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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...

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Detalles Bibliográficos
Autores principales: Groz, Benoît, Mallmann-Trenn, Frederik, Mathieu, Claire, Verdugo, Victor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
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.
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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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