Compressed histograms with arbitrary bucket layouts for selectivity estimation

Selectivity estimation is an important step of query optimization in a database management system, and multidimensional histogram techniques have proved promising for selectivity estimation. Recent multidimensional histogram techniques such as GenHist and STHoles use an arbitrary bucket layout. This layout has the advantage of requiring a smaller number of buckets to model tuple densities than those required by the traditional grid or recursive layouts. However, the arbitrary bucket layout brings an inherent disadvantage of requiring more memory to store each bucket location information. This diminishes the advantage of requiring fewer buckets and, therefore, has an adverse effect on the resulting selectivity estimation accuracy. To our knowledge, however, no existing histogram-based technique with arbitrary layout addresses this issue. In this paper, we introduce the idea of bucket location compression and then demonstrate its effectiveness for improving selectivity estimation accura...

• Type: Journal
• Year: 2007
• Where: ISCI
• Authors: Dennis Fuchs, Zhen He, Byung Suk Lee
• Keywords: Buckets, ISCI 2007, Multidimensional Histogram Techniques, Selectivity Estimation