Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: query performance problem determination is often performed by analyzing query execution plans (QEPs) in addition to other performance data. As the query workloads that organizations run have become larger and more complex, analyzing QEPs manually even by experts has become a very time consuming and cumbersome task. Most performance diagnostic tools help with identifying problematic queries and most query tuning tools address a limited number of known problems and recommendations.  present the OptImatch system that offers a way to (a) look for varied user defined problem patterns in QEPs and (b) automatically get recommendations from an expert provided and user customizable knowledge base. Existing approaches do not provide the ability to perform workload analysis with flexible user defined patterns, as they lack the ability to impose a proper structure on QEPs.  introduce a novel semantic b system that allows a relatively naive user to search for arbitrary patterns and to get solution recommendations stored in a knowledge base. Our methodology includes transforming a QEP into an RDF graph and transforming a GUI based user-defined pattern into a SPARQL query through handlers. The SPARQL query is matched against the abstracted RDF graph, and any matched portion of the abstracted RDF graph is relayed back to the user. With the knowledge base, the OptImatch system automatically scans and matches interesting stored patterns in a statistical way as appropriate and returns the corresponding recommendations. Although the knowledge base patterns and solution recommendations are not in the context of the user supplied QEPs, the context is adapted automatically through the handler tagging interface.  test the performance and scalability of our framework to demonstrate its efficiency using a real query workload.  also perform a user study to quantify the benefits of the approach in terms of precision and time compared to manually searching for patterns.
['Guilherme Damasio', 'Piotr Mierzejewski', 'Jaroslaw Szlichta', 'Calisto Zuzarte']
I am writing a research paper on a idea Query Performance Problem Determination with Knowledge Base in Semantic Web System OptImatch
[{"from": "human", "value": "I am writing a research paper on a idea Query Performance Problem Determination with Knowledge Base in Semantic Web System OptImatch"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: query performance problem determination is often performed by analyzing query execution plans (QEPs) in addition to other performance data. As the query workloads that organizations run have become larger and more complex, analyzing QEPs manually even by experts has become a very time consuming and cumbersome task. Most performance diagnostic tools help with identifying problematic queries and most query tuning tools address a limited number of known problems and recommendations.  present the OptImatch system that offers a way to (a) look for varied user defined problem patterns in QEPs and (b) automatically get recommendations from an expert provided and user customizable knowledge base. Existing approaches do not provide the ability to perform workload analysis with flexible user defined patterns, as they lack the ability to impose a proper structure on QEPs.  introduce a novel semantic b system that allows a relatively naive user to search for arbitrary patterns and to get solution recommendations stored in a knowledge base. Our methodology includes transforming a QEP into an RDF graph and transforming a GUI based user-defined pattern into a SPARQL query through handlers. The SPARQL query is matched against the abstracted RDF graph, and any matched portion of the abstracted RDF graph is relayed back to the user. With the knowledge base, the OptImatch system automatically scans and matches interesting stored patterns in a statistical way as appropriate and returns the corresponding recommendations. Although the knowledge base patterns and solution recommendations are not in the context of the user supplied QEPs, the context is adapted automatically through the handler tagging interface.  test the performance and scalability of our framework to demonstrate its efficiency using a real query workload.  also perform a user study to quantify the benefits of the approach in terms of precision and time compared to manually searching for patterns."}]