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SIGMOD
1999
ACM

Multi-dimensional Selectivity Estimation Using Compressed Histogram Information

11 years 9 months ago
Multi-dimensional Selectivity Estimation Using Compressed Histogram Information
The database query optimizer requires the estimation of the query selectivity to find the most efficient access plan. For queries referencing multiple attributes from the same relation, we need a multi-dimensional selectivity estimation technique when the attributes are dependent each other because the selectivity is determined by the joint data distribution of the attributes. Additionally, for multimedia databases, there are intrinsic requirements for the multi-dimensional selectivity estimation because feature vectors are stored in multi-dimensional indexing trees. In the 1-dimensional case, a histogram is practically the most preferable. In the multi-dimensional case, however, a histogram is not adequate because of high storage overhead and high error rates. In this paper, we propose a novel approach for the multidimensional selectivity estimation. Compressed information from a large number of small-sized histogram buckets is maintained using the discrete cosine transform. This ena...
Ju-Hong Lee, Deok-Hwan Kim, Chin-Wan Chung
Added 03 Aug 2010
Updated 03 Aug 2010
Type Conference
Year 1999
Where SIGMOD
Authors Ju-Hong Lee, Deok-Hwan Kim, Chin-Wan Chung
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