DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning
Summary: DOT dynamically prunes unimportant DBMS knobs via RFECV and balances exploration/exploitation with an LRT-driven online sampler. Bayesian optimization tunes configurations without costly warm-up phases or prior knowledge, reducing overhead while matching or exceeding state-of-the-art tuners. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yifan Wang (INRIA; Orange; University of Lille)
- 2. Debabrota Basu (CNRS; INRIA; University of Lille)
- 3. Pierre Bourhis (CNRS; INRIA; University of Lille)
- 4. Romain Rouvoy (CNRS; INRIA; University of Lille)
- 5. Patrick Royer (Orange)
BibTeX Citation
@article{wang_vldb26,
title = {{DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning}},
author = {Wang, Yifan and Basu, Debabrota and Bourhis, Pierre and Rouvoy, Romain and Royer, Patrick},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {4},
pages = {589--602},
doi = {10.14778/3785297.3785302},
url = {https://doi.org/10.14778/3785297.3785302},
year = {2026}
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