A Unified Approach to Ranking in Probabilistic Databases
Summary: Recasts probabilistic top-k as multi-criteria optimization, introducing parameterized PRF^ω and PRF^e rankings that subsume or approximate prior semantics. Generating-function algorithms support correlated data, with parameters learnable from user preferences. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jian Li (University of Maryland)
- 2. Barna Saha (University of Maryland)
- 3. Amol Deshpande (University of Maryland)
BibTeX Citation
@article{li_vldb09,
title = {{A Unified Approach to Ranking in Probabilistic Databases}},
author = {Li, Jian and Saha, Barna and Deshpande, Amol},
journal = {PVLDB},
series = {{VLDB} '09},
doi = {10.14778/1687627.1687685},
url = {https://doi.org/10.14778/1687627.1687685},
year = {2009}
}
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