CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Challenges in Big Data Application: A Review

by Satanand Mishra, Vijay Dhote, G. S. Prajapati, J.p. Shukla
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 19
Year of Publication: 2015
Authors: Satanand Mishra, Vijay Dhote, G. S. Prajapati, J.p. Shukla
10.5120/21651-4962

Satanand Mishra, Vijay Dhote, G. S. Prajapati, J.p. Shukla . Challenges in Big Data Application: A Review. International Journal of Computer Applications. 121, 19 ( July 2015), 42-46. DOI=10.5120/21651-4962

@article{ 10.5120/21651-4962,
author = { Satanand Mishra, Vijay Dhote, G. S. Prajapati, J.p. Shukla },
title = { Challenges in Big Data Application: A Review },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 19 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 42-46 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number19/21651-4962/ },
doi = { 10.5120/21651-4962 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:08:52.733165+05:30
%A Satanand Mishra
%A Vijay Dhote
%A G. S. Prajapati
%A J.p. Shukla
%T Challenges in Big Data Application: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 19
%P 42-46
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

New invention of advanced technology, enhanced capacity of storage media, maturity of information technology and popularity of social media, business intelligence and Scientific invention, produces huge amount of data which made ample set of information that is responsible for birth of new concept well known as big data. Big data analytics is the process of examining large amounts of data. The analysis is done on huge amount of data which is structure, semi structure and unstructured. In big data, data is generated at exponentially for reason of increase use of social media, email, document and sensor data. The growth of data has affected all fields, whether it is business sector or the world of science. In this paper, the process of system is reviewed for managing "Big Data" and today's activities on big data tools and techniques.

References
  1. Gartner IT glossary Big data. http://www. gartner. com/it-glossary/big-data Accessed 25 June 2014.
  2. Zikipoulos, P. , Deutsch, T. , Deroos, D. , Parasuraman, K. , Corrigan, D. and Giles J. Y. 2012. Harness the Power of Big Data.
  3. Gartner, Big Data Definition, http://www. gartner. com/it-glossary/big-data/
  4. "Data,data every where"The Economist. 25 February 2010. Retrieved9 December 2012
  5. Hilbert, M. and Lopez, P. 2011. "The World's Technological Capacity to Store, Communicate, and Compute Information"
  6. http://www. slideshare. net/HarshMishra3/harsh-big-data-seminar-report
  7. http://hive. apache. org/.
  8. http://searchdatamanagement. techtarget. com/definition/big-data management.
  9. https://www. talend. com/resource/big-data-management. html
  10. Ghemawat, S. , Gobioff, H. and Leung, S. T. 2003. The google file system. In Proc. 19th ACM Symp. On Operating Systems Principles, SOSP '03, New York, NY, USA, ACM.
  11. Addressing big data problem using Hadoop and Map Reduce
  12. Garlasu, D. , Sandulescu, V. , Halcu, I. and Neculoi G. 2013. "A Big Data implementation based on Grid Computing", Grid Computing.
  13. Pitre, R. , and Kolekar, V. 2014. Describe the big data is a new term used to identify the data sets due to their large size and complexity.
  14. Zou, H. , Yu, Y. , Tang, W. and Chen, H. W. M. 2014. Carried out a flexible data analytics framework for big data application with I/O performance improvement.
  15. Hudda, M. M. G. and Ramannavar, M. M. 2013. Propose an increase in use of social media forums, email, document and sensor data etc.
  16. Kaur, M. , and Shilpa, 2013. Describe the big data and methodology.
  17. Sagiroglu, S. and Sinanc, D. 2013. Introduce the review of big data.
  18. Fahad, A. , Alshatri, N. , Tari, Z. , Alamri, A. , Khalil, I. , Zomaya, A. Y. , Foufou, S. and Bouras, A. 2014. Describe the clustering algorithm for big data.
  19. Armour, F. , Kaisler, S. and Espinosa, J. A. , Money W. 2013. Illustrated the issues and challenges in big data.
  20. Lee, K. H. , Choi, T. W. , Ganguly, A. , Wolinsky, D. I. , Boykin, P. O. and Figueired, R. 2011. Presents the parallel data processing with map Reduce.
  21. Grolinger, K. ,Michael, Hayes, Higashino, W. , Heureux ,A. L. , Allison, D. S. and Capretz ,M. A. M. 2014. Discuss the challenges for mapreduce in big data.
  22. Dr. Siddaraju, Sowmya, C. L. , Rashmi, K. and Rahul M. 2014. Carried out the analysis of big data using mapreduce framework.
  23. Ekanayak, J. , Pallickara, S. and Fox, G. 2008. Proposes the MapReduce for data intensive for scientific analysis.
  24. Luo, Y. and Plale, B. 2012. Carried out the hierarchical mapreduce programming model and scheduling algorithms.
  25. Dean, J. and Ghemawat, S. 2004. Illustrated the Simplifed Data Processing on Large Clusters.
  26. Natarajan, S. and Sehar, S. 2013. Presents the algorithm for distributed data mining in HDFS.
  27. Manikandan, S. G. and Ravi, S. 2014. Discuss Big Data Analysis using Apache Hadoop.
  28. Kala Karun, A. and Chitharanjan, K. 2013. Discuss A Review on Hadoop HDFS Infrastructure Extensions.
  29. Bifet, A. 2012. Present a mining big data in real time and discuss the data streams.
  30. Mukherjee, A. , Datta, J. , Jorapur, R. , Singhvi, R. , Haloi, S. and Akram, W. 2012. Shared disk big data analytics with Apache Hadoop.
  31. Marz, N. and Warren, J. 2013. Big Data: Principles and best practices of scalable realtime data systems. Manning Publications.
  32. Feldman, D. , Schmidt, M. and Sohler, C. 2013. Turning big data into tiny data: Constant-size coresets for k-means, pca and projective clustering. In SODA.
  33. Fan, W. and Bifet, A. , Discribe the big data mining current status and forecast to the future.
  34. Patel, A. B. , Birla,M. and Nair, U. 2012. Presents Addressing Big Data Problem Using Hadoop and Map Reduce.
  35. Redid, K. K. and Indira, D. 2013. Introduce the knowledge that big data is combination of structured, semi-structured, unstructured homogeneous and heterogeneous data.
  36. Ousterhout, J. and Agrawal P. 2011 "The case for RAMCloud",Communications of the ACM, Vol. 54(7).
  37. Petrovic,J. 2008 "Using Memory cached for Data Distribution in IndustrialEnvironment", In Third International Conference on Systems(ICONS08),Cancun,
  38. Ousterhout, J. and Agrawal P. 2010 "The case for RAMCloud" ScalableHigh-Performance Storage Entirely in DRAM", ACM SIGOPSOperating Systems Review, Vol. 43 (4).
  39. Begoli,E. and Horey,J. 2012 "Design Principles for Effective Knowledge Discovery from Big Data", Software Architecture (WICSA) and European Conference on Software Architecture (ECSA) Joint Working IEEE/IFIP Conference on, Helsinki.
  40. Intel IT Center, "Planning Guide: Getting Started with Hadoop", Steps IT Managers Can Take to Move Forward with Big Data Analytics. http://www. intel. com/content/dam/www/public/us/en/documents/guides/getting-started-with-hadoop-planning-guide. pdf
Index Terms

Computer Science
Information Sciences

Keywords

Big data big data challenges and management Hadoop HDFS Hadoop component.

Powered by PhDFocusTM