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MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration
Summary: MCTuner uses a Mixture-of-Experts of LLMs to pinpoint performance‑critical knobs and avoid ineffective regions in high‑dimensional config spaces. Its recursive spatial decomposition runs Bayesian optimization on hierarchical subspaces, yielding up to 19.2% gains and 1.4× faster discovery versus SOTA.
(summarized by gpt-5-mini on Feb 11 2026)
Paper ID
h7d7e1f2c79122917
Venue
SIGMOD
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,580 | 28.87%
DOI
10.1145/3769807
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
1.
Zihan Yan
(University of Electronic Science and Technology of China)
2.
Rui Xi
(University of Electronic Science and Technology of China)
3.
Mengshu Hou
(University of Electronic Science and Technology of China)
BibTeX Citation
Copy BibTeX
@inproceedings{yan_sigmod26,
title = {{MCTuner: Spatial Decomposition-Enhanced Database Tuning via LLM-Guided Exploration}},
author = {Yan, Zihan and Xi, Rui and Hou, Mengshu},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769807},
url = {https://dl.acm.org/doi/10.1145/3769807},
year = {2026}
}
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Outgoing Citations (Sorted by Pagerank)
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