PAR2QO: Parametric Penalty-Aware Robust Query Optimization
Summary: PAR2QO: probe workload-informed selectivities, cache plan–penalty profiles, pick runtime plans minimizing expected penalty under selectivity uncertainty. Adds CARVER, a subquery-cardinality-aware generator for better PQO evaluation/training; yields substantial speedups and improved robustness. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Haibo Xiu (Duke University)
- 2. Yang Li (Duke University)
- 3. Qianyu Yang (Duke University)
- 4. Pankaj K. Agarwal (Duke University)
- 5. Jun Yang (Duke University)
BibTeX Citation
@article{xiu_vldb25,
title = {{PAR2QO: Parametric Penalty-Aware Robust Query Optimization}},
author = {Xiu, Haibo and Li, Yang and Yang, Qianyu and Agarwal, Pankaj K. and Yang, Jun},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {4532--4545},
doi = {10.14778/3749646.3749711},
url = {https://doi.org/10.14778/3749646.3749711},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,151 | Coresets for Robust Query Optimization | 2026 | PODS | 5.093636e-05 |
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