Analysis of different pattern evaluation procedures for big data visualization in data analysis

Abstract

Data visualization is the main focusing concept in big data analysis for processing and analyzing multi variate data, because of rapid growth of data size and complexity of data. Basically data visualization may achieve three main problems, i.e. 1. Structured and Unstructured pattern evaluation in big data analysis. 2. Shrink the attributes in data indexed big data analysis. 3. Rearrange of attributes in parallel index based data storage. So in this paper we analyze different techniques for solving above three problems with feasibility of each client requirement in big data analysis for visualization in real time data stream extraction based on indexed data arrangement. We have analyzed different prototypes in available parallel co-ordinate and also evaluate quantitative exert review in real time configurations for processing data visualization. Report different data visualization analysis results for large and scientific data created by numerical simulation in practice sessions analysed in big data presentation.