AN ANALYTICAL REVIEW STUDY ON BIG DATA ANALYSIS USING R STUDIO
Keywords:Huge Statistics, Big Data Analysis, R Studio
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.
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”.
How to Cite
License and Copyright Agreement
In submitting the manuscript to the journal, the authors certify that:
- They are authorized by their co-authors to enter into these arrangements.
- The work described has not been formally published before, except in the form of an abstract or as part of a published lecture, review, thesis, or overlay journal.
- That it is not under consideration for publication elsewhere.
- That its release has been approved by all the author(s) and by the responsible authorities – tacitly or explicitly – of the institutes where the work has been carried out.
- They secure the right to reproduce any material that has already been published or copyrighted elsewhere.
- They agree to the following license and copyright agreement.
Authors who publish with International Journal of Engineering Technologies and Management Research agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors can enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or edit it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) before and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
For More info, please visit CopyRight Section