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Ranking with Uncertain Scoring Functions: Semantics and Sensitivity Measures

Summary: Investigates ranking with uncertain/incomplete scoring functions (weight ranges, partial preferences) rather than fixed weights. Delivers formal semantics and sensitivity measures, with efficient techniques for interactive top-K under uncertainty. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
4433
Venue
SIGMOD
Year
2011
Pagerank
7.70946e-05
Overall Rank
3,014 | 79.04%
DOI
-

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

Rank Cited Paper Year Venue Pagerank
430 The Onion Technique: Indexing for Linear Optimization Queries 2000 SIGMOD 0.00023463938
552 Supporting Incremental Join Queries on Ranked Inputs 2001 VLDB 0.00020310903
674 Supporting Top-k Join Queries in Relational Databases 2003 VLDB 0.00018327585
3,505 Consensus Answers for Queries over Probabilistic Databases 2009 PODS 7.0337815e-05
4,095 Ranking Continuous Probabilistic Datasets 2010 VLDB 6.4556768e-05
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