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Cloud Based Heterogeneous Big Data Integration and Data Analysis for Business Intelligence

  • T. JayarajEmail author
  • J. Abdul Samath
Conference paper
  • 44 Downloads
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 49)

Abstract

Due to the enormous growth of information technology, a huge amount of big data is produced daily, wherein heterogeneity is considered as the main feature of big data. Heterogeneous data integration is still remaining as a bottleneck. It becomes as a very difficult task to integrate and complete the business information demands. Hence, in this research work we have presented a novel Heterogeneous Data Integration and Analysis framework for solving the challenges associated with heterogeneous big data. Big data analysis is an information extraction technique generally used by organizations for business intelligence. However, data mining doesn’t provide good performance for very large data set due to the problems of high computational cost and lack of memory. In this article, we have proposed Convolutional Neural Networks (CNN) architecture for heterogeneous big data analysis. Finally, experimental results make it clear that the proposed method is the fastest data integration framework and that it is also considered as a good analysis model for business.

Keywords

Big data Heterogeneous data Data integration Data analysis Multisource data 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Research and Development CentreBharathiar UniversityCoimbatoreIndia
  2. 2.Chikkana Government Arts and Science CollegeTiruppurIndia

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