A survey on information visualization: recent advances and challenges

• Published in: The Visual computer Vol. 30; no. 12; pp. 1373 - 1393
• Main Authors: Liu, Shixia, Cui, Weiwei, Wu, Yingcai, Liu, Mengchen
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2014; Springer Nature B.V
• Subjects: Artificial Intelligence; Big Data; Classification schemes; Computer Graphics; Computer Science; Design; Empirical analysis; Image Processing and Computer Vision; Knowledge representation; Original Article; R&D; Research & development; Researchers; Scientific visualization; Taxonomy; Trends; Usability; Visualization; Interactive techniques; Large datasets; Information visualization
• ISSN: 0178-2789; 1432-2315
• DOI: 10.1007/s00371-013-0892-3

Information visualization (InfoVis), the study of transforming data, information, and knowledge into interactive visual representations, is very important to users because it provides mental models of information. The boom in big data analytics has triggered broad use of InfoVis in a variety of domains, ranging from finance to sports to politics. In this paper, we present a comprehensive survey and key insights into this fast-rising area. The research on InfoVis is organized into a taxonomy that contains four main categories, namely empirical methodologies, user interactions, visualization frameworks, and applications, which are each described in terms of their major goals, fundamental principles, recent trends, and state-of-the-art approaches. At the conclusion of this survey, we identify existing technical challenges and propose directions for future research.