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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)

Paper ID
4271
Venue
SIGMOD
Year
2009
Pagerank
8.1359291e-05
Overall Rank
2,775 | 80.97%
DOI
10.1145/1559845.1559984

Incoming Non-self Citations Over Time

Authors

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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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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