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Distinction of Discrete Transformations Applied to Hadoop's MapReduce

by Bhavini Kanoongo, Puja Jagani, Chetashri Bhadane
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 104 - Number 10
Year of Publication: 2014
Authors: Bhavini Kanoongo, Puja Jagani, Chetashri Bhadane
10.5120/18241-9321

Bhavini Kanoongo, Puja Jagani, Chetashri Bhadane . Distinction of Discrete Transformations Applied to Hadoop's MapReduce. International Journal of Computer Applications. 104, 10 ( October 2014), 35-38. DOI=10.5120/18241-9321

@article{ 10.5120/18241-9321,
author = { Bhavini Kanoongo, Puja Jagani, Chetashri Bhadane },
title = { Distinction of Discrete Transformations Applied to Hadoop's MapReduce },
journal = { International Journal of Computer Applications },
issue_date = { October 2014 },
volume = { 104 },
number = { 10 },
month = { October },
year = { 2014 },
issn = { 0975-8887 },
pages = { 35-38 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume104/number10/18241-9321/ },
doi = { 10.5120/18241-9321 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:35:49.636763+05:30
%A Bhavini Kanoongo
%A Puja Jagani
%A Chetashri Bhadane
%T Distinction of Discrete Transformations Applied to Hadoop's MapReduce
%J International Journal of Computer Applications
%@ 0975-8887
%V 104
%N 10
%P 35-38
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Hadoop MapReduce is an effective data processing platform for both commercial as well as academic applications. It intends the simplification of vast quantities of data as well as ease of processing in parallel on enormous clusters of hardware in a fault-tolerant and dependable approach. There are many modifications possible in the MapReduce to increase the performance along with increasing the simplicity of job tuning. Three of the adaptive run-time techniques namely, HIPI (Hadoop Image Processing Interface), HOG (Distributed Hadoop MapReduce on Grid) and using SAM (Situation Aware Mappers) are described and compared in the following paper. There is a rapid increase in the amount of images uploaded on the internet; however the applications which utilize this data are severely inadequate. Large and computational distributed processing can be done by employing HIPI. Another Hadoop transformation that we study is the HOG which provides a complimentary, adaptable and dynamic MapReduce environment on the resources of the grid, reforms Hadoop's fault tolerance for wide area data analysis. All the modifications to the Hadoop framework are transparent to the existing Hadoop MapReduce applications.

References
  1. Jeffrey Dean and Sanjay Ghemawat. 2004. MapReduce: Simplied Data Processing on Large Clusters. Google inc.
  2. R. Pordes, D. Petravick, B. Kramer, D. Olson, M. Livny, A. Roy, P. Avery, K. Blackburn, T. Wenaus, F. W¨urthweinetal. 2007. The open science grid. Journal of Physics: Conference Series, vol. 78.
  3. J. Cieslewicz and K. A. Ross. 2007. Adaptive aggregation on chip multiprocessors. VLDB, 2007, pp. 339–350.
  4. I. Raicu, I. Foster, and Y. Zhao. 2008. Many-task computing for grids and supercomputers. Many-Task Computing on Grids and Supercomputers, 2008. MTAGS 2008. Workshop on. IEEE, 2008, pp. 1–11.
  5. Andrey Balmin, Kevin S. Beyer, Vuk Ercegovac. 2012. Adaptive MapReduce Using Situation Aware Mappers. IBM Research Report.
  6. Chen He, Derek Weitzel, David Swanson and Ying Lu. HOG: Distributed Hadoop MapReduce on the Grid. University of Nebraska – Lincoln.
  7. ChrisSweeney, LiuLiu and SeanArietta. HIPI: A Hadoop Image Processing Interface for Image-based MapReduce Tasks. University of Virginia.
  8. Casey McTaggart. Hadoop/MapReduce. Object-oriented framework presentation.
Index Terms

Computer Science
Information Sciences

Keywords

Hadoop MapReduce Grid Parallel data systems Distributed data systems.

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