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 4 of 4 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,744 | SQLStorm: Taking Database Benchmarking into the LLM Era | 2025 | VLDB | 6.1019672e-05 |
| 8,163 | Learned Offline Query Planning via Bayesian Optimization | 2025 | SIGMOD | 5.4751517e-05 |
| 10,128 | Survivorship Bias in Industrial Database Workloads | 2026 | CIDR | 5.093636e-05 |
| 10,559 | OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning | 2026 | VLDB | 5.093636e-05 |
Previous
Page 1 / 1
Next
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.
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 4,434 | LEON: A New Framework for ML-Aided Query Optimization | 2023 | VLDB |
| 2 | 5,340 | Machine Learning for Databases | 2021 | VLDB |
| 3 | 11,103 | RankPQO: Learning-to-Rank for Parametric Query Optimization | 2025 | VLDB |
| 4 | 2,420 | Lero: A Learning-to-Rank Query Optimizer | 2023 | VLDB |
| 5 | 6,271 | Is Your Learned Query Optimizer Behaving As You Expect? A Machine Learning Perspective | 2024 | VLDB |
| 6 | 3,051 | Towards a Hands-Free Query Optimizer through Deep Learning | 2019 | CIDR |
| 7 | 6,088 | How Good are Learned Cost Models, Really? Insights from Query Optimization Tasks | 2025 | SIGMOD |
| 8 | 5,394 | Leveraging Query Logs and Machine Learning for Parametric Query Optimization | 2022 | VLDB |
| 9 | 8,163 | Learned Offline Query Planning via Bayesian Optimization | 2025 | SIGMOD |
| 10 | 9,428 | LIMAO: A Framework for Lifelong Modular Learned Query Optimization | 2025 | VLDB |