Abstract
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.
Highlights
Using Hadoop in Cloud-computing as an environment for this kind of applications is, so efficient for-at least- four reasons
Big data is use to illustrate massive datasets consisting of 4-V definitions: Volume, Velocity, Variety and Value [3]
Electroencephalogram (EEG) data is a kind of biomedical signal data sets and clinical Big-Data
Summary
Using Hadoop in Cloud-computing as an environment for this kind of applications is, so efficient for-at least- four reasons. These reasons are the following: (a) the highly fault tolerance it has, (b) the automated data distributed it performs with balancing of the computation load across different nodes it performs, (c) parallel computation property it has and (d) as close as possible the computation location from data position property it has that reflects in network overhead of transferring [1]. EEG is widely used in the diagnosis and analysis of critical diseases Electrophysiological data is another domain, where Big Data implemented and contains approximately 100 multi-channel signals. In the Reduce step, the programmer or the developer can choose the data that they are interested in, so that this will minimize the amount of data that we have and focus on the data that is of their main interest
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