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Beta Probabilistic Databases: A Scalable Approach to Belief Updating and Parameter Learning

Summary: Beta Probabilistic Databases (B-PDBs) model each tuple probability as a Beta latent variable, enabling principled belief updates from noisy, indirect evidence. Remains TI-PDB-compatible, enabling scalable Bayesian updates and soft-EM learning in-database. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
5464
Venue
SIGMOD
Year
2017
Pagerank
5.4049137e-05
Overall Rank
8,597 | 41.02%
DOI
10.1145/3035918.3064026

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{meneghetti_sigmod17,
        title = {{Beta Probabilistic Databases: A Scalable Approach to Belief Updating and Parameter Learning}},
        author = {Meneghetti, Niccolò and Kennedy, Oliver and Gatterbauer, Wolfgang},
        series = {{SIGMOD} '17},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3035918.3064026},
        url = {https://dl.acm.org/doi/10.1145/3035918.3064026},
        year = {2017}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
7,515 CYADB: A Database that Covers Your Ask 2018 VLDB 5.6029996e-05
11,189 StarfishDB: a Query Execution Engine for Relational Probabilistic Programming 2024 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 17 of 17 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

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