LpBound: Pessimistic Cardinality Estimation using ℓp-Norms of Degree Sequences
Summary: LpBound yields upper bound on multi-join cardinalities via lp-norms of degree sequences; solved by an LP with data stats. Two optimizations exploit query structure to speed estimation; it beats traditional ML estimators in accuracy with comparable cost. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Haozhe Zhang (University of Zurich)
- 2. Christoph Mayer (University of Zurich)
- 3. Mahmoud Abo Khamis (RelationalAI)
- 4. Dan Olteanu (University of Zurich)
- 5. Dan Suciu (University of Washington)
BibTeX Citation
@inproceedings{zhang_sigmod25,
title = {{LpBound: Pessimistic Cardinality Estimation using ℓp-Norms of Degree Sequences}},
author = {Zhang, Haozhe and Mayer, Christoph and Khamis, Mahmoud Abo and Olteanu, Dan and Suciu, Dan},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725321},
url = {https://dl.acm.org/doi/10.1145/3725321},
year = {2025}
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