Robust Plan Evaluation based on Approximate Probabilistic Machine Learning

Summary: Roq formalizes robustness in query optimization as risk-aware plan performance under uncertain cardinalities and invalid assumptions. Approximate probabilistic ML jointly predicts execution costs and risks, enabling robust plan evaluation and selection beyond conventional point-estimate optimizers. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 14094
Venue: VLDB
Year: 2025
Pagerank: 5.093636e-05
Overall Rank: 10,881 | 25.35%
DOI: 10.14778/3742728.3742753

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Amin Kamali (University of Ottawa)
2. Verena Kantere (University of Ottawa)
3. Calisto Zuzarte (IBM Canada Lab)
4. Vincent Corvinelli (IBM Canada Lab)

BibTeX Citation

@article{kamali_vldb25,
        title = {{Robust Plan Evaluation based on Approximate Probabilistic Machine Learning}},
        author = {Kamali, Amin and Kantere, Verena and Zuzarte, Calisto and Corvinelli, Vincent},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {8},
        pages = {2626--2638},
        doi = {10.14778/3742728.3742753},
        url = {https://doi.org/10.14778/3742728.3742753},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
10,492	Reqo: A Comprehensive Learning-Based Cost Model for Robust and Explainable Query Optimization	2026	SIGMOD	5.093636e-05

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
18	How Good Are Query Optimizers, Really?	2016	VLDB	0.00059284255
154	Neo: A Learned Query Optimizer	2019	VLDB	0.00028726181
323	DeepDB: Learn from Data, not from Queries!	2020	VLDB	0.00021264788
388	Preventing Bad Plans by Bounding the Impact of Cardinality Estimation Errors	2009	VLDB	0.00019410042
476	The Making of TPC-DS	2006	VLDB	0.00017860667
492	Robust Query Processing through Progressive Optimization	2004	SIGMOD	0.0001756877
566	Towards a Robust Query Optimizer: A Principled and Practical Approach	2005	SIGMOD	0.00016436005
829	Proactive Re-Optimization	2005	SIGMOD	0.00013769838
1,241	Balsa: Learning a Query Optimizer Without Expert Demonstrations	2022	SIGMOD	0.00011521639
1,795	Least Expected Cost Query Optimization: What Can We Expect?	2002	PODS	9.738718e-05
1,832	DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems	2021	VLDB	9.6607418e-05
1,876	Flow-Loss: Learning Cardinality Estimates That Matter	2021	VLDB	9.5717543e-05
2,420	Lero: A Learning-to-Rank Query Optimizer	2023	VLDB	8.605257e-05
2,762	Cost-based or Learning-based? A Hybrid Query Optimizer for Query Plan Selection	2022	VLDB	8.1539867e-05
2,822	Cost Models for Big Data Query Processing: Learning, Retrofitting, and Our Findings	2020	SIGMOD	8.0898536e-05
3,516	LOGER: A Learned Optimizer towards Generating Efficient and Robust Query Execution Plans	2023	VLDB	7.3524442e-05
3,571	Looking Ahead Makes Query Plans Robust: Making the Initial Case with In-Memory Star Schema Data Warehouse Workloads	2017	VLDB	7.2991953e-05
4,468	One Model to Rule them All: Towards Zero-Shot Learning for Databases	2022	CIDR	6.6819041e-05
6,009	Optimization of Conjunctive Predicates for Main Memory Column Stores	2016	VLDB	6.0113733e-05
6,019	Robustness Metrics for Relational Query Execution Plans	2018	VLDB	6.0060149e-05
6,593	Simple Adaptive Query Processing vs. Learned Query Optimizers: Observations and Analysis	2023	VLDB	5.8297039e-05
7,829	Robust Query Processing: Mission Possible	2020	VLDB	5.5360082e-05
8,004	A Concave Path to Low-overhead Robust Query Processing	2018	VLDB	5.5082745e-05
9,455	Small Selectivities Matter: Lifting the Burden of Empty Samples	2021	SIGMOD	5.2653318e-05

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