Abstract
Among the many issues in high dimensional index structures using Minimum Bounding Rectangle(MBR), the reduction of fan-out and increase of overlapping area are the key factors in reduction of search speed. It is known that the usage of only minimum and maximum distance in MBR’s pruning process lowers the accuracy of search. In this paper, we present an index structure using cell based MBR in which fan-out gets increased and overlapping is avoided, and a search algorithm which reflects the distribution status of data in MBR to the search. The proposed index structure produces MBR as Vector Approximation-file(VA-file)’s cell units and produces child-MBR by dividing cells. The search algorithm raises the search accuracy by executing pruning using centroid of values included in MBR other than the minimum and maximum distance of cell based MBR and query vector in the k-nn query concerned. Through experiment, we find that the proposed search algorithm has improved its search speed and its accuracy in comparison with existing algorithm.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Sakurai., Y., Yoshikawa., M., Uemura., S., Kojima, H.: The A-tree: An Index Structure for High Dimensional Spaces Using Relative Approximation. In: Proceedings on the 26th VLDB Conference, Egypt, pp. 516–526 (2000)
Sakurai., Y., Yoshikawa., M., Uemura., S., Kojima, H.: The A-tree: An Index Structure for High-Dimensional Spaces Using Relative Approximation. Technical report, Nara Institute of Science and Technology, pp.1–22 (2000)
Beckmann, N., Kriegel, H.-P., Schneider, R., Seeger, B.: The R*-tree:An Efficient and Robust Access Method for Points and Rectangles. In: Proceedings on ACM SIGMOD International Conference on Management of Data, Atlantic City, NJ, pp. 322–331 (1990)
Katayama., N., Satoh, S.: The SR-tree: An index structure for high-dimensional nearest neighbor queries. In: Proceedings on the ACM SIGMOD International Conference on Management of Data, Tucson, Arizon USA, vol. 26(2), pp. 369–380 (1997)
Weber, R., Schek, H., Blott, S.: A quantitative analysis and performance study for similarity-search methods in highdimensional spaces. In: Proceedings on the 24th VLDB Conference, New York City, pp. 194–205 (1998)
White., D.A., Jain, R.: Similarity Indexing with the SS-tree. In: Proceedings Of the 12th International Conference on Data Engineering, New Orleans, USA, pp. 516–523 (1996)
Roussopoulos., N., Kelley., S., Vincent, F.: Nearest Neighbor Queries. In: Proceedings ACM SIGMOD, San Jose, USA, pp. 71–79 (1995)
Berchtold, S., Keim, D.A., Kriegel, H.-P.: The X-tree: An Index Structure for High-Dimensional Data. In: Proceedings on the 22nd VLDB Conference, Bombay, pp. 28–39 (1996)
Lin, K., Jagadish, H.V., Faloutsos, C.: The TV-tree: An Index Structure for High-Dimensional Data. VLDB Journal 3(4), 517–542 (1994)
Berchtold, S., Bohm, C., Kriegel, H.-P.: The Pyramid-Technique: Towards Breaking the Curse of Dimensionality. In: ACM SIGMOD, Seattle, vol. 27(2), pp. 142–153 (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, BH., Lee, BW. (2006). An Efficient Search Algorithm for High-Dimensional Indexing Using Cell Based MBR. In: Gavrilova, M., et al. Computational Science and Its Applications - ICCSA 2006. ICCSA 2006. Lecture Notes in Computer Science, vol 3980. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11751540_103
Download citation
DOI: https://doi.org/10.1007/11751540_103
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34070-6
Online ISBN: 978-3-540-34071-3
eBook Packages: Computer ScienceComputer Science (R0)