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An Expressive Hadoop MapReduce Framework

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The traditional Hadoop MapReduce framework is a simple programming model for large scale parallel and distributed data processing. However, the model is not structured for semantic-oriented large data processing since it is not expressive. This paper presents a tree-oriented approach to enable expressiveness in the traditional Hadoop MapReduce framework. The new tree based MapReduce structure provides for group based processing, level based processing, and traversal order based processing. Stand-alone or nested, these processing constructs provides the required expressivity for semantic-oriented large data processing. This is accomplished yet preserving the fundamental benefit of traditional MapReduce framework—fault-tolerant processing.
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Keywords: Expressive; Hadoop MapReduce; Parallel Trees

Document Type: Research Article

Affiliations: 1: Faculty of Computing and Informatics, Multimedia University, Cyberjaya 63100, Malaysia 2: School of Information Technology, Monash University, Victoria 3800, Australia

Publication date: November 1, 2017

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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