OptImatch: Semantic web system for query problem determination

Abstract

Query performance problem determination is usually performed by analyzing query execution plans (QEPs). Analyzing complex QEPs is excessively time consuming and existing automatic problem determination tools do not provide ability to perform analysis with flexible user-defined problem patterns. We present the novel OptImatch system that allows a relatively naive user to search for patterns in QEPs and get recommendations from an expert and user customizable knowledge base. Our system transforms a QEP into an RDF graph. We provide a web graphical interface for the user to describe a pattern that is transformed with handlers into a SPARQL query. The SPARQL query is matched against the abstracted RDF graph and any matched parts of the graph are relayed back to the user. With the knowledge base the system automatically matches stored patterns to the QEPs by adapting dynamic context through developed tagging language and ranks recommendations using statistical correlation analysis.