Simplifying MapReduce data processing Online publication date: Fri, 27-Dec-2013
by Chih-Shan Liao; Jin-Ming Shih; Ruay-Shiung Chang
International Journal of Computational Science and Engineering (IJCSE), Vol. 8, No. 3, 2013
Abstract: MapReduce is a programming model developed by Google for processing and generating large datasets in distributed environments. Many real world tasks can be implemented by two functions, map and reduce. MapReduce plays a key role in cloud computing, since it decreases the complexity of the distributed programming and is easy to be developed on large clusters of common machines. Hadoop, an open-source project, is used to implement Google MapReduce architecture. It is widely used by many applications such as FaceBook, Yahoo, Twitter, and so on. However, it is difficult to decouple an application into functions of map and reduce for common users. In this paper, we focus on convenient use of MapReduce and propose using components to compose a MapReduce solution. We develop a web-based graphic user interface for ordinary users to utilise MapReduce without the real programming. Users only have to know how to specify their tasks in target-value-action tuples. Real examples are provided for demonstration.
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