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Please use this identifier to cite or link to this item: http://hdl.handle.net/1843/56960
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dc.creatorRoozbeh Haghnazar Koochaksaraeipt_BR
dc.creatorFrederico Gadelha Guimarãespt_BR
dc.creatorBabak Hamidzadehpt_BR
dc.creatorSarfaraz Hashemkhani Zolfanipt_BR
dc.date.accessioned2023-07-25T17:32:17Z-
dc.date.available2023-07-25T17:32:17Z-
dc.date.issued2021-
dc.citation.volume9pt_BR
dc.citation.issue9pt_BR
dc.citation.spage940pt_BR
dc.citation.epage946pt_BR
dc.identifier.doihttps://doi.org/10.3390/math9090940pt_BR
dc.identifier.issn2227-7390pt_BR
dc.identifier.urihttp://hdl.handle.net/1843/56960-
dc.description.resumoData and information visualization have drawn an increasingly wide range of interest from several academic fields and industries. Concurrently, exploring a huge set of data to support feasible decisions needs an organized method of Multi-Criteria Decision Making (MCDM). The dramatic increasing of data producing during the past decade makes visualization necessary as a presentation layer on the top of MCDM process. This study aims to propose an integrated strategy to rank the alternatives in the dataset, by combining data, MCDM methods, and visualization layers. In fact, the well designed combination of Information Visualization and MCDM provides a more user-friendly approach than the traditional methods. We investigate a case study in bibliometric analyses, which have become an important dimension and tool for evaluating the impact and performance of researchers, departments, and universities. Hence, finding the best and most reliable papers, authors, and publishers considering diverse criteria is one of the important challenges in science world. Therefore, this text is presenting a new strategy on the bibliometric dataset as a case study and it demonstrates that this strategy can be more meaningful for the end users than the current tools. Finally, the presented simulations illustrate the performance and utilization of this combination. In other words, the researchers of this study could design and implement a tool that overcomes the biggest challenges of data analyzing and ranking via a combination of MCDM and visualization methodologies that can provide a tremendous amount of insight and information from a massive dataset in an efficient way.pt_BR
dc.format.mimetypepdfpt_BR
dc.languageengpt_BR
dc.publisherUniversidade Federal de Minas Geraispt_BR
dc.publisher.countryBrasilpt_BR
dc.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELÉTRICApt_BR
dc.publisher.departmentENGENHARIA - ESCOLA DE ENGENHARIApt_BR
dc.publisher.initialsUFMGpt_BR
dc.relation.ispartofMathematicspt_BR
dc.rightsAcesso Abertopt_BR
dc.subjectData visualizationpt_BR
dc.subjectVIKORpt_BR
dc.subjectMulti-criteria decision makingpt_BR
dc.subjectRelational analyzingpt_BR
dc.subjectRelational analyzingpt_BR
dc.subjectBibliographic networkspt_BR
dc.subjectBibliometricspt_BR
dc.subject.otherVisualização da informaçãopt_BR
dc.subject.otherDecision makingpt_BR
dc.subject.otherEngenharia elétricapt_BR
dc.subject.otherBibliometriapt_BR
dc.titleVisualization method for decision-making: a case study in bibliometric analysispt_BR
dc.typeArtigo de Periódicopt_BR
dc.url.externahttps://www.mdpi.com/2227-7390/9/9/940pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0003-4656-9887pt_BR
dc.identifier.orcidhttps://orcid.org/0000-0001-9238-8839pt_BR
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