TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data. (January 2020)

• Record Type: Journal Article
• Authors: Soriano-Vargas, Aurea; Hamann, Bernd; F de Oliveira, Maria Cristina
• Is Part Of: Information visualization. Volume 19:Number 1(2020)
• Page Start: 3
• Page End: 23
• Publication Date: 2020-01
• Subjects: Visual analytics -- time-varying multivariate data -- visual feature selection -- data visualization -- data analytics; Information visualization -- Periodicals; 006.605
• DOI: 10.1177/1473871619858937
• Languages: English
• ISSNs: 1473-8716

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.