A Model Based on Statistical Machine Learning for Determining Factors that Influence the Performance of Relational Database Management Systems
DOI:
https://doi.org/10.26439/ciis2020.5511Keywords:
SQL, relational database management system, principal component analysis, canonical correlation analysis, statistical machine learning algorithm, performance of SQL statements executions, performance managementAbstract
Business processes supporting their operations with applications that interact with relational database management systems (RDBMS) may increase their productivity through the identification of factors that affect the performance of SQL statement execu tions of any given workload, especially workloads generated by applications implemented in production environments which recur over time. This paper proposes a model to identify factors that affect the performance of SQL statement executions processed in RDBMS, using statistical machine learning algorithms (principal component analysis and canonical correla tion analysis) that exploit the information of the plans, statistics and metrics generated during the life cycle of SQL statement executions.
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