Analysis on Big Data Using R Programming

Authors(2) :-Sangita, Shagun

Over the past decade, big data analysis has seen an exponential growth and will certainly continue to witness spectacular developments due to the emergence of new interactive multimedia applications and highly integrated systems driven by the rapid growth in information services and microelectronic devices. So far, most of the current mobile systems are mainly targeted to voice communications with low transmission rates. In the near future, however, big data access at high transmission rates will be. This is a result on accessible big-data systems that include a set of tools on R Studio and technique to load, extract, and improve dissimilar data while leveraging the immensely parallel processing power to perform complex transformations and analysis. “Big-Data” system faces a series of technical challenges.

Authors and Affiliations

M. Tech. Scholar, Department of Computer Science & Engineering Manav Institutes of Technology & Management, Haryana, India
Asstt. Professor, Department of Computer Science & Engineering Manav Institutes of Technology & Management, Haryana, India

Big Data, IDC, NIST, Big Data, EMC.

  1. Andrew Crotty, Alex Galakatos, Kayhan Dursun, Tim Kraska, Ugur Cetintemel, Stan Zdonik, “Tupleware: “Big” Data, Big Analytics, Small Clusters”, 2016
  2. Jennifer Ortiz, Victor Teixeira de Almeida, Magdalena Balazinska, “Changing the Face of Database Cloud Services with Personalized Service Level Agreements”, 2015
  3. Anant Bhardwaj1, Souvik Bhattacherjee2, Amit Chavan2 Amol Deshpande2, Aaron J. Elmore1,3, Samuel Madden1, Aditya Parameswaran, “DataHub: Collaborative Data Science & Dataset Version Management at Scale”, 2015
  4. Challenges and Opportunities with Big Data
  5. Hongbo Zou, Yongen Yu, Wei Tang, Hsuan- Wei Michelle Chen, “Flex Analytics: A Flexible Data Analytics Framework for Big Data Application with I/O Performance Improvement”, Elsevier 2014
  6. Alekh Jindal, Robust Data Transformations, 2015
  7. Radu Tudoran, “High-Performance Big Data Management Across Cloud Data Centers”, Jan 2015
  8. Bill Howe, “Big Data Science Needs Big Data Middleware”, Jan 2015
  9. Burt L. Monroe , Jennifer Pan , Margaret E. Roberts, Maya Sen , Betsy Sinclair, “No! Formal Theory, Causal Inference, and Big Data Are Not Contradictory Trends in Political Science”, American Political Science Association, 2015 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
  10. Han hu1At. Al. (Fellow, IEEE),” Toward Scalable Systems for Big Data Analytics: A Technology Tutorial”, IEEE 2169-3536(2014),PP 652-687
  11. 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
  12. 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
  13. Zhen Jia1 At. Al.“Characterizing and Subsetting Big Data Workloads", IEEE 978-1-4799-6454-3/14, PP 191-201
  14. AvitaKatal, Mohammad Wazid, R H Goudar, “Big Data: Issues, Challenges, Tools and Good Practices”, IEEE 978-1-4799-0192-0/13,PP 404-409
  15. Du Zhang,” Inconsistencies in Big Data”, IEEE 978-1-4799-0783-0/13, PP 61-67
  16. ZibinZheng, Jieming Zhu, and Michael R. Lyu, “Service-generated Big Data and Big Data-as-a-Service: An Overview”, IEEE (International Congress on Big Data) 978-0-7695-5006-0/13, PP 403-410
  17.  Lei,Wang At. Al., “BigDataBench: aBig Data Benchmark Suite from Internet Services”,IEEE 978-1-4799-3097-5/14.
  18. 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
  19. SAS, Five big data challenges and how to overcome them with visual analytics
  20. 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
  21. 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
  22. 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
  23. Joseph Rickert, “Big Data Analysis with Revolution R Enterprise”, 2011
  24. 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

Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 747-752
Manuscript Number : CSEIT1835149
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Sangita, Shagun, "Analysis on Big Data Using R Programming", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.747-752, May-June-2018.
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