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E-LQO: A Comprehensive Energy Evaluation Framework for Learned Query Optimization
Summary: E-LQO evaluates energy across the full learned-query-optimization lifecycle, introducing EROI and energy payback time alongside latency. Across seven methods, supervision strategy—not learned vs. non-learned design—drives energy debt; execution-based methods peak near 50% template coverage.
(summarized by gpt-6-luna on Oct 22 2026)
Paper ID
hdfe0bc2ac02c8e66
Venue
SIGMOD
Year
2027
Pagerank
4.9605343e-05
Overall Rank
10,392 | 30.35%
DOI
10.1145/3837107
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(CC BY 4.0)
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Authors
1.
Zibo Liang
(University of Electronic Science and Technology of China)
2.
Quanqing Xu
(Independent)
3.
Xu Chen
(University of Electronic Science and Technology of China)
4.
Junming Chen
(University of Electronic Science and Technology of China)
5.
Yuyang Xia
(University of Electronic Science and Technology of China)
6.
Kai Zheng
(University of Electronic Science and Technology of China)
BibTeX Citation
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@inproceedings{liang_sigmod27,
title = {{E-LQO: A Comprehensive Energy Evaluation Framework for Learned Query Optimization}},
author = {Liang, Zibo and Xu, Quanqing and Chen, Xu and Chen, Junming and Xia, Yuyang and Zheng, Kai},
series = {{SIGMOD} '27},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3837107},
url = {https://dl.acm.org/doi/10.1145/3837107},
year = {2027}
}
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Outgoing Citations (Sorted by Pagerank)
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