Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach
Summary: DL framework for monotone cardinality estimation of similarity selections. Feature extractor maps data and threshold to Hamming space; regression yields monotone, incremental estimates across data types and distance functions; discusses training, updates, fast estimation, and optimizer impact. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yaoshu Wang (Shenzhen University)
- 2. Chuan Xiao (Nagoya University; Osaka University)
- 3. Jianbin Qin (Shenzhen University)
- 4. Xin Cao (University of New South Wales)
- 5. Yifang Sun (University of New South Wales)
- 6. Wei Wang (University of New South Wales)
- 7. Makoto Onizuka (Osaka University)
BibTeX Citation
@inproceedings{wang_sigmod20,
title = {{Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach}},
author = {Wang, Yaoshu and Xiao, Chuan and Qin, Jianbin and Cao, Xin and Sun, Yifang and Wang, Wei and Onizuka, Makoto},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3380570},
url = {https://dl.acm.org/doi/10.1145/3318464.3380570},
year = {2020}
}
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