This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch!
William Zhang; Wan Shen Lim; Andrew Pavlo · 2026 · Proceedings of the ACM on Management of Data
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Tuning database management systems (DBMSs) is challenging due to trillions of possible configurations and evolving workloads. Recent advances in tuning have led to breakthroughs in optimizing over the possible configurations. However, due…
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WASTE indexes this work — it does not host or republish it. Failure-type classification is automated and approximate.
Metadata source: Crossref · DOI 10.1145/3786704