Learning Transformation Rules for Semantic Query Optimization: A Data-Driven Approach

Learning query transformation rules is 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 target. 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 to 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.