Parallel High-Dimensional Index Structure Using Cell-Based Filtering for Multimedia Data

  • Jae-Woo Chang
  • Yong-Ki Kim
  • Young-Jin Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4331)


A large number of high-dimensional index structures suffer from the so called ’dimensional curse’ problem, i.e., the retrieval performance becomes increasingly degraded as the dimensionality is increased. To solve this problem, the cell-based filtering scheme has been proposed, but it shows a linear decrease in performance as the dimensionality is increased. In this paper, we propose a parallel high-dimensional index structure using the cell-based filtering for multimedia data so as to cope with the linear decrease in retrieval performance. In addition, we devise data insertion, range query and k-NN query processing algorithms which are suitable for the cluster-based parallel architecture. Finally, we show that our parallel index structure achieves good retrieval performance in proportion to the number of servers in the cluster-based architecture and it outperforms a parallel version of the VA-File when the dimensionality is over 10.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Robinson, J.T.: The K-D-B-tree: A Search Structure for Large Multidimensional Dynamic Indexes. In: Proc. of Int. Conf. on Management of Data, pp. 10–18 (1981)Google Scholar
  2. 2.
    White, D.A., Jain, R.: Similarity Indexing: Algorithms and Performance. In: Proc. of the SPIE: Storage and Retrieval for Image and Video Databases IV, vol. 2670, pp. 62–75 (1996)Google Scholar
  3. 3.
    Lin, H.I., Jagadish, H., Faloutsos, C.: The TV-tree: An Index Structure for High Di-men-sional Data. VLDB Journal 3, 517–542 (1995)CrossRefGoogle Scholar
  4. 4.
    Berchtold, S., Keim, D.A., Kriegel, H.-P.: The X-tree: An Index Structure for High-Dimensional Data. In: Proceedings of the 22nd VLDB Conference, pp. 28–39 (1996)Google Scholar
  5. 5.
    Arya, S., Mount, D.M., Narayan, O.: Accounting for Boundary Effects in Nearest Neighbor Searching. In: Proc. of 11th Annaual Symp. on Computational Geometry, Vancouver, Canada, pp. 336–344 (1995)Google Scholar
  6. 6.
    Berchtold, S., Bohm, C., Keim, D., Kriegel, H.-P.: A Cost Model for Nearest Neighbor Search in High-Dimensional Data Space. In: ACM PODS Symposium on Principles of Data-bases Systems, Tucson, Arizona (1997)Google Scholar
  7. 7.
    Weber, R., Schek, H.-J., Blott, S.: A Quantitative Analysis and Perform-ance Study for Similarity-Search Methods in High-Dimensional Spaces. In: Proc. of 24th International Conference on Very Large Data Bases, pp. 24–27 (1998)Google Scholar
  8. 8.
    Han, S.-G., Chang, J.-W.: A New High-Dimensional Index Structure Using a Cell-Based Filtering Technique. In: Masunaga, Y., Thalheim, B., Štuller, J., Pokorný, J. (eds.) ADBIS 2000 and DASFAA 2000. LNCS, vol. 1884, pp. 79–92. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  9. 9.
    Faloutsos, C.: Design of a Signature File Method that Accounts for Non-Uniform Occur-rence and Query Frequencies. In: ACM SIGMOD, pp. 165–170 (1985)Google Scholar
  10. 10.
    Kim, J.-K., Chang, J.-W.: Horizontally-divided Signature File on a Parallel Machine Architecture. Journal of Systems Architecture 44(9-10), 723–735 (1998)CrossRefGoogle Scholar
  11. 11.
    Kim, J.-K., Chang, J.-W.: Vertically-partitioned Parallel Signature File Method. Journal of Systems Architecture 46(8), 655–673 (2000)CrossRefGoogle Scholar
  12. 12.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest Neighbor Queries. In: Proc. of ACM Int. Conf. on Management of Data (SIGMOD), pp. 71–79 (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jae-Woo Chang
    • 1
  • Yong-Ki Kim
    • 1
  • Young-Jin Kim
    • 1
  1. 1.Dept. of Computer Eng.Chonbuk National Univ.Chonju, ChonbukSouth Korea

Personalised recommendations