AN ANALYTICAL REVIEW STUDY ON BIG DATA ANALYSIS USING R STUDIO

  • Anita Kumari MTech Scholar, Dept of Computer Science & Engineering PPIMT, Hisar (Haryana), India
  • Neeraj Verma Assistant Professor, Dept of Computer Science & Engineering PPIMT, Hisar (Haryana), India
Keywords: Huge Statistics, Big Data Analysis, R Studio

Abstract

A larger amount of data gives a better output but also working with it can become a challenge due to processing limitations. Nowadays companies are starting to realize the importance of using more data in order to support decision for their strategies. It was said and proved through study cases that “More data usually beats better algorithms”. With this statement companies started to realize that they can chose to invest more in processing larger sets of data rather than investing in expensive algorithms. During the last decade, large statistics evaluation has seen an exponential boom and will absolutely retain to witness outstanding tendencies due to the emergence of new interactive multimedia packages and extraordinarily incorporated systems driven via the speedy growth in statistics services and microelectronic gadgets. Up to now, maximum of the modern mobile structures are especially centered to voice communications with low transmission fees.

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References

C.L. Philip Chen, Chun-Yang Zhang, “Data intensive applications, challenges, techniques and technologies: A survey on Big Data” Information Science 0020-0255 (2014), PP 341-347, elsevier DOI: https://doi.org/10.1016/j.ins.2014.01.015

Han hu1At. Al. (Fellow, IEEE),” Toward Scalable Systems for Big Data Analytics: A Technology Tutorial”, IEEE 2169-3536(2014),PP 652-687 DOI: https://doi.org/10.1109/ACCESS.2014.2332453

Shweta Pandey, Dr.VrindaTokekar, “Prominence of MapReduce in BIG DATA Processing”, IEEE (Fourth International Conference on Communication Systems and Network Technologies)978-1-4799-3070-8/14, PP 555-560

Katarina Grolinger At. Al. “Challenges for MapReduce in Big Data”, IEEE (10th World Congress on Services)978-1-4799-5069-0/14,PP 182-189

Zhen Jia1 At. Al. “Characterizing and Subsetting Big Data Workloads", IEEE 978-1-4799-6454- 3/14, PP 191-201

AvitaKatal, Mohammad Wazid, R H Goudar, “Big Data: Issues, Challenges, Tools and Good Practices”, IEEE 978-1-4799-0192-0/13,PP 404-409

Du Zhang,” Inconsistencies in Big Data”, IEEE 978-1-4799-0783-0/13, PP 61-67

ZibinZheng, Jieming Zhu, and Michael R. Lyu, “Service-generated Big Data and Big Data-as-aService: An Overview”, IEEE (International Congress on Big Data) 978-0-7695-5006-0/13, PP 403-410

VigneshPrajapati, Big Data Analytics with R and RStudioPackt Publishing

Lei Wang At. Al., “BigDataBench: aBigData Benchmark Suite from InternetServices”, IEEE 978-1-4799-3097-5/14.

AnirudhKadadi At. Al., “Challenges of Data Integration and Interoperability in Big Data”, IEEE (International Conference on Big Data)978-1-4799-5666-1/14, PP 38-40

SAS, Five big data challenges and how to overcome them with visual analytics

HajarMousanif At. Al., “From Big Data to Big Projects: a Step-by-step Roadmap”, IEEE (International Conference on Future Internet of Things and Cloud) 978-1-4799-4357-9/14, PP 373-378

Tianbo Lu At. Al., “Next Big Thing in Big Data: The Security of the ICT Supply Chain”, IEEE (SocialCom/PASSAT/BigData/EconCom/BioMedCom) 978-0-7695-5137-1/13, PP 1066-1073

Ganapathy Mani, NimaBarit, Duoduo Liao, Simon Berkovich, “Organization of Knowledge Extraction from Big Data Systems”, IEEE (4 Fifth International Conference on Computing for Geospatial Research and Application) 978-1-4799-4321-0/14, PP 63-69

Joseph Rickert, “Big Data Analysis with Revolution R Enterprise”, 2011

Carson Kai-Sang Leung, Richard Kyle MacKinnon, Fan Jiang, “Reducing the Search Space for Big Data Mining for Interesting Patterns from Uncertain Data”, IEEE 2014, PP 315-322

Ajith Abraham1, Swagatam Das2, and Sandip Roy3, “Swarm Intelligence Algorithms for Data Clustering”, PP 280-313

Swagatam Das, Ajith Abraham, Senior Member, IEEE, and Amit Konar, “Automatic Clustering Using an Improved Differential Evolution Algorithm”, IEEE 2008, PP 218-237 DOI: https://doi.org/10.1109/TSMCA.2007.909595

KarthikKambatla, GiorgosKollias, Vipin Kumar, AnanthGrama, “J. Parallel Distrib. Comput”, Elsevier 2014, PP 2561-2573 DOI: https://doi.org/10.1016/j.jpdc.2014.01.003

Yanchang Zhao, “R and Data Mining: Examples and Case Studies”, www.RDataMining.com,2014

H. T. Kahraman, Sagiroglu, S., Colak,“User Knowledge Modeling Data Set”, UCI, vol. 37, pp. 283-295, 2013 DOI: https://doi.org/10.1016/j.knosys.2012.08.009

Mrigank Mridul, Akashdeep Khajuria, Snehasish Dutta, Kumar N, “Analysis of Bidgata using Apache RStudio and Map”, Volume 4, Issue 5, May 2014 Reduce, PP. 555-560.

Sonja Pravilovic,” R language in data mining techniques and statistics”, 20130201.12,2013

Vrushali Y Kulkarni,” Random Forest Classifiers: A Survey and Future Research Directions”, International Journal of Advanced Computing, ISSN: 2051-0845, Vol.36, Issue.1, April 2013

Aditya Krishna Menon,” Large-Scale Support Vector Machines: Algorithms and Theory”.

Published
2019-06-30
How to Cite
Kumari, A., & Verma, N. (2019). AN ANALYTICAL REVIEW STUDY ON BIG DATA ANALYSIS USING R STUDIO . International Journal of Engineering Technologies and Management Research, 6(6), 116-122. https://doi.org/10.29121/ijetmr.v6.i6.2019.399