2D approach measuring multidimensional data pattern in big data visualization

Abstract

Big Data, structured and unstructured data, contains millions attributes in multiple dimensions. This has arisen threeissues: 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 Datavariety based on the complexity of multidimensional data. Firstly, we introduce2D 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 createtwo 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.