MOSE: A Monotonic Selectivity Estimator Using Learned CDF (Extended abstract)

2022 IEEE 38TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2022)（2022）

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摘要

The accuracy of selectivity estimation is of vital importance to create good query plans in database management systems. We propose MOSE, a learning-based MOnotonic Selectivity Estimator, to provide accurate, reliable, and efficient selectivity estimation for query optimization.

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关键词

database management systems,learned CDF,learning-based monotonic selectivity estimator,MOSE

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