Spatial selectivity estimation using compressed histogram information

Authors:
Jeong Hee Chi;Sang Ho Kim;Keun Ho Ryu
Affiliations:
Database Laboratory, Chungbuk National University, Korea;Database Laboratory, Chungbuk National University, Korea;Database Laboratory, Chungbuk National University, Korea
Venue:
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Year:
2005

Citing 7
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Abstract

Selectivity estimation for spatial query is very important process in finding the most efficient execution plan. Many works have been performed to estimate accurate selectivity. However, the existing works require a large amount of memory to retain accurate selectivity, and these works can not get good results in little memory environments such as mobile-based small database. In order to solve this problem, we propose a new technique called MW histogram which is able to compress summary data and get reasonable results. The proposed method is based on the spatial partitioning algorithm of MinSkew histogram and wavelet transformation. The experimental results showed that the MW histogram has lower relative error than MinSkew histogram and gets a good selectivity in little memory.