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Reseach Article

Hadoop MapReduce Framework: Performance Analysis in Business Intelligence

Published on April 2015 by Blessy Trenia Lincy.s.s, Archana.g.k.
National Conference on Information Processing and Remote Computing
Foundation of Computer Science USA
NCIPRC2015 - Number 1
April 2015
Authors: Blessy Trenia Lincy.s.s, Archana.g.k.
f8238c3d-ecc3-4814-97d3-bd1c9ba94658

Blessy Trenia Lincy.s.s, Archana.g.k. . Hadoop MapReduce Framework: Performance Analysis in Business Intelligence. National Conference on Information Processing and Remote Computing. NCIPRC2015, 1 (April 2015), 5-7.

@article{
author = { Blessy Trenia Lincy.s.s, Archana.g.k. },
title = { Hadoop MapReduce Framework: Performance Analysis in Business Intelligence },
journal = { National Conference on Information Processing and Remote Computing },
issue_date = { April 2015 },
volume = { NCIPRC2015 },
number = { 1 },
month = { April },
year = { 2015 },
issn = 0975-8887,
pages = { 5-7 },
numpages = 3,
url = { /proceedings/nciprc2015/number1/20505-8003/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Information Processing and Remote Computing
%A Blessy Trenia Lincy.s.s
%A Archana.g.k.
%T Hadoop MapReduce Framework: Performance Analysis in Business Intelligence
%J National Conference on Information Processing and Remote Computing
%@ 0975-8887
%V NCIPRC2015
%N 1
%P 5-7
%D 2015
%I International Journal of Computer Applications
Abstract

Organizations generate large amount of data each day which involves storage of data, processing of data and retrieval of data for other purpose and usage. But an individual organization or an enterprise finds this difficult i. e. they cannot handle this data and thus the useful data remains useless for a longer duration resulting in waste of storage area. Thus I propose an approach for processing and handling the data from various sources in an efficient manner. This approach can be used for Business Intelligence where the data can be processed and can provide ideas about the popularity, cost and feedback about the product released by the enterprise. The Hadoop technology is used for this purpose.

References
  1. "MapReduce: Simplified Data Processing on Large Clusters" Jeffrey Dean and Sanjay Ghemawat.
  2. Hadoop. http://Hadoop. apache. org/.
  3. "A Performance Analysis of MapReduce Task with Large Number of Files Dataset in Big Data Using Hadoop" Amrit pal, Kunal Jain, oinkiAgrawal, Sanjay Agrawal.
  4. "The BTWorld Use Case for Big Data Analytics: Description, MapReduce Logical Workflow, and Empirical Evaluation", Tim Hegeman, BogdanGhit,, MihaiCapot?a, Jan Hidders, Dick Epema, and AlexandruIosup Parallel and Distributed Systems Group, Delft University of Technology, the Netherlands T. M. {B. I. Ghit, M. Capota, A. J. H. Hidders, D. H. J. Epema, A. Iosup}@tudelft. nl.
  5. Big Data for Business Managers - Bridging the gapbetween Potential and Value, AnmolRajpurohit, Department of Computer Science, The LNM Institute of Information Technology, Jaipur, India, anmol@lnmiit. ac. in.
  6. "Reducing the Search Space for Big Data Miningfor Interesting Patterns from Uncertain Data" Carson Kai-Sang Leung, Richard Kyle MacKinnon, Fan Jiang, Department of Computer Science, University of Manitoba, Winnipeg, MB, Canada, kleung@cs. umanitoba. ca.
  7. "Store, Schedule and Switch – A New Data Delivery Modelin the Big Data Era" Weiqiang Sun, Fengqin Li, Wei Guo, Yaohui Jin and Weisheng, Hu, State Key Laboratory of Advanced Optical Communications Systems and NetworksShanghai Jiao Tong University, Shanghai, 200240, China, sunwq@sjtu. edu. cn.
Index Terms

Computer Science
Information Sciences

Keywords

Big-data Hadoop Mapreduce Hadoop Distributed Filesystem Business Intelligence.