Manuscript Number : GISRRJ19255

Performance Optimization in Big Data Predictive Analytics

Authors(2) :-Dr. S. Saravana Kumar, Dr. Natarajan

Big Data moves around 5 Vs- volume, velocity, variety, value and veracity. Storing huge volume of data available in various formats which is increasing with high velocity to gain values out it is itself a big deal. Large business organizations in various domains are looking forward to get maximum out of this big data solutions to compete in business world. Making right decisions on right time is the logic of business. High speed Query execution from large datasets is based on the storage structure. The approach to solve the problem is to monitor the query execution speed for Predictive analytics on Big Datasets and providing solutions to speed up the query execution using various predictive models and data mining techniques which results in enhancing the predictive scores and business values. This will results in high and more précised predictive scores on time which helps in maximizing the productivity of people, processes and assets of an organization. It can be helpful in detecting and preventing threats and frauds before they affect the organization.

Authors and Affiliations

Dr. S. Saravana Kumar
Assistant Professor, Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India
Dr. Natarajan
Professor, Department of Computer Science, Karpagam Academy of Higher Education, Coimbatore, Tamil Nadu, India

Big Data, Predictive Analytics, Datasets, Query Execution, Data Mining

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Publication Details

Published in : Volume 2 | Issue 5 | September-October 2019
Date of Publication : 2019-10-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 21-26
Manuscript Number : GISRRJ19255
Publisher : Technoscience Academy

ISSN : 2582-0095

Cite This Article :

Dr. S. Saravana Kumar, Dr. Natarajan, "Performance Optimization in Big Data Predictive Analytics", Gyanshauryam, International Scientific Refereed Research Journal (GISRRJ), ISSN : 2582-0095, Volume 2, Issue 5, pp.21-26, September-October.2019
URL : https://gisrrj.com/GISRRJ19255

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