BigData visualization: Parallel coordinates using density approach
Jinson Zhang, Zhaopeng Meng, Mao Lin Huang · 2014
Information visualization is a very important tool in BigData analytics. BigData, structured and unstructured data which contains images, videos, texts, audio and other forms of data, collected from multiple datasets, is too big, too complex and moves too fast to analyse using traditional methods. This has given rise to two issues; 1) how to reduce multidimensional data without the loss of any data patterns for multiple datasets, 2) how to visualize BigData patterns for analysis. In this paper, we have classified the BigData attributes into `5Ws' data dimensions, and then established a `5Ws' density approach that represents the characteristics of data flow patterns. We use parallel coordinates to display the `5Ws' sending and receiving densities which provide more analytic features for BigData analysis. The experiment shows that this new model with parallel coordinate visualization can be efficiently used for BigData analysis and visualization.