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ActivePDB: Active Probabilistic Databases

Summary: ActivePDB proposes an end-to-end framework for uncertain relational data with an oracle for verification. It uses provenance to map outputs to inputs and active learning to minimize verifications, updating probabilities; demonstrated on NELL. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
13039
Venue
VLDB
Year
2022
Pagerank
5.093636e-05
Overall Rank
11,597 | 20.44%
DOI
10.14778/3554821.3554863

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Authors

BibTeX Citation

@article{drien_vldb22,
        title = {{ActivePDB: Active Probabilistic Databases}},
        author = {Drien, Osnat and Freiman, Matanya and Amsterdamer, Yael},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {12},
        pages = {3638--3641},
        doi = {10.14778/3554821.3554863},
        url = {https://doi.org/10.14778/3554821.3554863},
        year = {2022}
}

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Outgoing Citations (Sorted by Pagerank)

Showing 6 of 6 cited papers.

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

Rank Cited Paper Year Venue Pagerank
17 Provenance Semirings 2007 PODS 0.00059843817
112 HoloClean: Holistic Data Repairs with Probabilistic Inference 2017 VLDB 0.00032801121
1,101 KATARA: A Data Cleaning System Powered by Knowledge Bases and Crowdsourcing 2015 SIGMOD 0.00012168934
2,570 Query-Oriented Data Cleaning with Oracles 2015 SIGMOD 8.4061164e-05
5,211 CDB: A Crowd-Powered Database System 2018 VLDB 6.3161892e-05
5,648 Consistent Query Answers in Inconsistent Probabilistic Databases 2010 SIGMOD 6.1351417e-05
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