IDENTIFYING APPLICATION SOFTWARE PERFORMANCE PROBLEMS USING AUTOMATED CONTENT-BASED SEMANTIC MONITORING
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United States of America
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Mar 19, 2020
app pub date -
Sep 13, 2018
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Sep 13, 2018
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Abstract
Embodiments are directed to computer-implemented methods of operating a monitoring host of a performance management infrastructure. The method includes receiving, using natural language processing (NLP) algorithms, training data that includes operational data and functionality data. The operational data represents natural language descriptions of performance problems identified for an application computer program running a computing system. The functionality data represents natural language descriptions of functional capabilities of the application computer program. The NLP algorithms and a classifier are used to extract features of the training data. The extracted features are used to build a semantic correlation model that includes correlated data sets, wherein each of the correlated data sets includes functionality data and operational data having a semantic correlation to functionality data. One of the correlated data sets is selected and used to create an application monitoring algorithm configured to submit requests to and receive responses from the application computer program.
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