A New Analytics Model for Large Scale Multidimensional Data Visualization

Abstract

Abstract. With the rise of Big Data, the challenge for modern multidimensional data analysis and visualization is how it grows very quickly in size and complexity. In this paper, we first present a classification method called the 5Ws Dimensions which classifies multidimensional data into the 5Ws definitions. The 5Ws Dimensions can be applied to multiple datasets such as text datasets, audio datasets and video datasets. Second, we establish a Pair-Density model to analyze the data patterns to compare the multidimensional data on the 5Ws patterns. Third, we created two additional parallel axes by using pair-density for visualization. The attributes has been shrunk to reduce data over-crowding in pair-density parallel coordinates. This has achieved more than 80% clutter reduction without the loss of information. The experiment shows that our model can be efficiently used for Big Data analysis and visualization.