TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data
| Autor: | Aurea Soriano-Vargas, Bernd Hamann, Maria Cristina Ferreira de Oliveira |
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| Rok vydání: | 2019 |
| Předmět: |
ESTUDO DE CASO
Multivariate statistics Visual analytics business.industry Computer science 020207 software engineering 02 engineering and technology Data science Data visualization Analytics 0202 electrical engineering electronic engineering information engineering Data analysis 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition business |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| ISSN: | 1473-8724 1473-8716 |
| DOI: | 10.1177/1473871619858937 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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