CorrBound: Cardinality Estimation Accounting for Inter- and Intra-relation Correlations
Summary: CorrBound is a cardinality estimator that explicitly models inter-/intra-relation join-column correlations via a new information inequality and LP over stats + Shannon constraints, extending beyond LpBound. Sketching and low-rank tensor compression make the correlation-aware estimates practical for multi-join, range, and group-by queries. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Christoph Mayer (University of Zurich)
- 2. Haozhe Zhang (University of Zurich)
- 3. Mahmoud Abo Khamis (RelationalAI)
- 4. Kyle Deeds (Boston University)
- 5. Dan Olteanu (University of Zurich)
- 6. Dan Suciu (University of Washington)
BibTeX Citation
@inproceedings{mayer_sigmod26,
title = {{CorrBound: Cardinality Estimation Accounting for Inter- and Intra-relation Correlations}},
author = {Mayer, Christoph and Zhang, Haozhe and Khamis, Mahmoud Abo and Deeds, Kyle and Olteanu, Dan and Suciu, Dan},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786633},
url = {https://dl.acm.org/doi/10.1145/3786633},
year = {2026}
}
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