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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
6596
Venue
SIGMOD
Year
2023
Pagerank
5.6770249e-05
Overall Rank
7,193 | 50.66%
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 7 of 7 citing papers.

Rank Citing Paper Year Venue Pagerank
7,846 The Holon Approach for Simultaneously Tuning Multiple Components in a Self-Driving Database Management System with Machine Learning via Synthesized Proto-Actions 2024 VLDB 5.5331459e-05
8,305 PARQO: Penalty-Aware Robust Plan Selection in Query Optimization 2024 VLDB 5.4568571e-05
9,796 ROME: Robust Query Optimization via Parallel Multi-Plan Execution 2024 SIGMOD 5.21848e-05
10,151 Coresets for Robust Query Optimization 2026 PODS 5.093636e-05
10,506 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 5.093636e-05
10,578 ConANN: Conformal Approximate Nearest Neighbor Search 2026 VLDB 5.093636e-05
10,986 PAR2QO: Parametric Penalty-Aware Robust Query Optimization 2025 VLDB 5.093636e-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
18 How Good Are Query Optimizers, Really? 2016 VLDB 0.00059284255
35 Improved Histograms for Selectivity Estimation of Range Predicates 1996 SIGMOD 0.00048481081
76 Practical Selectivity Estimation through Adaptive Sampling 1990 SIGMOD 0.00037054261
84 Learned Cardinalities: Estimating Correlated Joins with Deep Learning 2019 CIDR 0.00035838391
100 LEO - DB2's LEarning Optimizer 2001 VLDB 0.00034385207
154 Neo: A Learned Query Optimizer 2019 VLDB 0.00028726181
280 Selectivity Estimation using Probabilistic Models 2001 SIGMOD 0.00022454217
323 DeepDB: Learn from Data, not from Queries! 2020 VLDB 0.00021264788
378 Bao: Making Learned Query Optimization Practical 2021 SIGMOD 0.00019638121
465 An End-to-End Learning-based Cost Estimator 2020 VLDB 0.0001803934
513 NeuroCard: One Cardinality Estimator for All Tables 2021 VLDB 0.00017190574
566 Towards a Robust Query Optimizer: A Principled and Practical Approach 2005 SIGMOD 0.00016436005
593 Wander Join: Online Aggregation via Random Walks 2016 SIGMOD 0.00016027871
692 Independence is Good: Dependency-Based Histogram Synopses for High-Dimensional Data 2001 SIGMOD 0.00014919816
694 Cardinality Estimation Done Right: Index-Based Join Sampling 2017 CIDR 0.00014911698
1,061 Are We Ready For Learned Cardinality Estimation? 2021 VLDB 0.00012369764
1,256 Sampling-Based Query Re-Optimization 2016 SIGMOD 0.00011457194
1,337 DB-BERT: A Database Tuning Tool that "Reads the Manual" 2022 SIGMOD 0.00011117488
1,499 Pessimistic Cardinality Estimation: Tighter Upper Bounds for Intermediate Join Cardinalities 2019 SIGMOD 0.00010564536
1,516 Cardinality Estimation: An Experimental Survey 2018 VLDB 0.00010520885
1,536 Improved Selectivity Estimation by Combining Knowledge from Sampling and Synopses 2018 VLDB 0.00010460864
1,621 Orca: A Modular Query Optimizer Architecture for Big Data 2014 SIGMOD 0.00010203114
1,712 SkinnerDB: Regret-Bounded Query Evaluation via Reinforcement Learning 2019 SIGMOD 9.9492299e-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
2,723 Learned Cardinality Estimation: A Design Space Exploration and A Comparative Evaluation 2022 VLDB 8.2049453e-05
3,086 A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation 2021 SIGMOD 7.7708642e-05
3,162 Efficiently Approximating Selectivity Functions using Low Overhead Regression Models 2020 VLDB 7.6785856e-05
3,513 Variance Aware Optimization of Parameterized Queries 2010 SIGMOD 7.354613e-05
3,741 Spatial Online Sampling and Aggregation 2016 VLDB 7.1586403e-05
3,953 Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload 2021 SIGMOD 6.996368e-05
5,137 Exact Cardinality Query Optimization with Bounded Execution Cost 2019 SIGMOD 6.3507372e-05
6,031 Sia: Optimizing Queries using Learned Predicates 2021 SIGMOD 6.0007422e-05
7,213 Scalable Multi-Query Execution using Reinforcement Learning 2021 SIGMOD 5.670422e-05
7,290 Learning to be a Statistician: Learned Estimator for Number of Distinct Values 2022 VLDB 5.6540503e-05
7,495 On the Calculation of Optimality Ranges for Relational Query Execution Plans 2018 SIGMOD 5.6041473e-05
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