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AniPQO: Almost Non-intrusive Parametric Query Optimization for Nonlinear Cost Functions
Summary: AniPQO is a heuristic parametric query optimization technique for nonlinear cost functions. It is almost non-intrusive, reuses an existing optimizer with minor modifications, scales to four parameters, and yields region-aware plans that perform well on TPC-D.
(summarized by gpt-5-nano on Feb 09 2026)
- Paper ID
- 9023
- Venue
- VLDB
- Year
- 2003
- Pagerank
- 9.8491124e-05
- Overall Rank
- 1,990 | 86.18%
- DOI
-
-
Incoming Non-self Citations Over Time
Incoming Citations (Sorted by Pagerank)
Showing 21 of 21 citing papers.
| Rank |
Citing Paper |
Year |
Venue |
Pagerank |
| 1,064 |
Analyzing Plan Diagrams of Database Query Optimizers |
2005 |
VLDB |
0.00014348262 |
| 1,268 |
Proactive Re-Optimization |
2005 |
SIGMOD |
0.00012914584 |
| 1,297 |
The Picasso Database Query Optimizer Visualizer |
2010 |
VLDB |
0.00012732768 |
| 2,479 |
Efficient Use of the Query Optimizer for Automated Physical Design |
2007 |
VLDB |
8.6836615e-05 |
| 2,652 |
Multi-Objective Parametric Query Optimization |
2015 |
VLDB |
8.3662031e-05 |
| 3,343 |
Toward Computational Fact-Checking |
2014 |
VLDB |
7.196079e-05 |
| 3,476 |
Solving the Join Ordering Problem via Mixed Integer Linear Programming |
2017 |
SIGMOD |
7.0560383e-05 |
| 4,052 |
Optimizing Nested Queries with Parameter Sort Orders |
2005 |
VLDB |
6.4923e-05 |
| 4,342 |
Identifying Robust Plans through Plan Diagram Reduction |
2008 |
VLDB |
6.2680535e-05 |
| 4,618 |
Adaptive Query Processing in the Looking Glass |
2005 |
CIDR |
6.039398e-05 |
| 5,344 |
Efficiently Approximating Query Optimizer Plan Diagrams |
2008 |
VLDB |
5.5579641e-05 |
| 5,476 |
On the Production of Anorexic Plan Diagrams |
2007 |
VLDB |
5.4866057e-05 |
| 6,471 |
Leveraging Re-costing for Online Optimization of Parameterized Queries with Guarantees |
2017 |
SIGMOD |
5.0438582e-05 |
| 6,561 |
On the Stability of Plan Costs and the Costs of Plan Stability |
2010 |
VLDB |
5.0055336e-05 |
| 6,639 |
Leveraging Query Logs and Machine Learning for Parametric Query Optimization |
2022 |
VLDB |
4.976781e-05 |
| 7,776 |
Plan Stitch: Harnessing the Best of Many Plans |
2018 |
VLDB |
4.6493147e-05 |
| 8,638 |
A Concave Path to Low-overhead Robust Query Processing |
2018 |
VLDB |
4.4750742e-05 |
| 9,312 |
Parallelizing Query Optimization on Shared-Nothing Architectures |
2016 |
VLDB |
4.353536e-05 |
| 9,692 |
ROME: Robust Query Optimization via Parallel Multi-Plan Execution |
2024 |
SIGMOD |
4.2986161e-05 |
| 10,050 |
APQO: An Adaptive Framework for Parametric Query Optimization |
2026 |
SIGMOD |
4.1905499e-05 |
| 10,884 |
RankPQO: Learning-to-Rank for Parametric Query Optimization |
2025 |
VLDB |
4.1905499e-05 |
Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking |
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| 5,073 |
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Leveraging Re-costing for Online Optimization of Parameterized Queries with Guarantees |
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Design and Analysis of Parametric Query Optimization Algorithms |
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| 1,644 |
Parametric Query Optimization for Linear and Piecewise Linear Cost Functions |
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