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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

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Author(s):
Soriano-Vargas, Aurea [1] ; Hamann, Bernd [2] ; de Oliveira, Maria Cristina F. [3]
Total Authors: 3
Affiliation:
[1] Univ Estadual Campinas, Inst Comp, BR-13083872 Campinas, SP - Brazil
[2] Univ Calif Davis, Dept Comp Sci, Davis, CA 95616 - USA
[3] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil
Total Affiliations: 3
Document type: Journal article
Source: INFORMATION VISUALIZATION; v. 19, n. 1 JULY 2019.
Web of Science Citations: 0
Abstract

We 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 experts. (AU)

FAPESP's process: 17/05838-3 - Visual analytics: applications and a conceptual investigation
Grantee:Maria Cristina Ferreira de Oliveira
Support Opportunities: Regular Research Grants
FAPESP's process: 12/24537-0 - Visualization to support identifying relevant attributes in time-varying multidimensional data
Grantee:Aurea Rossy Soriano Vargas
Support Opportunities: Scholarships in Brazil - Doctorate
FAPESP's process: 15/12831-0 - Visual exploration to support the identification of relevant attributes in multidimensional time-varying data
Grantee:Aurea Rossy Soriano Vargas
Support Opportunities: Scholarships abroad - Research Internship - Doctorate