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The pyramid-technique: towards breaking the curse of dimensionality
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Source International Conference on Management of Data archive
Proceedings of the 1998 ACM SIGMOD international conference on Management of data table of contents
Seattle, Washington, United States
Pages: 142 - 153  
Year of Publication: 1998
ISBN:0-89791-995-5
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Authors
Stefan Berchtold  AT&T Labs Research, Florham Park, NJ
Christian Böhm  University of Munich, Germany
Hans-Peter Kriegal  University of Munich, Germany
Sponsors
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
SIGMOD: ACM Special Interest Group on Management of Data
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 16,   Downloads (12 Months): 144,   Citation Count: 89
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ABSTRACT

In this paper, we propose the Pyramid-Technique, a new indexing method for high-dimensional data spaces. The Pyramid-Technique is highly adapted to range query processing using the maximum metric Lmax. In contrast to all other index structures, the performance of the Pyramid-Technique does not deteriorate when processing range queries on data of higher dimensionality. The Pyramid-Technique is based on a special partitioning strategy which is optimized for high-dimensional data. The basic idea is to divide the data space first into 2d pyramids sharing the center point of the space as a top. In a second step, the single pyramids are cut into slices parallel to the basis of the pyramid. These slices from the data pages. Furthermore, we show that this partition provides a mapping from the given d-dimensional space to a 1-dimensional space. Therefore, we are able to use a B+-tree to manage the transformed data. As an analytical evaluation of our technique for hypercube range queries and uniform data distribution shows, the Pyramid-Technique clearly outperforms index structures using other partitioning strategies. To demonstrate the practical relevance of our technique, we experimentally compared the Pyramid-Technique with the X-tree, the Hilbert R-tree, and the Linear Scan. The results of our experiments using both, synthetic and real data, demonstrate that the Pyramid-Technique outperforms the X-tree and the Hilbert R-tree by a factor of up to 14 (number of page accesses) and up to 2500 (total elapsed time) for range queries.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Bayer R., McCreight E.M.: 'Organization and Maintenance of Large Ordered Indices', Acta Informatica 1(3), 1977, pp. 173-189.
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Berchtold S., B0hm C., Keim D., Kriegel H.-P., Xu X.:'Optimal Multidimensional Query Processing Using Tree Striping', submitted.
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Beyer K., Goldstein J., Ramakrishnan R., Shaft U..: 'When Is "Nearest Neighbor" Meaningful?', submitted for publication, 1998.
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Jain R, White D.A.: 'Similarity indexing: Algorithms and Performance', Proc. SPIE Storage and Retrieval for Image and Video Databases IV, Vol. 2670, San Jose, CA, 1996, pp. 62-75.
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CITED BY  89

Collaborative Colleagues:
Stefan Berchtold: colleagues
Christian Böhm: colleagues
Hans-Peter Kriegal: colleagues