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

Author(s)

Soriano-Vargas, Aurea ^[1] ; Hamann, Bernd ^[2] ; de Oliveira, Maria Cristina F. ^[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

• 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