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A Study on Grid Partition for Declustering High-Dimensional Data

  • Tae-Wan Kim
  • Hak-Cheol Kim
  • Ki-Joune Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3261)

Abstract

Most of the previous work on declustering have been focused on proposing good mapping functions under the assumption that the data space is partitioned equally for all dimensions. In this paper, we relax equal partition restriction on all dimensions by choosing smaller number of dimensions as split axes and study the effects of grid-like partitioning methods on the performance of a mapping function which is widely used for declustering algorithms. For this, we propose a cost model to expect the number of grid cells intersecting a range query and apply the best mapping scheme so far to the partitioned grid cells. Experiments show that our cost model gives remarkable accuracy for all ranges of selectivities and dimensions. By applying different partitioning schemes on the Kronecker sequence mapping function [5], which is known to be the best mapping function for high-dimensional data so far, we can achieve up to 23 times performance gain. Thus we can conclude that the performance of a mapping function is highly dependent on partitioning schemes applied. And our cost model gives clear criteria on how to select the number of split dimensions out of d dimensions to achieve better performance of a mapping function on declustering.

Keywords

Grid Cell Mapping Function Cost Model High Dimensional Data Range Query 
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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References

  1. 1.
    Atallah, M.J., Prabhakar, S. (Almost) Optimal Parallel Block Access for Range Queries. In: Proc. PODS Conf., pp. 205–215 (2000)Google Scholar
  2. 2.
    Berchtold, S., Böhm, C., Kriegel, H.-.P.: Improving the Query Performance of High-Dimensional Index Structures by Bulk Loading R-trees. In: Schek, H.-J., Saltor, F., Ramos, I., Alonso, G. (eds.) EDBT 1998. LNCS, vol. 1377, pp. 216–230. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  3. 3.
    Bhatia, R., Sinha, R.K., Chen, C.-M.: Declustering Using Golden Ratio Sequences. In: Proc. ICDE Conf., pp. 271–280 (2000)Google Scholar
  4. 4.
    Chen, C.M., Cheng, C.T.: From Discrepancy to Declustering: Near optimal multidimensional declustering strategies for range queries. In: Proc. PODS Conf., pp. 29–38 (2002)Google Scholar
  5. 5.
    Chend, C.-M., Bhatia, R., Sinha, R.K.: Multidimensional Declustering Schemes Using Golden Ratio Sequence and Kronecker Sequences. IEEE TKDE 15(3), 659–670 (2003)Google Scholar
  6. 6.
    Du, H.C., Sobolewski, J.S.: Disk Allocation for Cartisian Files on Multiple-Disk Systems. ACM Trans. Database Systems 7(1), 82–102 (1982)MATHCrossRefGoogle Scholar
  7. 7.
    Faloutsos, C., Bhagwat, P.: Declustering Using Fractals. In: Proc. Parallel and Distributed Information Systems Conf., pp. 18–25 (1993)Google Scholar
  8. 8.
    Faloutsos, C., Metaxas, D.: Disk Allocation Methods Using Error Correcting Codes. IEEE Trans. on Computers 40(8), 907–914 (1991)CrossRefGoogle Scholar
  9. 9.
    Fang, M.T., Lee, R.C.T., Chang, C.C.: The Idea of De-Clustering and Its applications. In: Proc. VLDB Conf., pp. 181–188 (1986)Google Scholar
  10. 10.
    S-Wk. Kao, M., Winslee, M., Cho, Y., Lee., J.: New GDM-based Declustering Methods for Parallel Range Queries. In: Proc. IDEAS Symp., pp. 119–127 (1999)Google Scholar
  11. 11.
    Kim, H.C., Li, K.J.: Declustering Spatial Objects by Clustering for Parallel Disks. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, pp. 450–459. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  12. 12.
    Kim, H.C., Lopez, M., Leutenegger, S.T., Li, K.J.: Efficient Declustering of Nonuniform Multidimensional data Using Shifted Hilbert Curves. In: Lee, Y., Li, J., Whang, K.-Y., Lee, D. (eds.) DASFAA 2004. LNCS, vol. 2973, pp. 694–707. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Kim, M.H., Pramanik, S.: Optimal File Distribution For Partial Match Retrieval. In: Proc. SIGMOD Conf., pp. 173–182 (1988)Google Scholar
  14. 14.
    Liu, D.R., Shekhar, S.: Partitioning Similarity Graphs: A Framework for Declustering Problems. International Journal Information System 21(6), 475–496 (1996)Google Scholar
  15. 15.
    Liu, D.R., Wu, M.Y.: A Hypergraph Based Approach to Declustering Problems. Distributed and Parallel Databases 10(3), 269–288 (2001)CrossRefGoogle Scholar
  16. 16.
    Prabhakar, S., Abdel-Ghaffar, K., El Abbadi, A.: Cyclic Allocation of Two-Dimensional Data. In: Proc. ICDE Conf., pp. 94–101 (1998)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Tae-Wan Kim
    • 1
  • Hak-Cheol Kim
    • 2
  • Ki-Joune Li
    • 2
  1. 1.Research Institute of Computer Information and CommunicationPusan National UniversityKorea
  2. 2.Department of Computer SciencePusan National UniversityKorea

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