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Querying Probabilistic Preferences in Databases

Summary: Embed probabilistic preference symbols in relational schemas, representing each preference as distributions like RIM/Mallows to model uncertain binary preferences in PDBs. Reduce CQ evaluation to a new inference problem over RIM, prove complexity bounds and give a polynomial‑data solver. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1723
Venue
PODS
Year
2017
Pagerank
4.4937074e-05
Overall Rank
8,541 | 40.59%
DOI
10.1145/3034786.3056111

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 5 of 5 citing papers.

Rank Citing Paper Year Venue Pagerank
8,538 A Query Engine for Probabilistic Preferences 2018 SIGMOD 4.4937074e-05
11,331 The Gibbs–Rand Model 2022 PODS 4.1945683e-05
11,643 Query Evaluation in Election Databases 2019 PODS 4.1945683e-05
13,180 Most Expected Winner: An Interpretation of Winners over Uncertain Voter Preferences 2023 SIGMOD -
13,281 Supporting Hard Queries over Probabilistic Preferences 2020 VLDB -
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Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank Cited Paper Year Venue Pagerank
74 Efficient Query Evaluation on Probabilistic Databases 2004 VLDB 0.00057857292
707 Foundations of Preferences in Database Systems 2002 VLDB 0.00017782998
4,803 A System for Management and Analysis of Preference Data 2014 VLDB 5.9107061e-05
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