Subset Sampling over Joins
Summary: Introduces the first efficient subset (Poisson) sampling algorithms over implicitly defined acyclic joins, with probabilities from decomposable tuple-weight functions. Provides static, one-shot, and insertion-dynamic indexes achieving near-optimal input-size and expected-sample-size bounds without materializing the join. (summarized by gpt-5.6-luna on Jul 26 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Aryan Esmailpour (University of Illinois Chicago)
- 2. Xiao Hu (University of Waterloo)
- 3. Jinchao Huang (Chinese University of Hong Kong)
- 4. Stavros Sintos (University of Illinois Chicago)
BibTeX Citation
@inproceedings{esmailpour_pods26,
address = {New York, NY, USA},
series = {{PODS} '26},
title = {{Subset Sampling over Joins}},
url = {https://dl.acm.org/doi/10.1145/3801913},
doi = {10.1145/3801913},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Esmailpour, Aryan and Hu, Xiao and Huang, Jinchao and Sintos, Stavros},
year = {2026}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,512 | Poisson Sampling over Acyclic Joins | 2026 | SIGMOD | 5.2571354e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 19 of 19 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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