Adaptive Selectivity Estimation Using Query Feedback

Abstract

In this paper, we propose a novel approach for estimating the record selectivities of database queries. The real attribute value distribution is adaptively approximated by a curve-fitting function using a query feedback mechanism. This approach has the advantage of requiring no extra database access overhead for gathering statistics and of being able to continuously adapt the value distribution through queries and updates. Experimental results show that the estimation accuracy of this approach is comparable to traditional methods based on statistics gathering.