Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/340218
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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.contributor.authorunicampVargas, Aurea Rossy Soriano-
dc.typeArtigopt_BR
dc.titleTV-MV analytics : a visual analytics framework to explore time-varying multivariate datapt_BR
dc.contributor.authorSoriano-Vargas, Aurea-
dc.contributor.authorHamann, Bernd-
dc.contributor.authorOliveira, Maria Cristina F de-
dc.subjectVariação temporalpt_BR
dc.subjectAnálise de dadospt_BR
dc.subject.otherlanguageTemporal variationpt_BR
dc.subject.otherlanguageData analyticspt_BR
dc.description.abstractWe present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain expertspt_BR
dc.relation.ispartofInformation visualizationpt_BR
dc.publisher.cityLondonpt_BR
dc.publisher.countryReino Unidopt_BR
dc.publisherSagept_BR
dc.date.issued2020-01-
dc.date.monthofcirculationJan.pt_BR
dc.language.isoengpt_BR
dc.description.volume19pt_BR
dc.description.issuenumber1pt_BR
dc.description.firstpage3pt_BR
dc.description.lastpage23pt_BR
dc.rightsFechadopt_BR
dc.sourceWOSpt_BR
dc.identifier.issn1473-8716pt_BR
dc.identifier.eissn1473-8724pt_BR
dc.identifier.doi10.1177/1473871619858937pt_BR
dc.identifier.urlhttps://journals.sagepub.com/doi/full/10.1177/1473871619858937pt_BR
dc.description.sponsorshipCONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQpt_BR
dc.description.sponsorshipFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPpt_BR
dc.description.sponsordocumentnumber301847/ 2017-7pt_BR
dc.description.sponsordocumentnumber12/24537-0; 15/12831-0; 17/05838-3pt_BR
dc.date.available2020-05-06T13:37:56Z-
dc.date.accessioned2020-05-06T13:37:56Z-
dc.description.provenanceSubmitted by Susilene Barbosa da Silva (susilene@unicamp.br) on 2020-05-06T13:37:56Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-08-27T19:16:50Z : No. of bitstreams: 1 000481486300001.pdf: 3960330 bytes, checksum: 6f63f71382176c6d9a1f2384ba5cde16 (MD5)en
dc.description.provenanceMade available in DSpace on 2020-05-06T13:37:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-01en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/340218-
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.subject.keywordVisual analyticspt_BR
dc.subject.keywordVisual feature selectionpt_BR
dc.subject.keywordData visualizationpt_BR
dc.identifier.source000481486300001pt_BR
dc.creator.orcid0000-0002-8429-4119pt_BR
dc.type.formArtigopt_BR
dc.description.sponsorNoteThe author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work received financial support of the State of Sa˜o Paulo Research Foundation (FAPESP) (grants 12/24537-0, 15/ 12831-0, and 17/05838-3), and from the Brazilian National Research Council (CNPq) (grant 301847/ 2017-7)pt_BR
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