DOT: Dynamic Knob Selection and Online Sampling for Automated Database Tuning

Summary: DOT prunes knobs via RFECV to focus optimization on influential parameters and uses a likelihood-ratio test (LRT) for online exploration–exploitation. Couples this with warm-up-free Bayesian optimization for on-the-fly tuning, matching or outperforming prior tuners while reducing overhead. (summarized by gpt-5-mini on Mar 13 2026)

Paper ID: 14343
Venue: VLDB
Year: 2026
Pagerank: 4.1945683e-05
Overall Rank: 10,301 | 28.34%
DOI: 10.14778/3785297.3785302

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Rank	Citing Paper	Year	Venue	Pagerank

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Showing 10 of 10 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank	Cited Paper	Year	Venue	Pagerank
183	Automatic Database Management System Tuning Through Large-scale Machine Learning	2017	SIGMOD	0.00036721403
1,407	DB-BERT: A Database Tuning Tool that "Reads the Manual"	2022	SIGMOD	0.00012146739
1,827	An Inquiry into Machine Learning-based Automatic Configuration Tuning Services on Real-World Database Management Systems	2021	VLDB	0.00010390548
3,114	GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimization	2024	VLDB	7.5451724e-05
3,538	Database Tuning: principles, experiments, and troubleshooting techniques	2002	SIGMOD	6.995825e-05
3,812	Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation	2022	VLDB	6.7373184e-05
4,399	HUNTER: An Online Cloud Database Hybrid Tuning System for Personalized Requirements	2022	SIGMOD	6.2225151e-05
4,913	UDO: Universal Database Optimization using Reinforcement Learning	2021	VLDB	5.8316231e-05
5,010	iTuned: A Tool for Configuring and Visualizing Database Parameters	2010	SIGMOD	5.7611118e-05
5,637	Database Workload Characterization with Query Plan Encoders	2022	VLDB	5.3979505e-05

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