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Implementation and Performance Analysis of Academic_MapReduce Algorithm (AcdMR)

by Ratnamala Mantri, Ashwini Jewalikar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 121 - Number 19
Year of Publication: 2015
Authors: Ratnamala Mantri, Ashwini Jewalikar
10.5120/21647-4645

Ratnamala Mantri, Ashwini Jewalikar . Implementation and Performance Analysis of Academic_MapReduce Algorithm (AcdMR). International Journal of Computer Applications. 121, 19 ( July 2015), 17-20. DOI=10.5120/21647-4645

@article{ 10.5120/21647-4645,
author = { Ratnamala Mantri, Ashwini Jewalikar },
title = { Implementation and Performance Analysis of Academic_MapReduce Algorithm (AcdMR) },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 121 },
number = { 19 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 17-20 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume121/number19/21647-4645/ },
doi = { 10.5120/21647-4645 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:08:50.046734+05:30
%A Ratnamala Mantri
%A Ashwini Jewalikar
%T Implementation and Performance Analysis of Academic_MapReduce Algorithm (AcdMR)
%J International Journal of Computer Applications
%@ 0975-8887
%V 121
%N 19
%P 17-20
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In current scenario all organizations whether it is commercial, financial or educational, face the problem of maintaining and processing huge data. If we consider the case of educational institute, here thousands of student attendance records are generated per day. This will multiply per day, week and year as a result generates vast amount of data and subsequently increases the processing time apart from cost. In order to process the data efficiently, we have proposed MapReduce based algorithm (AcdMR) for processing of an academic data in our first paper. This work describes practical implementation of AcdMR and performance analysis based on distinct cluster configuration over dataset size (1GB, 5GB).

References
  1. Ratnamala Mantri, Rajesh Ingle and Prachi Patil, "SCDP: Scalable, Cost –Effective, Distributed and Parallel Computing Model for Academics," ICNCS,Vol 5, 77-80, 2011. ISBN 978-1-4244-8677-9 published by IEEE.
  2. Maryam Kontagora, Horacio Gonz ?lez–V ?lez, "Benchmarking a MapReduce Environment on a Full Virtualization Platform," 2010 IEEE International Conference on Complex, Intelligent and Software Intensive Systems,page-433-438,2010
  3. Tom White, "Hadoop: The definitive guide," O'Reilly Media / Yahoo Press, October 2010.
  4. J. Dean and S. Ghemawat, "MapReduce: Simplified data Processing on large clusters," in OSDI'04. San Francisco: USENIX, Dec. 2004, pp. 137–150.
  5. Google Code University, "Introduction to Parallel Programming and MapReduce", http://code. google. com/edu/parallel/mapreducetutorial. html, Nov 2010.
  6. Shimin Chen, Steven W. Schlosser, "Map-Reduce Meets Wider Varieties of Applications," research at Intel, pages 1- 8, 2008.
  7. Andrew Pavlo, Erik Paulson, Alexander Rasin, "A Comparison of Approaches to Large-Scale Data Analysis," ACM SIGMOD'09, June 29–July 2, 2009.
  8. The Apache Software Foundation, "Hadoop Map-Reduce Tutorial," Hadoop Project, https://hadoop. apache. org/docs/r1. 0. 4, Feb. 2013 .
Index Terms

Computer Science
Information Sciences

Keywords

Big Data MapReduce Data analysis Parallel programming Distributed computing Hadoop

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