Query Selectivity Estimation via Data Mining

Estimating the result size of a join is an important query optimization problem as it determines the choice of a good query evaluation strategy. Yet, there are few efficient techniques that solve this problem. We propose a new approach to join selectivity estimation. Our strategy relies on information extracted from stored data in the form of empty joins which represent portions of the two joined tables that produce an empty result. We present experimental results indicating that empty joins are common in real data sets and propose a simple strategy that uses information about empty joins for an improved join selectivity estimation.

• Type: Conference
• Year: 2004
• Where: IIS
• Authors: Jarek Gryz, Dongming Liang
• Keywords: IIS 2004, Information Technology, Join Selectivity Estimation, Query Evaluation Strategy, Query Optimization Problem