Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation
Summary: Learning-based Progressive Cardinality Estimation (LPCE) combines a light LPCE-I with a refinement LPCE-R to accelerate end-to-end query execution. Runtime re-optimization uses actual operator cardinalities to refine plans, integrated into PostgreSQL and beating state-of-the-art estimators on end-to-end time. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Fang Wang (Hong Kong Polytechnic University)
- 2. Xiao Yan (Southern University of Science and Technology)
- 3. Man Lung Yiu (Hong Kong Polytechnic University)
- 4. Shuai Li (Hong Kong Polytechnic University)
- 5. Zunyao Mao (Southern University of Science and Technology)
- 6. Bo Tang (Southern University of Science and Technology)
BibTeX Citation
@inproceedings{wang_sigmod23,
title = {{Speeding Up End-to-end Query Execution via Learning-based Progressive Cardinality Estimation}},
author = {Wang, Fang and Yan, Xiao and Yiu, Man Lung and Li, Shuai and Mao, Zunyao and Tang, Bo},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588708},
url = {https://dl.acm.org/doi/10.1145/3588708},
year = {2023}
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