FastPDB: Towards Bag-Probabilistic Queries at Interactive Speeds
Summary: FastPDB tackles bag-probabilistic queries with bag semantics and PTIME data, but exact processing is slower than deterministic evaluation. It enables fast approximations by sampling monomials directly, avoiding upfront lineage, scalable to data. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Aaron Huber (State University of New York at Buffalo)
- 2. Oliver Kennedy (State University of New York at Buffalo)
- 3. Atri Rudra (State University of New York at Buffalo)
- 4. Zhuoyue Zhao (State University of New York at Buffalo)
- 5. Su Feng (Nanjing Tech University)
- 6. Boris Glavic (University of Illinois Chicago)
BibTeX Citation
@inproceedings{huber_sigmod25,
title = {{FastPDB: Towards Bag-Probabilistic Queries at Interactive Speeds}},
author = {Huber, Aaron and Kennedy, Oliver and Rudra, Atri and Zhao, Zhuoyue and Feng, Su and Glavic, Boris},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3709691},
url = {https://dl.acm.org/doi/10.1145/3709691},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,293 | Variational Inference for De Finetti Logic | 2026 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 22 of 22 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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