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Enhancing Big Data Analysis by using Map-reduce Technique
Author(s) -
Alaa Hussein Al-Hamami,
Ali Adel Flayyih
Publication year - 2018
Publication title -
bulletin of electrical engineering and informatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.251
H-Index - 12
ISSN - 2302-9285
DOI - 10.11591/eei.v7i1.895
Subject(s) - big data , computer science , data mining , data set , set (abstract data type) , cloud computing , process (computing) , data analysis , analytics , database , artificial intelligence , operating system , programming language
Normal 0 false false false MS X-NONE X-NONE Database is defined as a set of data that is organized and distributed in a manner that permits the user to access the data being stored in an easy and more convenient manner. 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 enhances the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on Electroencephalogram (EEG) Big-data as a case study. The proposed approach showed clear enhancement on 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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