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Skyline Queries with Noisy Comparisons

Summary: Introduces a noisy-comparison model for skyline queries (pairwise comparisons may err; confidence via repetition) and gives the first output-sensitive algorithms that minimize comparisons and rounds to compute/verify skylines w.h.p. Shows optimal skyline prediction from partial noisy comparisons is computationally intractable. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
1660
Venue
PODS
Year
2015
Pagerank
4.2675549e-05
Overall Rank
9,866 | 31.37%
DOI
10.1145/2745754.2745775

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Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
7,117 Crowdsourced Data Management: Overview and Challenges 2017 SIGMOD 4.826509e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank Cited Paper Year Venue Pagerank
267 Human-powered Sorts and Joins 2012 VLDB 0.00029690405
386 Shooting Stars in the Sky: An Online Algorithm for Skyline Queries 2002 VLDB 0.00024768022
859 So Who Won? Dynamic Max Discovery with the Crowd 2012 SIGMOD 0.00015870894
1,179 Probabilistic Skylines on Uncertain Data 2007 VLDB 0.00013457451
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