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Approximate Lifted Inference with Probabilistic Databases

Summary: Approximates #P-hard probabilistic-database queries by taking the minimum of schema-aware upper bounds from a minimal set of in-engine plans. Strictly generalizes safe self-join-free CQs: safety iff one plan, with optimized execution and ranking implications. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
11362
Venue
VLDB
Year
2015
Pagerank
6.310093e-05
Overall Rank
5,225 | 64.16%
DOI
10.14778/2735479.2735488

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BibTeX Citation

@article{gatterbauer_vldb15,
        title = {{Approximate Lifted Inference with Probabilistic Databases}},
        author = {Gatterbauer, Wolfgang and Suciu, Dan},
        journal = {PVLDB},
        series = {{VLDB} '15},
        volume = {8},
        number = {5},
        pages = {629--640},
        doi = {10.14778/2735479.2735488},
        url = {https://doi.org/10.14778/2735479.2735488},
        year = {2015}
}

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