Analysis of Different Pattern Evaluation Procedures for Big Data Visualization in Data Analysis
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. Struct
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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. Keywords Data visualization analysis Pattern evaluation
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Big data analysis
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Parallel co-ordinate
1 Introduction Now a day’s explore data analysis is main focusing term in real time data streams for big data experts with preceding their research in big data analysis. Big data, consists both reliable and unreliable with multimedia applications like image, audio S.R. Madala (✉) ⋅ V.N. Rajavarman ⋅ T. Venkata Satya Vivek Department of Computer Science & Engineering, Dr. M.G.R Educational and Research Institute University, Chennai, India e-mail: [email protected] V.N. Rajavarman e-mail: [email protected] T. Venkata Satya Vivek e-mail: [email protected] © Springer Nature Singapore Pte Ltd. 2018 S.C. Satapathy et al. (eds.), Data Engineering and Intelligent Computing, Advances in Intelligent Systems and Computing 542, DOI 10.1007/978-981-10-3223-3_44
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and other forms of data collect from different container data bases rapidly increase size and complexity in data extraction with different resources like indexed attributes in labelled data [1, 2]. In many contributes analyst experts needs explore and complex data sets with respect to data points and based on variables present in real time data streams. To bolster great obvious information investigation, learning finding and hypothesis inspecting, we have customized and drawn out the possibility of comparable orchestrates, specifically binned or histogram-based comparable blends, for use with top of the line inquiry driven production of vast data. Analysts have recommended different other options to this issue by sketching out video cuts or envisioning the substance in different sorts that can prompt more powerful guide surfing around and disclosure. Process different frameworks based on principle component techniques that best appropriate for video information at sematic levels with associations and creation highlights for individual decision at more prominent levels of execution. For instance, finding a m
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