Visual Analytics: A Comprehensive Overview

• Published in: IEEE access 2019, Vol.7, p.81555-81573
• Main Author: Cui, Wenqiang
• Format: Article
• Language: English
• Subjects: Algorithms; analytical reasoning; Astronomy; Classification; Cognition; Data analysis; Data collection; Data mining; Data processing; Data storage; Data visualization; Domains; high-dimensional data; information visualization; interactive visualization; knowledge representations; Mathematical analysis; perception; sense-making; Task analysis; Visual analytics; visual data mining; Visual fields; Visualization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2923736

With the ever-increasing amount of data, the world has stepped into the era of "Big Data". Presently, the analysis of massive and complex data and the extraction of relevant information, have been become essential tasks in many fields of studies, such as health, biology, chemistry, social science, astronomy, and physics. However, compared with the development of data storage and management technologies, our ability to gain useful information from the collected data does not match our ability to collect the data. This gap has led to a surge of research activity in the field of visual analytics. Visual analytics employs interactive visualization to integrate human judgment into algorithmic data-analysis processes. In this paper, the aim is to draw a complete picture of visual analytics to direct future research by examining the related research in various application domains. As such, a novel categorization of visual-analytics applications from a technical perspective is proposed, which is based on the dimensionality of visualization and the type of interaction. Based on this categorization, a comprehensive survey of visual analytics is performed, which examines its evolution from visualization and algorithmic data analysis, and investigates how it is applied in various application domains. In addition, based on the observations and findings gained in this survey, the trends, major challenges, and future directions of visual analytics are discussed.