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Title: Data behaviours model for Big Data visual analytics
Authors: Jinson Zhang; Mao Lin Huang
School of Software, Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia
School of Computer Software, Tianjin University, Tianjin, China; School of Software, Faculty of Engineering and Information Technology, University of Technology, Sydney, Australia
Abstract: 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.
Keywords: big data analytics; visual analytics; 5Ws dimensions; 5Ws density; parallel coordinate; information visualisation; big data visualisation; sending density; content density; transfer density; purpose density; receiving density.
Int. J. of Big Data Intelligence, 2016 Vol.3, No.1, pp.1 - 17
Submission date: 08 Mar 2015
Date of acceptance: 09 May 2015
Available online: 29 Dec 2015