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dbET: Execution Time Distribution-based Plan Selection

Summary: dbET introduces execution-time distributions for query plans via conformal predictions, replacing single-cost estimates. No DBMS modification, minimal overhead; uses distributions to guide plan selection and improve objective attainment on benchmarks. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
hefb9aa6d491ce5d9
Venue
SIGMOD
Year
2023
Pagerank
5.6811782e-05
Overall Rank
6,791 | 54.35%
DOI
10.1145/3588711

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{li_sigmod23,
        title = {{dbET: Execution Time Distribution-based Plan Selection}},
        author = {Li, Yifan and Yu, Xiaohui and Koudas, Nick and Lin, Shu and Sun, Calvin and Chen, Chong},
        series = {{SIGMOD} '23},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3588711},
        url = {https://dl.acm.org/doi/10.1145/3588711},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 8 of 8 citing papers.

Rank Citing Paper Year Venue Pagerank
7,977 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.4142519e-05
8,389 PARQO: Penalty-Aware Robust Plan Selection in Query Optimization 2024 VLDB 5.3413016e-05
8,524 ROME: Robust Query Optimization via Parallel Multi-Plan Execution 2024 SIGMOD 5.3231221e-05
9,730 PAR2QO: Parametric Penalty-Aware Robust Query Optimization 2025 VLDB 5.1349531e-05
10,290 This is Going to Sound Crazy, But What If We Used Large Language Models to Boost Automatic Database Tuning Algorithms By Leveraging Prior History? We Will Find Better Configurations More Quickly Than Retraining From Scratch! 2026 SIGMOD 5.0431863e-05
10,368 Coresets for Robust Query Optimization 2026 PODS 4.9793485e-05
10,834 ConANN: Conformal Approximate Nearest Neighbor Search 2026 VLDB 4.9793485e-05
10,876 ConRAD: Conformal Risk-Aware Neural Databases 2026 VLDB 4.9793485e-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.

Rank Cited Paper Year Venue Pagerank
15 How Good Are Query Optimizers, Really? 2016 VLDB 0.00061066921
37 Improved Histograms for Selectivity Estimation of Range Predicates 1996 SIGMOD 0.00047731453
79 Practical Selectivity Estimation through Adaptive Sampling 1990 SIGMOD 0.00036487763
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035864347
98 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034106982
145 Neo: A Learned Query Optimizer 2019 VLDB 0.0002908188
286 Selectivity Estimation using Probabilistic Models 2001 SIGMOD 0.0002211981
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021167555
362 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019989474
461 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017829982
512 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017050173
569 Towards a Robust Query Optimizer: A Principled and Practical Approach 2005 SIGMOD 0.00016245271
596 Wander Join: Online Aggregation via Random Walks 2016 SIGMOD 0.00015785583
688 Cardinality Estimation Done Right: Index-Based Join Sampling 2017 CIDR 0.00014753664
701 Independence is Good: Dependency-Based Histogram Synopses for High-Dimensional Data 2001 SIGMOD 0.00014680907
1,064 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012202282
1,250 DB-BERT: A Database Tuning Tool that "Reads the Manual" 2022 SIGMOD 0.00011339256
1,257 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011310561
1,276 Orca: A Modular Query Optimizer Architecture for Big Data 2014 SIGMOD 0.00011239266
1,465 Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities 2019 SIGMOD 0.00010576304
1,515 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 0.00010417728
1,536 Cardinality Estimation: An Experimental Survey 2018 VLDB 0.00010327422
1,542 Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses 2018 VLDB 0.00010308631
1,603 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2019 SIGMOD 0.00010097649
1,734 Flow-Loss: Learning Cardinality Estimates That Matter 2021 VLDB 9.7545773e-05
2,522 Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation 2022 VLDB 8.3477168e-05
3,052 A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation 2021 SIGMOD 7.7052471e-05
3,210 Efficiently Approximating Selectivity Functions using Low Overhead Regression Models 2020 VLDB 7.5363533e-05
3,396 Variance Aware Optimization of Parameterized Queries 2010 SIGMOD 7.3418098e-05
3,818 Spatial Online Sampling and Aggregation 2016 VLDB 7.0060535e-05
3,978 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.8807882e-05
5,199 Exact Cardinality Query Optimization with Bounded Execution Cost 2019 SIGMOD 6.2327836e-05
6,147 Sia: Optimizing Queries using Learned Predicates 2021 SIGMOD 5.8710365e-05
7,341 Scalable Multi-Query Execution using Reinforcement Learning 2021 SIGMOD 5.5481233e-05
7,429 Learning to be a Statistician: Learned Estimator for Number of Distinct Values 2022 VLDB 5.5300853e-05
7,597 On the Calculation of Optimality Ranges for Relational Query Execution Plans 2018 SIGMOD 5.4872821e-05
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