Similarity-based visual exploration of very large georeferenced multidimensional datasets

Descripción del Articulo

This work was supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
Detalles Bibliográficos
Autores: Peralta-Aranibar R., Comba J.L.D., Pahins C.A.L., Gomez-Nieto E.
Formato: objeto de conferencia
Fecha de Publicación:2019
Institución:Consejo Nacional de Ciencia Tecnología e Innovación
Repositorio:CONCYTEC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.concytec.gob.pe:20.500.12390/774
Enlace del recurso:https://hdl.handle.net/20.500.12390/774
https://doi.org/10.1145/3297280.3297556
Nivel de acceso:acceso abierto
Materia:Visualization
Effective approaches
Geographic information
Geographical locations
Interactive exploration
Interactive visualizations
Multi-dimensional datasets
Multidimensional data
Similarity
Data visualization
https://purl.org/pe-repo/ocde/ford#2.02.03
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oai_identifier_str oai:repositorio.concytec.gob.pe:20.500.12390/774
network_acronym_str CONC
network_name_str CONCYTEC-Institucional
repository_id_str 4689
dc.title.none.fl_str_mv Similarity-based visual exploration of very large georeferenced multidimensional datasets
title Similarity-based visual exploration of very large georeferenced multidimensional datasets
spellingShingle Similarity-based visual exploration of very large georeferenced multidimensional datasets
Peralta-Aranibar R.
Visualization
Effective approaches
Effective approaches
Geographic information
Geographic information
Geographical locations
Interactive exploration
Interactive visualizations
Interactive visualizations
Multi-dimensional datasets
Multidimensional data
Multidimensional data
Similarity
Data visualization
https://purl.org/pe-repo/ocde/ford#2.02.03
title_short Similarity-based visual exploration of very large georeferenced multidimensional datasets
title_full Similarity-based visual exploration of very large georeferenced multidimensional datasets
title_fullStr Similarity-based visual exploration of very large georeferenced multidimensional datasets
title_full_unstemmed Similarity-based visual exploration of very large georeferenced multidimensional datasets
title_sort Similarity-based visual exploration of very large georeferenced multidimensional datasets
author Peralta-Aranibar R.
author_facet Peralta-Aranibar R.
Comba J.L.D.
Pahins C.A.L.
Gomez-Nieto E.
author_role author
author2 Comba J.L.D.
Pahins C.A.L.
Gomez-Nieto E.
author2_role author
author
author
dc.contributor.author.fl_str_mv Peralta-Aranibar R.
Comba J.L.D.
Pahins C.A.L.
Gomez-Nieto E.
dc.subject.none.fl_str_mv Visualization
topic Visualization
Effective approaches
Effective approaches
Geographic information
Geographic information
Geographical locations
Interactive exploration
Interactive visualizations
Interactive visualizations
Multi-dimensional datasets
Multidimensional data
Multidimensional data
Similarity
Data visualization
https://purl.org/pe-repo/ocde/ford#2.02.03
dc.subject.es_PE.fl_str_mv Effective approaches
Effective approaches
Geographic information
Geographic information
Geographical locations
Interactive exploration
Interactive visualizations
Interactive visualizations
Multi-dimensional datasets
Multidimensional data
Multidimensional data
Similarity
Data visualization
dc.subject.ocde.none.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.03
description This work was supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.available.none.fl_str_mv 2024-05-30T23:13:38Z
dc.date.issued.fl_str_mv 2019
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12390/774
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1145/3297280.3297556
dc.identifier.scopus.none.fl_str_mv 2-s2.0-85065639857
url https://hdl.handle.net/20.500.12390/774
https://doi.org/10.1145/3297280.3297556
identifier_str_mv 2-s2.0-85065639857
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.none.fl_str_mv Proceedings of the ACM Symposium on Applied Computing
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Association for Computing Machinery
publisher.none.fl_str_mv Association for Computing Machinery
dc.source.none.fl_str_mv reponame:CONCYTEC-Institucional
instname:Consejo Nacional de Ciencia Tecnología e Innovación
instacron:CONCYTEC
instname_str Consejo Nacional de Ciencia Tecnología e Innovación
instacron_str CONCYTEC
institution CONCYTEC
reponame_str CONCYTEC-Institucional
collection CONCYTEC-Institucional
repository.name.fl_str_mv Repositorio Institucional CONCYTEC
repository.mail.fl_str_mv repositorio@concytec.gob.pe
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spelling Publicationrp01989600rp01988600rp01990600rp01234500Peralta-Aranibar R.Comba J.L.D.Pahins C.A.L.Gomez-Nieto E.2024-05-30T23:13:38Z2024-05-30T23:13:38Z2019https://hdl.handle.net/20.500.12390/774https://doi.org/10.1145/3297280.32975562-s2.0-85065639857This work was supported by grant 234-2015-FONDECYT (Master Program) from Cienciactiva of the National Council for Science, Technology and Technological Innovation (CONCYTEC-PERU) and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.Big data visualization is a main task for data analysis. Due to its complexity in terms of volume and variety, very large datasets are unable to be queried for similarities among entries in traditional Database Management Systems. In this paper, we propose an effective approach for indexing millions of elements with the purpose of performing single and multiple visual similarity queries on multidimensional data associated with geographical locations. Our approach makes use of Z-Curve algorithm to map into 1D space considering similarities between data. Additionally, we present a set of results using real data of different sources and we analyze the insights obtained from the interactive exploration.Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengAssociation for Computing MachineryProceedings of the ACM Symposium on Applied Computinginfo:eu-repo/semantics/openAccessVisualizationEffective approaches-1Effective approaches-1Geographic information-1Geographic information-1Geographical locations-1Interactive exploration-1Interactive visualizations-1Interactive visualizations-1Multi-dimensional datasets-1Multidimensional data-1Multidimensional data-1Similarity-1Data visualization-1https://purl.org/pe-repo/ocde/ford#2.02.03-1Similarity-based visual exploration of very large georeferenced multidimensional datasetsinfo:eu-repo/semantics/conferenceObjectreponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/774oai:repositorio.concytec.gob.pe:20.500.12390/7742024-05-30 15:36:05.016http://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="e7210f72-f615-420f-b092-4dc46cb9f485"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Similarity-based visual exploration of very large georeferenced multidimensional datasets</Title> <PublishedIn> <Publication> <Title>Proceedings of the ACM Symposium on Applied Computing</Title> </Publication> </PublishedIn> <PublicationDate>2019</PublicationDate> <DOI>https://doi.org/10.1145/3297280.3297556</DOI> <SCP-Number>2-s2.0-85065639857</SCP-Number> <Authors> <Author> <DisplayName>Peralta-Aranibar R.</DisplayName> <Person id="rp01989" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Comba J.L.D.</DisplayName> <Person id="rp01988" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Pahins C.A.L.</DisplayName> <Person id="rp01990" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Gomez-Nieto E.</DisplayName> <Person id="rp01234" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Association for Computing Machinery</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Visualization</Keyword> <Keyword>Effective approaches</Keyword> <Keyword>Effective approaches</Keyword> <Keyword>Geographic information</Keyword> <Keyword>Geographic information</Keyword> <Keyword>Geographical locations</Keyword> <Keyword>Interactive exploration</Keyword> <Keyword>Interactive visualizations</Keyword> <Keyword>Interactive visualizations</Keyword> <Keyword>Multi-dimensional datasets</Keyword> <Keyword>Multidimensional data</Keyword> <Keyword>Multidimensional data</Keyword> <Keyword>Similarity</Keyword> <Keyword>Data visualization</Keyword> <Abstract>Big data visualization is a main task for data analysis. Due to its complexity in terms of volume and variety, very large datasets are unable to be queried for similarities among entries in traditional Database Management Systems. In this paper, we propose an effective approach for indexing millions of elements with the purpose of performing single and multiple visual similarity queries on multidimensional data associated with geographical locations. Our approach makes use of Z-Curve algorithm to map into 1D space considering similarities between data. Additionally, we present a set of results using real data of different sources and we analyze the insights obtained from the interactive exploration.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1
score 13.466479
Similarity-based visual exploration of very large georeferenced multidimensional datasets
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La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).

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