BigData, defined as structured and unstructured data containing images,#R##N#   videos, texts, audio and other forms of data collected from multiple#R##N#   datasets, is too big, too complex and moves too fast to analyze using#R##N#   traditional methods. This has given rise to a few issues that must be#R##N#   addressed; 1) how to analyze BigData across multiple datasets, 2) how to#R##N#   classify the different data forms, 3) how to identify BigData patterns based#R##N#   on its behaviours, 4) how to visualize BigData attributes in order to gain a#R##N#   better understanding of data. It is therefore necessary to establish a new#R##N#   framework for BigData analysis and visualization. In this paper, we have#R##N#   extended our previous works for classifying the BigData attributes into the#R##N#   "5Ws" dimensions based on different data behaviours. Our approach not only#R##N#   classifies BigData attributes for different data forms across multiple#R##N#   datasets, but also establishes the "5Ws" densities to represent the#R##N#   characteristics of data flow patterns. We use additional non-dimensional#R##N#   parallel axes in parallel coordinates to display the „5Ws‟ sending and#R##N#   receiving densities, which provide more analytic features for BigData#R##N#   analysis. The experiment shows that our approach with parallel coordinate#R##N#   visualization can be efficiently used for BigData analysis and visualization.
['Jinson Zhang', 'Mao Lin Huang', 'Zhaopeng Meng']
Visual analytics for BigData variety and its behaviours