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An Efficient Multidimensional Big Data Fusion Approach in Machine-to-Machine Communication

An Efficient Multidimensional Big Data Fusion Approach in Machine-to-Machine Communication An Efficient Multidimensional Big Data Fusion Approach in Machine-to-Machine Communication AWAIS AHMAD, ANAND PAUL, and MAZHAR RATHORE, Kyungpook National University, Korea HANGBAE CHANG, Chung-Ang University, Korea Machine-to-Machine communication (M2M) is nowadays increasingly becoming a world-wide network of interconnected devices uniquely addressable, via standard communication protocols. The prevalence of M2M is bound to generate a massive volume of heterogeneous, multisource, dynamic, and sparse data, which leads a system towards major computational challenges, such as, analysis, aggregation, and storage. Moreover, a critical problem arises to extract the useful information in an efficient manner from the massive volume of data. Hence, to govern an adequate quality of the analysis, diverse and capacious data needs to be aggregated and fused. Therefore, it is imperative to enhance the computational efficiency for fusing and analyzing the massive volume of data. Therefore, to address these issues, this article proposes an efficient, multidimensional, big data analytical architecture based on the fusion model. The basic concept implicates the division of magnitudes (attributes), i.e., big datasets with complex magnitudes can be altered into smaller data subsets using five levels of the fusion model that can be easily processed by the Hadoop Processing Server, resulting in formalizing the problem http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Embedded Computing Systems (TECS) Association for Computing Machinery

An Efficient Multidimensional Big Data Fusion Approach in Machine-to-Machine Communication

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References (71)

Publisher
Association for Computing Machinery
Copyright
Copyright © 2016 by ACM Inc.
ISSN
1539-9087
DOI
10.1145/2834118
Publisher site
See Article on Publisher Site

Abstract

An Efficient Multidimensional Big Data Fusion Approach in Machine-to-Machine Communication AWAIS AHMAD, ANAND PAUL, and MAZHAR RATHORE, Kyungpook National University, Korea HANGBAE CHANG, Chung-Ang University, Korea Machine-to-Machine communication (M2M) is nowadays increasingly becoming a world-wide network of interconnected devices uniquely addressable, via standard communication protocols. The prevalence of M2M is bound to generate a massive volume of heterogeneous, multisource, dynamic, and sparse data, which leads a system towards major computational challenges, such as, analysis, aggregation, and storage. Moreover, a critical problem arises to extract the useful information in an efficient manner from the massive volume of data. Hence, to govern an adequate quality of the analysis, diverse and capacious data needs to be aggregated and fused. Therefore, it is imperative to enhance the computational efficiency for fusing and analyzing the massive volume of data. Therefore, to address these issues, this article proposes an efficient, multidimensional, big data analytical architecture based on the fusion model. The basic concept implicates the division of magnitudes (attributes), i.e., big datasets with complex magnitudes can be altered into smaller data subsets using five levels of the fusion model that can be easily processed by the Hadoop Processing Server, resulting in formalizing the problem

Journal

ACM Transactions on Embedded Computing Systems (TECS)Association for Computing Machinery

Published: Jun 7, 2016

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