Efficient and Effective Cardinality Estimation for Skyline Family
Summary: EECE unifies skyline variants into a single end-to-end estimator; learning distributions and enforcing monotonicity. Incremental learning with a mixture-data guided transformer and monotonicity clamping yields six-order speedup and 39% accuracy gain over SOTA. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Xiaoye Miao (Zhejiang University)
- 2. Yangyang Wu (Zhejiang University)
- 3. Jiazhen Peng (Zhejiang University)
- 4. Yunjun Gao (Zhejiang University)
- 5. Jianwei Yin (Zhejiang University)
BibTeX Citation
@inproceedings{miao_sigmod23,
title = {{Efficient and Effective Cardinality Estimation for Skyline Family}},
author = {Miao, Xiaoye and Wu, Yangyang and Peng, Jiazhen and Gao, Yunjun and Yin, Jianwei},
series = {{SIGMOD} '23},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3588958},
url = {https://dl.acm.org/doi/10.1145/3588958},
year = {2023}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,424 | Causal Search for Skylines (CSS): Causally-Informed Selective Data De-Correlation | 2026 | SIGMOD | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 27 of 27 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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