Big-data Management using Map Reduce on Cloud: Case study, EEG Images' Data
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...
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| Formato: | article |
| Lenguaje: | EN |
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Al-Khwarizmi College of Engineering – University of Baghdad
2017
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| Acceso en línea: | https://doaj.org/article/2bc6d2b62dd1425f9a57285666653afd |
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| Sumario: | 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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