Applications of Big Data in various Domains
Keywords:
Big Data, Cloud Computing, Data Mining, Business, Hadoop and Map ReduceAbstract
The term Big data is very popular recently in all the domains. Every where and every body talking about big data numerously. The goal of this paper is to describe what is big data and how it can be used in various applications. The rising number of applications serving millions of users and dealing with terabytes of data need to a faster processing paradigms. Recently, there is growing enthusiasm for the notion of big data analysis. Big data analysis becomes a very important aspect for growth productivity, reliability and quality of services. Processing of big data using a powerful machine is not efficient solution. So, companies focused on using Hadoop software for big data analysis. This is because Hadoop designed to support parallel and distributed data processing. Hadoop provides a distributed file processing system that stores and processes a large scale of data. The author tries to give the introduction about Hadoop and Map Reduce architecture. The main goal of this paper is applications of big data in various domains and how to build decision support system using big data. Big data have applications in many fields such as Business, Technology, Health Care, Smart cities etc. These applications will allow people to have better services, better customer experiences, and also to prevent and detect illness much easier than before.
References
Anchalia, P.P.; Koundinya, A.K.; Srinath, N.K., "MapReduce Design of K-Means Clustering Algorithm," International Conference onInformation Science and Applications (ICISA), pp.1,5, 24-26 June 2013, doi:10.1109/ICISA.2013.6579448.
“Big Data, Big Impact: New Possibilities for International Development.” World Economic Forum (2012): 1-9. Vital Wave Consulting. Jan. 2012
Big data: The next frontier for innovation, competition, and productivity. James Manyika, Michael Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers. McKinsey Global Institute. May 2011.
C.Lam, “Hadoop in Action”, Manning Publications Co., USA,ISBN:9781935182191, Dec. 2010.
King, Gary. “Ensuring the Data-Rich Future of Social Science.” Science Mag 331 (2011) 719-721. 11 Feb, 2011 Web.
Keim, Daniel, Huamin Qu, and Kwan-Liu Ma. "Big-Data Visualization." Computer Graphics and Applications, IEEE 33.4 (2013): 20-21.
Lakew, Ewnetu Bayuh. Managing Resource Usage and Allocations in Multi-Cluster Clouds. 2013, http://www8.cs.umu.se/~ewnetu/papers/lic.pdf
Monga, Inder, Eric Pouyoul, and Chin Guok. Software-Defined Networking for Big-Data Science-Architectural Models from Campus to the WAN. High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion:. IEEE, 2012.
Russom, “ Big Data Analytics”, TDWI Research, 2011.
Richa Gupta, Sunny Gupta, Anuradha Singhal, (2014), “Big Data:Overview”, IJCTT, 9 (5). [11]S.Perera, T.Gunarathne, “Hadoop MapReduce Cookbook”, Packt Publishing, ISBN:1849517282,Jan. 2013.
Turn Big Data into Big Value, A Practical Strategy, Intel White Paper,2013.
T. H. Davenport and J. Dyche, "Big Data in Big Companies," May 2013, 2013.
Wei Fan and Albert Bifet “ Mining Big Data:Current Status and Forecast to the Future”,Vol 14,Issue 2,2013
Wu, Xindong, et al. "Data mining with big data." Knowledge and Data Engineering, IEEE Transactions on 26.1 (2014): 97-107.
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