Big data density analytics using parallel coordinate visualization

Publisher:
Institute of Electrical and Electronics Engineers Inc.
Publication Type:
Conference Proceeding
Citation:
Proceedings - 17th IEEE International Conference on Computational Science and Engineering, CSE 2014, Jointly with 13th IEEE International Conference on Ubiquitous Computing and Communications, IUCC 2014, 13th International Symposium on Pervasive Systems, Algorithms, and Networks, I-SPAN 2014 and 8th International Conference on Frontier of Computer Science and Technology, FCST 2014, 2014, pp. 1115 - 1120
Issue Date:
2014
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Parallel coordinate is a popular tool for visualizing high-dimensional data and analyzing multivariate data. With the rapid growth of data size and complexity, data clutter in parallel coordinates is a major issue for Big Data visualization. This has given rise to three problems; 1) how to rearrange the parallel axes without the loss of data patterns, 2) how to shrink data attributes on each axis without the loss of data trends, 3) how to visualize the structured and unstructured data patterns for Big Data analysis. In this paper, we introduce the 5Ws dimensions as the parallel axes and establish the 5Ws sending density and receiving density as additional axes for Big Data visualization. Our model not only demonstrates Big Data attributes and patterns, but also reduces data over-lapping by up to 80 percent without the loss of data patterns. Experiments show that this new model can be efficiently used for Big Data analysis and visualization.
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