2D approach measuring multidimensional data pattern in big data visualization

Abstract

Abstract-Big Data, structured and unstructured data, contains millions attributes in multiple dimensions. This has arisen three issues: 1) how to measure the structured and unstructured multidimensional data patterns for Big Data analysis; 2) how to display multidimensional data patterns in normal size of screen; 3) how to optimize the data attributes in Big Data visualization. In this work, we have visual analyzed Big Data variety based on the complexity of multidimensional data. Firstly, we introduce 2D dimension which divided the multidimensional dataset into 2D data pattern subsets, and then establish 2D-Ratio algorithm to measure 2D dimension in multiple data patterns. Second, we create two additional parallel axes by using 2D-Ratio to compare 2D dimensional patterns for visualization. Third, the dimension clustering and shrunk attribute have been introduced in 2D-Ratio parallel coordinates to reduce the data over-crowed. The experiment shows that our model can be efficiently and accurately used for Big Data analysis and visualization.

A Distance-preserving Matrix Sketch

“…Parallel Coordinates, in various forms, are one of the most popular multidimensional visual analytic methods for big data (Inselberg, 2009;Zhang and Huang, 2016;Johansson et al, 2005). Their well-known weakness is visual clutter, caused both by many cases and many variables.…”

An n-Sided Polygonal Model to Calculate the Impact of Cyber Security Events

“…2D models have been proposed in a variety of domains [1,7,25,27] as synthetic and generic visualization models that overcome previous drawbacks from 3D representations. 2D models enable viewers to visualize the overall big picture and the interrelationships of various entities.…”