Neural network detection of resource-intensive database queries
Development of methods and algorithms for clustering database queries based on self-organizing Kohonen maps
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The server information processing system spends an unacceptably large amount of computational resources, as well as time and storage resources, to execute resource-intensive database queries. Detection and correction of resource-intensive queries can improve the speed of software client-oriented applications by reducing the overall load on database servers, reducing competition for shared resources, and reducing the execution time of other queries. Currently used methods of searching problematic queries, used in system utilities, do not always allow identifying resource-intensive SQL statement...
The server information processing system spends an unacceptably large amount of computational resources, as well as time and storage resources, to execute resource-intensive database queries. Detection and correction of resource-intensive queries can improve the speed of software client-oriented applications by reducing the overall load on database servers, reducing competition for shared resources, and reducing the execution time of other queries. Currently used methods of searching problematic queries, used in system utilities, do not always allow identifying resource-intensive SQL statements or skip those queries that can also be categorized as resource-intensive. The monograph is aimed at improving the process of detecting resource-intensive database queries based on the application of neural network clustering and fuzzy inference.
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