Learning Transformation Rules for Semantic Query Optimization: A
Data-Driven Approach
ABSTRACT
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 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 analying the existing data in the database. This paper describes
a framwork 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 framwork.
Keywords: Rule discovery, semantic query
optimiztion, discovery in databases
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