Hint-QPT: Hints for Robust Query Performance Tuning
Summary: Hint-QPT: an interactive system that models selectivity uncertainty to recommend and visualize robust execution plans (plan-cost distributions) rather than relying on point estimates. Identifies sensitivity hotspots for targeted selectivity acquisition or tuning so users can pick robust plans or selectively collect stats to fix optimizer errors. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Authors
- 1. Haibo Xiu
- 2. Yang Li
- 3. Qianyu Yang
- 4. Weihang Guo
- 5. Yuxi Liu
- 6. Pankaj K. Agarwal
- 7. Sudeepa Roy
- 8. Jun Yang
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,751 | PAR2QO: Parametric Penalty-Aware Robust Query Optimization | 2025 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 71 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00059038975 |
| 1,070 | Analyzing Plan Diagrams of Database Query Optimizers | 2005 | VLDB | 0.00014316791 |
| 5,780 | MOCHA: A Tool for Visualizing Impact of Operator Choices in Query Execution Plans for Database Education | 2022 | VLDB | 5.3298375e-05 |
| 8,448 | PARQO: Penalty-Aware Robust Plan Selection in Query Optimization | 2024 | VLDB | 4.5100508e-05 |
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| 6,278 | Uncertainty Aware Query Execution Time Prediction | 2014 | VLDB | 5.1309442e-05 |
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