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Database query performance problem determination is often performed byanalyzing query execution plans (QEPs) in addition to other performance data.As the query workloads that organizations run, have become larger and morecomplex, analyzing QEPs manually even by experts has become a very timeconsuming. Most performance diagnostic tools help with identifying problematicqueries and most query tuning tools address a limited number of known problemsand recommendations. We present the OptImatch system that offers a way to (a)look for varied user defined problem patterns in QEPs and (b) automatically getrecommendations from an expert provided and user customizable knowledge base.Existing approaches do not provide the ability to perform workload analysiswith flexible user defined patterns, as they lack the ability to impose aproper structure on QEPs. We introduce a novel semantic web system that allowsa relatively naive user to search for arbitrary patterns and to getrecommendations stored in a knowledge base either by experts or added by theuser tailored to the environment in which they operate. Our methodologyincludes transforming a QEP into an RDF graph and transforming a GUI baseduser-defined pattern into a SPARQL query through handlers. The SPARQL query ismatched against the abstracted RDF graph, and any matched portion of theabstracted RDF graph is relayed back to the user. With the knowledge base, theOptImatch system automatically scans and matches interesting stored patterns ina statistical way as appropriate and returns the corresponding recommendations.Although the knowledge base patterns and solution recommendations are not inthe context of the user supplied QEPs, the context is adapted automaticallythrough the handler tagging interface.

OptImatch: Semantic Web System with Knowledge Base for Query Performance Problem Determination