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- W3150554837 abstract "A large body of knowledge on database tuning is available in the form of natural language text. We propose to leverage natural language processing (NLP) to make that knowledge accessible to automated tuning tools. We describe multiple avenues to exploit NLP for database tuning, and outline associated challenges and opportunities. As a proof of concept, we describe a simple prototype system that exploits recent NLP advances to mine tuning hints from Web documents. We show that mined tuning hints improve performance of MySQL and Postgres on TPC-H, compared to the default configuration." @default.
- W3150554837 created "2021-04-13" @default.
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- W3150554837 date "2021-03-01" @default.
- W3150554837 modified "2023-10-16" @default.
- W3150554837 title "The case for NLP-enhanced database tuning" @default.
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- W3150554837 doi "https://doi.org/10.14778/3450980.3450984" @default.
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