A Knowledge Representation and Data Provenance Model to Self-Tuning Database Systems

Abstract- Most autonomic database systems do not explicit their decision rationale behind tuning activities. Consequently, users may not trust some of the automatic tuning decisions. In this paper we propose a rather transparent strategy, that provides feedback to database administrators, based on information extracted from the database log. The proposed approach consists in transforming log results into a user-friendly knowledge representation, based on the graphical representation for OWL. This model provides users with the rationale behind system decisions, adds semantics to the database self-tuning actions, and provides useful provenance information about the whole process.