Big-data Management using Map Reduce on Cloud: Case study, EEG Images' Data

  • Sahar Mahdie Klim Department of Computer Engineering / College of Engineering / Misan University
  • Sahar Mahdie Klim Department of Computer Engineering / College of Engineering / Misan University
Keywords: Big-data, Cloud Computing, Electroencephalogram, MapReduce, Hadoop

Abstract

Database is characterized as an arrangement of data that is sorted out and disseminated in a way that allows the client to get to the data being put away in a simple and more helpful way. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work studies the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on EEG Big-data as a case study. The proposed approach showed clear enhancement for managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG researchers and specialist with an easy and fast method of handling the EEG big data.

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Published
2017-03-31
How to Cite
Klim, S., & Klim, S. (2017). Big-data Management using Map Reduce on Cloud: Case study, EEG Images’ Data. Al-Khwarizmi Engineering Journal, 13(1), 129- 137. https://doi.org/10.22153/kej.2017.11.004
Section
Articles