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)
Incoming Non-self Citations Over Time
Authors
- 1. Wolfgang Gatterbauer (Carnegie Mellon University)
- 2. Dan Suciu (University of Washington)
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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