Optimization: Learning Transformation Rules for Semantic Query A Data-Driven Approach
Shashi Shekhar, Babak Hamidzadeh, Ashim Kohli, Mark Coyle · 1993
Absfrucf-Learning query-transformation rules are vital for the success of semantic query optimization in domains where the user cannot provide a comprehensive set of integrity constraints. Finding these rules is a discovery task because of the lack of targets. Previous approaches to learning query-transformation rules have been based on analyzing past queries. We propose a new approach to learning query-transformation rules based on analyzing the existing data in the database. This paper describes a framework and a closure algorithm for learning rules from a given data distribution. We characterize the correctness, completeness, and complexity of the proposed algorithm and provide a detailed example to illustrate the framework. Index Terms-Data, discovery in databases, learning, rule discovery, semantic query optimization.