Recognition model of variables that influence the performance of RDBMS transactions applying PCA and PCR
DOI:
https://doi.org/10.26439/ciis2021.5582Keywords:
RDBMS, Statistical Machine Learning Algorithm, Principal Component Analysis, PCA, Principal Components Regression, PCR, SQL Performanc, Performance ManagementAbstract
Knowledge of the factors that influence the efficiency of a system is essential for its administration and maintenance. Likewise, various organizations support their operations with applications that interact with a Relational Database Management System (RDBMS), which can improve their efficiency through knowledge of the factors that influence the performance of SQL statement executions. That makes up the workload, especially the workloads generated by applications implemented in Production environments that show recurrence over time. The present research article proposes a model for the recognition of factors that
affect the performance of the executions of the SQL statements that are processed in an RDBMS and discusses the implementation of a performance metric prediction technique, using algorithms of statistical machine learning called Principal Component Analysis (PCA) and Principal Components Regression (PCR), which exploit the information of the plans, statistics, and metrics generated during the life cycle of the executions of the SQL sentences.
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