Learned Cardinality Estimation: An In-depth Study
Summary: Introduces a taxonomy and unified workflow for learned cardinality estimators, enabling fair, apples-to-apples evaluation of join scenarios. Conducts comprehensive experiments on IMDB and TPC-DS beyond toy datasets, demystifies black-box models, identifies error drivers, and links CE to query optimization, suggesting actionable research directions. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Kyoungmin Kim (Pohang University of Science and Technology)
- 2. Jisung Jung (Pohang University of Science and Technology)
- 3. In Seo (Pohang University of Science and Technology)
- 4. Wook-Shin Han (Pohang University of Science and Technology)
- 5. Kangwoo Choi (SAP)
- 6. Jaehyok Chong (SAP)
BibTeX Citation
@inproceedings{kim_sigmod22,
title = {{Learned Cardinality Estimation: An In-depth Study}},
author = {Kim, Kyoungmin and Jung, Jisung and Seo, In and Han, Wook-Shin and Choi, Kangwoo and Chong, Jaehyok},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3526154},
url = {https://dl.acm.org/doi/10.1145/3514221.3526154},
year = {2022}
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