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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
5.6784895e-05
Overall Rank
6,796 | 54.33%
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
7,981 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.4117272e-05
8,393 PARQO: Penalty-Aware Robust Plan Selection in Query Optimization 2024 VLDB 5.3387995e-05
8,531 ROME: Robust Query Optimization via Parallel Multi-Plan Execution 2024 SIGMOD 5.3206021e-05
9,735 PAR2QO: Parametric Penalty-Aware Robust Query Optimization 2025 VLDB 5.1325223e-05
10,296 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 5.0407989e-05
10,380 Coresets for Robust Query Optimization 2026 PODS 4.9769913e-05
10,844 ConANN: Conformal Approximate Nearest Neighbor Search 2026 VLDB 4.9769913e-05
10,885 ConRAD: Conformal Risk-Aware Neural Databases 2026 VLDB 4.9769913e-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 0.00061067652
37 Improved Histograms for Selectivity Estimation of Range Predicates 1996 SIGMOD 0.0004772731
79 Practical Selectivity Estimation through Adaptive Sampling 1990 SIGMOD 0.00036476265
85 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035876108
98 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034099838
144 Neo: A Learned Query Optimizer 2019 VLDB 0.00029090793
286 Selectivity Estimation using Probabilistic Models 2001 SIGMOD 0.00022112534
318 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021166957
361 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00020000855
462 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.00017836105
510 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017059914
569 Towards a Robust Query Optimizer: A Principled and Practical Approach 2005 SIGMOD 0.00016244162
596 Wander Join: Online Aggregation via Random Walks 2016 SIGMOD 0.00015782051
688 Cardinality Estimation Done Right: Index-Based Join Sampling 2017 CIDR 0.00014749318
700 Independence is Good: Dependency-Based Histogram Synopses for High-Dimensional Data 2001 SIGMOD 0.00014675903
1,065 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012202293
1,245 DB-BERT: A Database Tuning Tool that "Reads the Manual" 2022 SIGMOD 0.0001136308
1,258 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011308863
1,277 Orca: A Modular Query Optimizer Architecture for Big Data 2014 SIGMOD 0.00011234276
1,465 Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities 2019 SIGMOD 0.00010572023
1,515 DSB: A Decision Support Benchmark for Workload-Driven and Traditional Database Systems 2021 VLDB 0.00010418766
1,537 Cardinality Estimation: An Experimental Survey 2018 VLDB 0.00010324934
1,543 Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses 2018 VLDB 0.00010305662
1,605 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2019 SIGMOD 0.00010095581
1,735 Flow-Loss: Learning Cardinality Estimates That Matter 2021 VLDB 9.7566604e-05
2,518 Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation 2022 VLDB 8.3532841e-05
3,053 A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation 2021 SIGMOD 7.7041081e-05
3,208 Efficiently Approximating Selectivity Functions using Low Overhead Regression Models 2020 VLDB 7.5355264e-05
3,396 Variance Aware Optimization of Parameterized Queries 2010 SIGMOD 7.3386533e-05
3,819 Spatial Online Sampling and Aggregation 2016 VLDB 7.0027383e-05
3,979 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.8791817e-05
5,200 Exact Cardinality Query Optimization with Bounded Execution Cost 2019 SIGMOD 6.2303304e-05
6,149 Sia: Optimizing Queries using Learned Predicates 2021 SIGMOD 5.8683464e-05
7,344 Scalable Multi-Query Execution using Reinforcement Learning 2021 SIGMOD 5.5455974e-05
7,426 Learning to be a Statistician: Learned Estimator for Number of Distinct Values 2022 VLDB 5.5295692e-05
7,603 On the Calculation of Optimality Ranges for Relational Query Execution Plans 2018 SIGMOD 5.4846849e-05
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