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http://dx.doi.org/10.3745/KIPSTD.2003.10D.6.927

Block Histogram Compression Method for Selectivity Estimation in High-dimensions  

Lee, Ju-Hong (인하대학교 컴퓨터공학부)
Jeon, Seok-Ju (안산1대학 인터넷정보과)
Park, Seon (인하대학교 대학원 전자계산공학과)
Publication Information
The KIPS Transactions:PartD / v.10D, no.6, 2003 , pp. 927-934 More about this Journal
Abstract
Database query optimates the selectivety 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 is inadequate for a query with multiple attributes because it incurs high storage overhead and high error rates. In this paper, we propose a novel method for multi-dimentional 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 adventages of the proposed approach.
Keywords
Database Query Optimizer; Black Partitioned Histogram; Multi-dimension Histogram;
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