Configuration-level pruning aims to reduce the optimization time for machine learning-based algorithms by identifying and focusing on the most impactful configuration parameters in DBMS.
In utilizing the Spearman Rank Correlation Coefficient (SRCC), our approach effectively filters out redundant parameters, streamlining the configuration process and enhancing tuning efficiency.
Principal Component Analysis (PCA) serves as a crucial tool in our methodology, allowing for dimensionality reduction by transforming correlated parameters into uncorrelated principal components, thereby simplifying the tuning process.
Understanding the key configuration parameters that significantly impact database management systems’ performance is essential for effective tuning and can lead to considerable improvements in optimization.
#configuration-tuning #dbms-optimization #machine-learning #parameter-pruning #data-analysis-techniques
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