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Skyline Computation with Noisy Comparisons
Given a set of n points in a d-dimensional space, we seek to compute the skyline, i.e., those points that are not strictly dominated by any other point, using few comparisons between elements. We adopt the noisy comparison model [15] where comparisons fail with constant probability and confidence ca...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7254908/ http://dx.doi.org/10.1007/978-3-030-48966-3_22 |
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author | Groz, Benoît Mallmann-Trenn, Frederik Mathieu, Claire Verdugo, Victor |
author_facet | Groz, Benoît Mallmann-Trenn, Frederik Mathieu, Claire Verdugo, Victor |
author_sort | Groz, Benoît |
collection | PubMed |
description | Given a set of n points in a d-dimensional space, we seek to compute the skyline, i.e., those points that are not strictly dominated by any other point, using few comparisons between elements. We adopt the noisy comparison model [15] where comparisons fail with constant probability and confidence can be increased through independent repetitions of a comparison. In this model motivated by Crowdsourcing applications, Groz and Milo [18] show three bounds on the query complexity for the skyline problem. We improve significantly on that state of the art and provide two output-sensitive algorithms computing the skyline with respective query complexity [Formula: see text] and [Formula: see text], where k is the size of the skyline and [Formula: see text] the expected probability that our algorithm fails to return the correct answer. These results are tight for low dimensions. |
format | Online Article Text |
id | pubmed-7254908 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
record_format | MEDLINE/PubMed |
spelling | pubmed-72549082020-05-28 Skyline Computation with Noisy Comparisons Groz, Benoît Mallmann-Trenn, Frederik Mathieu, Claire Verdugo, Victor Combinatorial Algorithms Article Given a set of n points in a d-dimensional space, we seek to compute the skyline, i.e., those points that are not strictly dominated by any other point, using few comparisons between elements. We adopt the noisy comparison model [15] where comparisons fail with constant probability and confidence can be increased through independent repetitions of a comparison. In this model motivated by Crowdsourcing applications, Groz and Milo [18] show three bounds on the query complexity for the skyline problem. We improve significantly on that state of the art and provide two output-sensitive algorithms computing the skyline with respective query complexity [Formula: see text] and [Formula: see text], where k is the size of the skyline and [Formula: see text] the expected probability that our algorithm fails to return the correct answer. These results are tight for low dimensions. 2020-04-30 /pmc/articles/PMC7254908/ http://dx.doi.org/10.1007/978-3-030-48966-3_22 Text en © Springer Nature Switzerland AG 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Article Groz, Benoît Mallmann-Trenn, Frederik Mathieu, Claire Verdugo, Victor Skyline Computation with Noisy Comparisons |
title | Skyline Computation with Noisy Comparisons |
title_full | Skyline Computation with Noisy Comparisons |
title_fullStr | Skyline Computation with Noisy Comparisons |
title_full_unstemmed | Skyline Computation with Noisy Comparisons |
title_short | Skyline Computation with Noisy Comparisons |
title_sort | skyline computation with noisy comparisons |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7254908/ http://dx.doi.org/10.1007/978-3-030-48966-3_22 |
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