V. Vivekanandan, N. Karpagavalli, Sri Guru Institute of Technology, Tamilnadu, India.
DOI : 01.0401/ijaict.2014.02.04
International Journal of Advanced Information and Communication Technology
Received On : October 16, 2014
Revised On : November 21, 2014
Accepted On : December 13, 2014
Published On : January 05, 2015
Volume 02, Issue 01
Pages : 193-198
Abstract
Big data is the process of handling large datasets. In today’s scenario, data is growing exponentially faster than ever so the concept of Big data has emerged. It can perform data storage, data analysis, and data processing as well as data management techniques in parallel. The aim of this project is to use the classification technique before mapping the tasks into the resources. Usually, the MapReduce will take more time to decide the resource for performing the tasks which is to be allocated. Parallel Database technology is used to increase the performance of Big data because it allocate the tasks in parallel into the resources. In this model, for classifying the tasks, Ensemble Classifier is used. Along with Ensemble Classifier, Map Reduce model and Parallel Database Technology is associated which increases the efficiency and throughput of Big Data by reducing the scheduling time.
Keywords
Map Reduce, Hadoop, Ensemble Classifier, Parallel Database
Cite this article
V. Vivekanandan, N. Karpagavalli, “Efficient Data Analysis Scheme for Increasing Performance in Big Data” INTERNATIONAL JOURNAL OF ADVANCED INFORMATION AND COMMUNICATION TECHNOLOGY, pp.193-198, January 05, 2015.
Copyright
© 2015 V. Vivekanandan, N. Karpagavalli. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.