Visualization of Big Data with the Map-Reduce program execution platform: Hadoop
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2017 by IJETT Journal|
|Year of Publication : 2017|
|Authors : Sara Riahi, Azzeddine Riahi
|DOI : 10.14445/22315381/IJETT-V54P214|
1 Sara Riahi, 2 Azzeddine Riahi "Visualization of Big Data with the Map-Reduce program execution platform: Hadoop", International Journal of Engineering Trends and Technology (IJETT), V54(2),94-104 December 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
"Big data" is the fashionable term currently found in all professional conferences related to data science, predictive modeling, data mining, to name just a few areas literally electrified by the prospect of integrating larger datasets and data flows more quickly into their business processes and other organizational processes. As is often the case when new technologies begin to transform industries, new terminologies emerge, along with new approaches to conceptualize reality, solve problems, or improve processes. A few years ago, we limited ourselves to "segment" customers into groups that could acquire specific properties or services. It is now possible and common to build models for each customer in real time as they browse the Internet for specific properties: Instantly, prospects` interests are analyzed and it is possible to display highly targeted advertising, which is a level of personalization inconceivable only a few years ago. Inevitably, the disappointment may be up to expectations in many areas as technology around big data are promising. A limited number of data accurately describing a critical aspect of reality (vital to the business) is far more valuable than a deluge of data on less essential aspects of that reality. The purpose of this article is to clarify and highlight some interesting opportunities around big data, and illustrate how analytic platforms can leverage this wealth of data to optimize a process, solve problems, or improve customer knowledge.
 Azzeddine RIAHI, Sara RIAHI,’’ The Big Data Revolution, Issues and Applications’’, International Journal of Advanced Research in Computer Science and Software Engineering’’, Volume 5, Issue 8,ISSN: 2277 128X, August- 2015, pp. 167- 173.
 Sara Riahi, Azzeddine Riahi, ’’ Innovation and opportunities of Big Data: promote business Intelligence’’, ISSN: 2395-1303 ,International Journal of Engineering and Techniques - Volume 3 Issue 6, Nov-Dec 2017,pp 471- 481  Zhang Tingting, Qu Haipeng,’’ Range Query on Big Data Based on Map Reduce’’, IJMER | ISSN: 2249–6645 | www.ijmer.com | Vol. 4 | Iss. 2 | Feb. 2014.
 C. Wang, N. Cao, J. Li, K. Ren, and W. Lou, “Secure ranked keyword search over encrypted cloud data,” in Proceedings of ICDC .IEEE, 2010, pp. 253 – 262.
 Vahid Ashktorab1 , Seyed Reza Taghizadeh2 and Dr. Kamran Zamanifar3 ,” A Survey on Cloud Computing and Current Solution Providers”, International Journal of Application or Innovation in Engineering & Management (IJAIEM), October 2012.
 S. M. K. R. Sahal and F. A. Omara, "GPSO: An improved search algorithm for resource allocation in cloud databases", in Computer Systems and Applications (AICCSA), 2013 ACS International Conference on, (2013), pp. 1-8.
 Rodriguez MA, Buyya R,"Deadline based resource provisioning and scheduling algorithm for scientific workflows on clouds" ,IEEE Transactions on Cloud Computing. 2014; 2(2):222–35
 Mrs. Premalatha P, Mrs. Marrynal S. Eastaff, " Big Data and Cloud Computing" , International Journal of Engineering and Applied Sciences (IJEAS)ISSN: 2394-3661, Volume-2, Issue-11, November 2015
 Pandey S, Voorsluys W, Niu S, Khandoker A, Buyya R (2012) An autonomic Cloud environment for hosting ECG data analysis services. Future Generation Computer Systems 28: 147-154.
Ei Ei Mon, Thinn Thu Naing“THE PRIVACY-AWARE ACCESS CONTROL SYSTEM USINGATTRIBUTE-AND ROLE-BASEDACCESSCONTROL IN PRIVATE CLOUD”978 -1-61284-159-5/11,2011IEEE Navjot Sekhon, Richa Mahajan ,’’Data Security in Cloud Computing Using HDFS’’, International Journal of Computer Science Trends and Technology IJCST) Volume 5 Issue 2,ISSN: 2347-8578 ,Mar Apr 2017  Prashant V. Dhakad, Krishnakant Kishore, ’’ Processing of Real Time Big Data for Using High Availability of HadoopNameNode’’, International Journal of Computer Systems, ISSN-(2394-1065), Vol. 03, Issue 05, May, 2016
 E. B. K. Manash and T. U. Rani, “Cloud computing- A potential area for research”, International Journal of Computer
International Journal of Engineering Trends and Technology (IJETT) – Volume 54 Issue2- December 2017
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Trends and Technology (IJCTT), Volume: 25, no.1, pp.10-11, 2015.
 Gandomi Amir and Haidar Murtaza (2015) Beyond the hype: Big data concepts, methods,Analytics. International Journal of Information Management, 35, 137-144.
 R.Devankuruchi “Analysis of Big Data Over the Years” International Journal of Scientific and Research Publications, Volume 4, Issue 1, January 2014 1 ISSN 2250-3153
 Furqan Alam, Rashid Mehmood, Iyad Katib, Nasser N. Albogami, Aiiad Albeshri, "Data Fusion and IoT for Smart Ubiquitous Environments: A Survey", Access IEEE, vol. 5, pp. 9533-9554, 2017, ISSN 2169-3536.  Dobre, C., and F. Xhafa. 2014. “Parallel Programming Paradigms and Frameworks in Big Data Era.” International Journal of Parallel Programming 42 (5): 710–738.
 García, S., J. Luengo, and F. Herrera. 2015. “ Data Preprocessing in Data Mining.” Intelligent Systems Reference Library 72. doi:10.1007/978-3-319-10247-4
 Zaheer Khan, Ashiq Anjum, Saad Liaquat Kiani, “Cloud based Big Data Analytics for Smart Future Cities”, IEEE/ACM 6 th International Conference on Utility and Cloud Computing, 9-12 Dec 2013, Dresden, pp 381- 386, DOI: 10.1109/UCC.2013.77
Massive data, Analytics, Hadoop, HDFS, Map Reduce