Block Histogram Compression Method for Selectivity Estimation in High-dimensions


The KIPS Transactions:PartD, Vol. 10, No. 6, pp. 927-934, Oct. 2003
10.3745/KIPSTD.2003.10.6.927,   PDF Download:

Abstract

Database query optimizer estimates the selectivity of a query to find the most efficient access plan. Multi-dimensional selectivity estimation technique is required for a query with multiple attributes because the attributes are not independent each other. Histogram is practically used in most commercial database products because it approximates data distributions with small overhead and small error rates. However, histogram method for multi-dimensional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This enables low error rates and low storage overheads even in high dimensions. Extensive experimental results show advantages of the proposed approach


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Cite this article
[IEEE Style]
L. J. Hong, J. S. Ju, P. Seon, "Block Histogram Compression Method for Selectivity Estimation in High-dimensions," The KIPS Transactions:PartD, vol. 10, no. 6, pp. 927-934, 2003. DOI: 10.3745/KIPSTD.2003.10.6.927.

[ACM Style]
Lee Ju Hong, Jeon Seog Ju, and Park Seon. 2003. Block Histogram Compression Method for Selectivity Estimation in High-dimensions. The KIPS Transactions:PartD, 10, 6, (2003), 927-934. DOI: 10.3745/KIPSTD.2003.10.6.927.