Data behaviours model for Big Data visual analytics
Jinson Zhang, Mao Lin Huang · International Journal of Big Data Intelligence · 2015
Big Data is composed of text, image, video, audio, mobile or other forms of data collected from multiple datasets, and is rapidly growing in size and complexity. It has created a huge volume of multidimensional data within a very short time period. This raises several new challenges, including; how to classify Big Data for multiple datasets, how to analyse Big Data for different forms of data, and how to visualise Big Data without the loss of information. In this paper, we extended our 5Ws density methods to Big Data behaviours analysis and visualisation. Our approach classifies Big Data into the 5Ws dimensions based on the data behaviours, and then further creates the 5Ws densities to measure Big Data patterns across multiple datasets for any form of data. We also establish non-dimensional data axes as additional parallel axes for Big Data visualisation. The experimental results have shown that the proposed new model has significantly improved the accuracy of Big Data visualisation, and has large potential benefits and applications.