Skyline Queries with Noisy Comparisons

Benoit Groz, Tova Milo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

20 Scopus citations


We study in this paper the computation of skyline queries - a popular tool for multicriteria data analysis - in the presence of noisy input. Motivated by crowdsourcing applications, we present the first algorithms for skyline evaluation in a computation model where the input data items can only be compared through noisy comparisons. In this model comparisons may return wrong answers with some probability, and confidence can be increased through independent repetitions of a comparison. Our goal is to minimize the number of comparisons required for computing or verifying a candidate skyline, while returning the correct answer with high probability. We design output-sensitive algorithms, namely algorithms that take advantage of the potentially small size of the skyline, and analyze the number of comparison rounds of our solutions. We also consider the problem of predicting the most likely skyline given some partial information in the form of noisy comparisons, and show that optimal prediction is computationally intractable.

Original languageEnglish
Title of host publicationPODS 2015 - Proceedings of the 34th ACM Symposium on Principles of Database Systems
PublisherAssociation for Computing Machinery
Number of pages14
ISBN (Electronic)9781450327572
StatePublished - 20 May 2015
Event34th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, PODS 2015 - Melbourne, Australia
Duration: 21 May 20154 Jun 2015

Publication series

NameProceedings of the ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems


Conference34th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, PODS 2015


FundersFunder number
Seventh Framework Programme291071


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