Clustering Multidimensional Extended Objects to Speed Up Execution of Spatial Queries

  • Cristian-Augustin Saita
  • François Llirbat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2992)

Abstract

We present a cost-based adaptive clustering method to improve average performance of spatial queries (intersection, containment, enclosure queries) over large collections of multidimensional extended objects (hyper-intervals or hyper-rectangles). Our object clustering strategy is based on a cost model which takes into account the spatial object distribution, the query distribution, and a set of database and system parameters affecting the query performance: object size, access time, transfer and verification costs. We also employ a new grouping criterion to group objects in clusters, more efficient than traditional approaches based on minimum bounding in all dimensions. Our cost model is flexible and can accommodate different storage scenarios: in-memory or disk-based. Experimental evaluations show that our approach is efficient in a number of situations involving large spatial databases with many dimensions.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Altimel, M., Franklin, M.J.: Efficient filtering of XML documents for selective dissemination of information. In: Proc. 26th VLDB Conf., Cairo, Egypt (2000)Google Scholar
  2. 2.
    Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The R*-tree: An efficient and robust access method for points and rectangles. In: Proc. ACM SIGMOD Conf., Atlantic City, NJ (1990)Google Scholar
  3. 3.
    Berchtold, S., Böhm, C., Kriegel, H.-P.: The Pyramid-technique: Towards breaking the curse of dimensionality. In: Proc. ACM SIGMOD Conf., Seattle, Washington (1998)Google Scholar
  4. 4.
    Berchtold, S., Keim, D.A., Kriegel, H.-P.: The X-tree: An index structure for high-dimensional data. In: Proc. 22nd VLDB Conf., Bombay, India (1996)Google Scholar
  5. 5.
    Beyer, K., Goldstein, J., Ramakrishnan, R., Shaft, U.: When is “nearest neighbor” meaningful? In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 217–235. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  6. 6.
    Böhm, C., Berchtold, S., Keim, D.A.: Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases. ACM Computing Surveys 33(3), 322–373 (2001)CrossRefGoogle Scholar
  7. 7.
    Böhm, C., Kriegel, H.-P.: Dynamically optimizing high-dimensional index structures. In: Zaniolo, C., Grust, T., Scholl, M.H., Lockemann, P.C. (eds.) EDBT 2000. LNCS, vol. 1777, p. 36. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Fabret, F., Jacobsen, H., Llirbat, F., Pereira, J., Ross, K., Shasha, D.: Filtering algorithm and implementation for very fast publish/subscribe systems. In: Proc. ACM SIGMOD Conf., Santa Barbara, California, USA (2001)Google Scholar
  9. 9.
    Faloutsos, C., Bhagwat, P.: Declustering using fractals. PDIS Journal of Parallel and Distributed Information Systems, 18–25 (1993)Google Scholar
  10. 10.
    Gaede, V., Günther, O.: Multidimensional access methods. ACM Computing Surveys 30(2), 170–231 (1998)CrossRefGoogle Scholar
  11. 11.
    Guttman, A.: R-trees: A dynamic index structure for spatial searching. In: Proc. ACM SIGMOD Conf., pp. 47–57 (1984)Google Scholar
  12. 12.
    Liu, H., Jacobsen, H.A.: Modelling uncertainties in publish/subscribe systems. In: Proc. 20th ICDE Conf., Boston, USA (2004)Google Scholar
  13. 13.
    Pagel, B.-U., Six, H.-W., Winter, M.: Window query-optimal clustering of spatial objects. In: Proc. ACM PODS Conf., San Jose (1995)Google Scholar
  14. 14.
    Sellis, T., Roussopoulos, N., Faloustos, C.: The R+-tree: A dynamic index for multi-dimensional objects. In: Proc. VLDB Conf., Brighton, England (1987)Google Scholar
  15. 15.
    Tao, Y., Papadias, D.: Adaptive index structures. In: Proc. 28th VLDB Conf., Hong Kong, China (2002)Google Scholar
  16. 16.
    Weber, R., Schek, H.-J., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: Proc. 24th VLDB Conf., New York, USA (1998)Google Scholar
  17. 17.
    Yu, C.: High-dimensional indexing. In: Yu, C. (ed.) High-Dimensional Indexing. LNCS, vol. 2341, pp. 9–35. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Cristian-Augustin Saita
    • 1
  • François Llirbat
    • 1
  1. 1.INRIA-Rocquencourt, Domaine de VoluceauLe Chesnay CedexFrance

Personalised recommendations