Poisson Sampling over Acyclic Joins
Summary: Introduces near-instance-optimal Poisson sampling for acyclic joins with tuple-specific probabilities, avoiding full materialization via random-access join indexes. Achieves O(|db| + k log |db|) time and a shared basis for sampling and Yannakakis-style processing. (summarized by gpt-5.6-luna on Jul 26 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Liese Bekkers (Hasselt University)
- 2. Frank Neven (Hasselt University)
- 3. Lorrens Pantelis (Hasselt University)
- 4. Stijn Vansummeren (Hasselt University)
BibTeX Citation
@inproceedings{bekkers_sigmod26,
title = {{Poisson Sampling over Acyclic Joins}},
author = {Bekkers, Liese and Neven, Frank and Pantelis, Lorrens and Vansummeren, Stijn},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802101},
url = {https://dl.acm.org/doi/10.1145/3802101},
year = {2026}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,719 | Subset Sampling over Joins | 2026 | PODS | 5.2319816e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 18 of 18 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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