Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process
Summary: Lightweight, uncertainty-aware cardinality estimation via Neural Network Gaussian Process (NNGP); trained in seconds with calibrated predictions. Bayesian deep learning; NNGP yields a universal nonparametric model (GP limit), robust to workload shift. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Kangfei Zhao (Chinese University of Hong Kong)
- 2. Jeffrey Xu Yu (Chinese University of Hong Kong)
- 3. Zongyan He (Chinese University of Hong Kong)
- 4. Rui Li (Chinese University of Hong Kong)
- 5. Hao Zhang (Chinese University of Hong Kong)
BibTeX Citation
@inproceedings{zhao_sigmod22,
title = {{Lightweight and Accurate Cardinality Estimation by Neural Network Gaussian Process}},
author = {Zhao, Kangfei and Yu, Jeffrey Xu and He, Zongyan and Li, Rui and Zhang, Hao},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3526156},
url = {https://dl.acm.org/doi/10.1145/3514221.3526156},
year = {2022}
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