HASE: A Hybrid Approach to Selectivity Estimation for Conjunctive Predicates

Current methods for selectivity estimation fall into two broad categories, synopsis-based and sampling-based. Synopsis-based methods, such as histograms, incur minimal overhead at query optimization time and thus are widely used in commercial database systems. Samplingbased methods are more suited for ad-hoc queries, but often involve high I/O cost because of random access to the underlying data. Though both methods serve the same purpose of selectivity estimation, their interaction in the case of selectivity estimation for conjuncts of predicates on multiple attributes is largely unexplored. Our work aims at taking the best of both worlds, by making consistent use of synopses and sample information when they are both present. To achieve this goal, we propose HASE, a novel estimation scheme based on a powerful mechanism called generalized raking. We formalize selectivity estimation in the presence of single attribute synopses and sample information as a constrained optimization problem...

• Type: Conference
• Year: 2006
• Where: EDBT
• Authors: Xiaohui Yu, Nick Koudas, Calisto Zuzarte
• Keywords: Constrained Optimization Problem, Database, EDBT 2006, Selectivity Estimation, Selectivity Estimation Fall