Low Rank Learning for Offline Query Optimization
Summary: LimeQO leverages offline low-rank learning and linear models to predict query-plan latencies, avoiding heavy neural nets. Active-learning exploration halves time (3h→1.5h); a TCNN variant yields similar gains in 0.5h, DBMS-agnostic, no regressions. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zixuan Yi (University of Pennsylvania)
- 2. Yao Tian (Hong Kong University of Science and Technology)
- 3. Zachary G. Ives (University of Pennsylvania)
- 4. Ryan Marcus (University of Pennsylvania)
BibTeX Citation
@inproceedings{yi_sigmod25,
title = {{Low Rank Learning for Offline Query Optimization}},
author = {Yi, Zixuan and Tian, Yao and Ives, Zachary G. and Marcus, Ryan},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725412},
url = {https://dl.acm.org/doi/10.1145/3725412},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,909 | SQLStorm: Taking Database Benchmarking into the LLM Era | 2025 | VLDB | 6.9127576e-05 |
| 7,561 | Learned Offline Query Planning via Bayesian Optimization | 2025 | SIGMOD | 5.4784915e-05 |
| 10,429 | Survivorship Bias in Industrial Database Workloads | 2026 | CIDR | 4.9605343e-05 |
| 10,812 | OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning | 2026 | VLDB | 4.9605343e-05 |
| 11,011 | Ultron: History-Based Query Optimization at Databricks | 2026 | VLDB | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 40 of 40 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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