Flow-loss: learning cardinality estimates that matter

Flow-loss: learning cardinality estimates that matter

<jats:p> Recently there has been significant interest in using machine learning to improve the accuracy of cardinality estimation. This work has focused on improving average estimation error, but not all estimates matter equally for downstream tasks like query optimization. Since...

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Bibliographic Details
Main Authors: Negi, Parimarjan, Marcus, Ryan, Kipf, Andreas, Mao, Hongzi, Tatbul, Nesime, Kraska, Tim, Alizadeh, Mohammad
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:English
Published: VLDB Endowment 2022
Online Access:https://hdl.handle.net/1721.1/142720

Internet

https://hdl.handle.net/1721.1/142720

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