Developing Parallel Cell-Based Filtering Scheme Under Shared-Nothing Cluster-Based Architecture

  • Jae-Woo Chang
  • Tae-Woong Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4032)


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 (CBF) scheme has been proposed, but it shows a linear decrease in performance as the dimensionality is increased. In this paper, we develop a parallel CBF scheme under an SN(Shared Nothing) cluster-based parallel architecture, 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 SN parallel architecture. Finally, we show that our parallel CBF scheme achieves good retrieval performance in proportion to the number of servers in the SN architecture and it outperforms a parallel version of the VA-File when the dimensionality is over 10.


Feature Vector Range Query Query Point Retrieval Performance Master Node 
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  1. 1.
    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
  2. 2.
    Lin, H.I., Jagadish, H., Faloutsos, C.: The TV-tree: An Index Structure for High Dimensional Data. VLDB Journal 3, 517–542 (1995)CrossRefGoogle Scholar
  3. 3.
    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
  4. 4.
    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 Databases Systems, Tucson, Arizona (1997)Google Scholar
  5. 5.
    Weber, R., Schek, H.-J., Blott, S.: A Quantitative Analysis and Performance Study for Similarity-Search Methods in High- Dimensional Spaces. In: Proceedings of 24rd International Conference on Very Large Data Bases, pp. 24–27 (1998)Google Scholar
  6. 6.
    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
  7. 7.
    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
  8. 8.
    Kim, J.-K., Chang, J.-W.: Vertically-partitioned Parallel Signature File Method. Journal of Systems Architecture 46(8), 655–673 (2000)CrossRefGoogle Scholar
  9. 9.
    Roussopoulos, N., Kelley, S., Vincent, F.: Nearest Neighbor Queries. In: Proc. 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
  • Tae-Woong Wang
    • 1
  1. 1.Dept. of Computer EngineeringChonbuk National Univ.Chonju, ChonbukSouth Korea

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