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