MayBMS: A Probabilistic Database Management System
Summary: MayBMS extends PostgreSQL with efficient probabilistic operators and APIs for scalable probabilistic querying. It demonstrates data cleaning, HR, and social-network analytics via demos and a web app that uses random-walk on stochastic matrices to model player fitness. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jiewen Huang (Cornell University; University of Oxford)
- 2. Lyublena Antova (Cornell University)
- 3. Christoph Koch (Cornell University)
- 4. Dan Olteanu (University of Oxford)
BibTeX Citation
@inproceedings{huang_sigmod09,
title = {{MayBMS: A Probabilistic Database Management System}},
author = {Huang, Jiewen and Antova, Lyublena and Koch, Christoph and Olteanu, Dan},
series = {{SIGMOD} '09},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1559845.1559984},
url = {https://dl.acm.org/doi/10.1145/1559845.1559984},
year = {2009}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 1 of 1 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,520 | Conditioning Probabilistic Databases | 2008 | VLDB | 0.00010510496 |
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| 10 | 4,653 | Query language support for incomplete information in the MayBMS system | 2007 | VLDB |