PARQO: Penalty-Aware Robust Plan Selection in Query Optimization
Summary: PARQO lets DBAs specify penalty-based robustness metrics to evaluate a plan's expected loss versus the true optimum under selectivity uncertainty. It builds workload-informed selectivity error models and uses sensitivity analysis to surface interpretable selectivity dimensions and choose robust, parametric plans. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Haibo Xiu (Duke University)
- 2. Pankaj K. Agarwal (Duke University)
- 3. Jun Yang (Duke University)
BibTeX Citation
@article{xiu_vldb24,
title = {{PARQO: Penalty-Aware Robust Plan Selection in Query Optimization}},
author = {Xiu, Haibo and Agarwal, Pankaj K. and Yang, Jun},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {13},
pages = {4627--4640},
doi = {10.14778/3704965.3704971},
url = {https://doi.org/10.14778/3704965.3704971},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,971 | Debunking the Myth of Join Ordering: Toward Robust SQL Analytics | 2025 | SIGMOD | 6.3209003e-05 |
| 9,753 | A Practical Theory of Generalization in Selectivity Learning | 2025 | VLDB | 5.1159926e-05 |
| 9,756 | APQO: An Adaptive Framework for Parametric Query Optimization | 2026 | SIGMOD | 5.115551e-05 |
| 9,767 | PAR2QO: Parametric Penalty-Aware Robust Query Optimization | 2025 | VLDB | 5.115551e-05 |
| 10,446 | Coresets for Robust Query Optimization | 2026 | PODS | 4.9605343e-05 |
| 10,725 | InferF: Declarative Factorization of AI/ML Inferences over Joins | 2026 | SIGMOD | 4.9605343e-05 |
| 10,812 | OBELISK: Efficient Offline Query Planning with Bayesian Optimization-Informed Language Model Reasoning | 2026 | VLDB | 4.9605343e-05 |
| 11,458 | Hint-QPT: Hints for Robust Query Performance Tuning | 2025 | VLDB | 4.9605343e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 36 of 36 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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