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Robust Plan Evaluation based on Approximate Probabilistic Machine Learning
Summary: Roq: risk-aware optimizer formalizing robustness and using approximate probabilistic ML to estimate cost distributions and execution risk instead of point estimates. Uses a learned cost model and new plan-evaluation/selection algorithms to trade expected cost vs. risk and improve robustness over prior optimizers.
(summarized by gpt-5-mini on Feb 09 2026)
- Paper ID
- 13907
- Venue
- VLDB
- Year
- 2025
- Pagerank
- 4.1905499e-05
- Overall Rank
- 10,635 | 26.09%
- DOI
-
10.14778/3742728.3742753
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
Outgoing Citations (Sorted by Pagerank)
Showing 24 of 24 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 |
0.00059446482 |
| 329 |
Neo: A Learned Query Optimizer |
2019 |
VLDB |
0.00027301488 |
| 606 |
DeepDB: Learn from Data, not from Queries! |
2020 |
VLDB |
0.00019251186 |
| 627 |
Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors |
2009 |
VLDB |
0.00018959896 |
| 650 |
Robust Query Processing through Progressive Optimization |
2004 |
SIGMOD |
0.0001865144 |
| 659 |
The Making of TPC-DS |
2006 |
VLDB |
0.00018514913 |
| 680 |
Towards a Robust Query Optimizer: A Principled and Practical Approach |
2005 |
SIGMOD |
0.00018193263 |
| 1,268 |
Proactive Re-Optimization |
2005 |
SIGMOD |
0.00012914584 |
| 2,090 |
Balsa: Learning a Query Optimizer Without Expert Demonstrations |
2022 |
SIGMOD |
9.5668285e-05 |
| 2,187 |
Least Expected Cost Query Optimization: What Can We Expect? |
2002 |
PODS |
9.3429142e-05 |
| 2,781 |
Flow-Loss: Learning Cardinality Estimates That Matter |
2021 |
VLDB |
8.1282042e-05 |
| 2,937 |
DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems |
2021 |
VLDB |
7.8552033e-05 |
| 3,345 |
Lero: A Learning-to-Rank Query Optimizer |
2023 |
VLDB |
7.1908499e-05 |
| 3,623 |
Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings |
2020 |
SIGMOD |
6.9017341e-05 |
| 3,729 |
Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection |
2022 |
VLDB |
6.8078013e-05 |
| 4,272 |
Looking Ahead Makes Query Plans Robust: Making the Initial Case with In-Memory Star Schema Data Warehouse Workloads |
2017 |
VLDB |
6.2933353e-05 |
| 4,464 |
LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans |
2023 |
VLDB |
6.1552798e-05 |
| 5,250 |
One Model to Rule them All: Towards Zero-Shot Learning for Databases |
2022 |
CIDR |
5.6007779e-05 |
| 6,372 |
Optimization of Conjunctive Predicates for Main Memory Column Stores |
2016 |
VLDB |
5.0878306e-05 |
| 6,763 |
Robustness Metrics for Relational Query Execution Plans |
2018 |
VLDB |
4.9291549e-05 |
| 7,011 |
Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis |
2023 |
VLDB |
4.8583284e-05 |
| 8,130 |
Robust Query Processing: Mission Possible |
2020 |
VLDB |
4.5746794e-05 |
| 8,638 |
A Concave Path to Low-overhead Robust Query Processing |
2018 |
VLDB |
4.4750742e-05 |
| 9,387 |
Small Selectivities Matter: Lifting the Burden of Empty Samples |
2021 |
SIGMOD |
4.341968e-05 |
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| 1,017 |
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| 1,698 |
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Robustness Metrics for Relational Query Execution Plans |
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| 680 |
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SIGMOD |
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