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Reseach Article

A Way Ahead Towards Efficient Big Data Analytics: Prime Utilization in Businesses Moving Towards Cloud

Published on July 2016 by Rupali Sathe, Sandeep Raskar
International Conference on Internet of Things, Next Generation Networks and Cloud Computing
Foundation of Computer Science USA
ICINC2016 - Number 1
July 2016
Authors: Rupali Sathe, Sandeep Raskar
49d1cb0a-000f-4ca1-92c9-c3e7696d91a7

Rupali Sathe, Sandeep Raskar . A Way Ahead Towards Efficient Big Data Analytics: Prime Utilization in Businesses Moving Towards Cloud. International Conference on Internet of Things, Next Generation Networks and Cloud Computing. ICINC2016, 1 (July 2016), 14-17.

@article{
author = { Rupali Sathe, Sandeep Raskar },
title = { A Way Ahead Towards Efficient Big Data Analytics: Prime Utilization in Businesses Moving Towards Cloud },
journal = { International Conference on Internet of Things, Next Generation Networks and Cloud Computing },
issue_date = { July 2016 },
volume = { ICINC2016 },
number = { 1 },
month = { July },
year = { 2016 },
issn = 0975-8887,
pages = { 14-17 },
numpages = 4,
url = { /proceedings/icinc2016/number1/25523-4747/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Internet of Things, Next Generation Networks and Cloud Computing
%A Rupali Sathe
%A Sandeep Raskar
%T A Way Ahead Towards Efficient Big Data Analytics: Prime Utilization in Businesses Moving Towards Cloud
%J International Conference on Internet of Things, Next Generation Networks and Cloud Computing
%@ 0975-8887
%V ICINC2016
%N 1
%P 14-17
%D 2016
%I International Journal of Computer Applications
Abstract

Nowadays Businesses have greatly benefited from data analytics. Companies analyze data from various activities like fraud, sales, risk management, marketing, inventory optimization, and customer support to improve their strategic and tactical business decisions. However, analyticsis powerful enough to work with big data which is too complex, expensive, difficult for computation and resource-intensive for smaller companies to use. However, all these businesses have not been able to benefit from high powered analytics and therefore cannot make the most out of their information. Big data administration generally require more no of IT staff. It also uses many expensive servers with high configuration and includes software that is very difficult to set up and maintain. Organizations require innovative technology or systems that should be able to handle complex data to get the appropriate output. Smaller companies are facing trouble in finding employees capable of working with big analytics. This field deals with advanced and complex technology and new area of technology growing rapidly. All above mentioned factors made big data analytics fitted only to the large organizations. The above requirements are accomplished by proposing a system which performs adopting cloud as a platform to work with big data, which will help to make big analytic easier to handle the analytics and provides on demand cost efficient platform with great horizontal scalability. This computational methodology and algorithm for big data in the cloud environment make their platform more accessible. This new paradigm will play a leading role in the near future.

References
  1. Stephen Kaisler, Frank Armour, J. Alberto Espinosa, William Money, 2013 "Big Data: Issues and Challenges Moving Forward", IEEE, 46th Hawaii International Conference on System Sciences.
  2. Yuri Demchenko, Zhiming Zhao, Paola Grosso, AdiantoWibisono, Cees de Laat,2012 "Addressing Big Data Challenges for Scientific Data Infrastructure", IEEE ,,4th International Conference on Cloud Computing Technology and Science.
  3. R. Ranjan, May 2014 "Streaming Big Data Processing in Datacenters Clouds", IEEE Cloud Computing, Blue Skies Column, Vol. 1, No. 1, Pp. 78–83.
  4. Sachidanand Singh, Nirmala Singh,2012 "Big Data Analytics", IEEE, International Conference on Communication, Information & Computing Technology.
  5. S. Loughran, J. AlcarazCalero, A. Farrell, J. Kirschnick, and J. Guijarro,Nov. 2012 "Dynamic cloud deployment of a map reduce architecture,"IEEEInternetComput. Vol. 16.
  6. B. F. Cooper, A. Silberstein, E. Tam, R. Ramakrishnan, and R. Sears,2010 ``Benchmarking cloud serving systems with YCSB,'' in Proc. ACM Symp. Cloud Computing. Pp. 43_154.
  7. Michael, K. and Miller, K. W. (2013) Big Data: New Opportunities and New Challenges. Journal of IEEE Computer Society, 46, 22-24
  8. G. Jung, N. Gnanasambandam, T. Mukherjee, 2012, Synchronous Parallel Processing of Big-Data Analytics Services to Optimize Performance in Federated Clouds, in: Proceedings of the IEEE 5th International Conference on Cloud Computing (Cloud 2012Pp. 811–818.
  9. Zulkernine, F. , Martin, P. , Ying Zou, F. ; Aboulnaga, A. , "Towards Cloud-Based Analytics-as-a-Service (CLAaaS) for Big Data Analytics in the Cloud," Big Data (Big Data Congress), 2013 IEEE International Congress on Big Data, Vol. , No. , Pp. 62, 69
  10. A. Thusoo, J. S. Sarma, N. Jain, Z. Shao, P. Chakka, S. Anthony, H. Liu, P. Wyckoff,and R. Murthy. 2009 Hive: a warehousing solution over a map-reduce framework. Proc.
Index Terms

Computer Science
Information Sciences

Keywords

Scalability cloud Analytics.