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ENABLING EFFICIENT MACHINE LEARNING MODEL INFERENCE USING ADAPTIVE SAMPLING FOR AUTONOMOUS DATABASE SERVICES
ENABLING EFFICIENT MACHINE LEARNING MODEL INFERENCE USING ADAPTIVE SAMPLING FOR AUTONOMOUS DATABASE SERVICES
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机译:使用自动数据库服务的自适应采样启用有效的机器学习模型推断
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摘要
Herein are approaches for self-optimization of a database management system (DBMS) such as in real time. Adaptive just-in-time sampling techniques herein estimate database content statistics that a machine learning (ML) model may use to predict configuration settings that conserve computer resources such as execution time and storage space. In an embodiment, a computer repeatedly samples database content until a dynamic convergence criterion is satisfied. In each iteration of a series of sampling iterations, a subset of rows of a database table are sampled, and estimates of content statistics of the database table are adjusted based on the sampled subset of rows. Immediately or eventually after detecting dynamic convergence, a machine learning (ML) model predicts, based on the content statistic estimates, an optimal value for a configuration setting of the DBMS.
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