SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning
Summary: SkinnerDB uses RL-based adaptive query processing to bound regret by cycling join orders. It layers on DBMSs or runs standalone, using timeouts and an iterative scheme to explore orders with a compact representation for fast switches. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Immanuel Trummer (Cornell University)
- 2. Samuel Moseley (Cornell University)
- 3. Deepak Maram (Cornell University)
- 4. Saehan Jo (Cornell University)
- 5. Joseph Antonakakis (Cornell University)
BibTeX Citation
@article{trummer_vldb18,
title = {{SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning}},
author = {Trummer, Immanuel and Moseley, Samuel and Maram, Deepak and Jo, Saehan and Antonakakis, Joseph},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {12},
pages = {2074--2077},
doi = {10.14778/3229863.3236263},
url = {https://doi.org/10.14778/3229863.3236263},
year = {2018}
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