving-object representations. In: Güting, R.H., Papadias, D., Lochovsky, F.H. (eds.) SSD 1999. LNCS, vol. 1651, pp. 111–132. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  20. 20.
    Lazaridis, I., Mehrotra, S.: Approximate selection queries over imprecise data. In: Proc. ICDE, pp. 140–152 (2004)Google Scholar
  21. 21.
    Weisstein, E.W.: Minkowski sum. From MathWorld—A Wolfram web resource (1999–2005),
  22. 22.
    Šaltenis, S., Jensen, C.S., Leutenegger, S., Lopez, M.: Indexing the positions of continuously moving objects. Technical report, Aalborg University (November 1999)Google Scholar
  23. 23.
    Kaufman, J., Myllymaki, J., Jackson, J.: CitySimulator (2001),
  24. 24.
    Myllymaki, J., Kaufman, J.: LOCUS: A testbed for dynamic spatial indexing. IEEE Data Eng. 25, 48–55 (2002)Google Scholar
  25. 25.
    Theodoridis, Y., Nascimento, M.A.: Generating spatiotemporal datasets on the WWW. SIGMOD Rec. 29, 39–43 (2000)CrossRefGoogle Scholar
  26. 26.
    Theodoridis, Y., Silva, J.R.O., Nascimento, M.A.: On the generation of spatiotemporal datasets. In: Proc. of the 6th International Symposium on Advances in Spatial Databases, pp. 147–164 (1999)Google Scholar
  27. 27.
    Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The R*-tree: an efficient and robust access method for points and rectangles. In: Proc. SIGMOD, pp. 322–331 (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Christian S. Jensen
    • 1
  • Dalia Tiešytė
    • 1
  • Nerius Tradišauskas
    • 1
  1. 1.Department of Computer ScienceAalborg UniversityDenmark

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