Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data
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1 National Center for High-Performance Computing, Hsinchu, Taiwan2 Department of Industrial Education, National Taiwan Normal University, Taiwan3 Department of Technology Management for Innovation, Graduate School of Engineering
The University of Tokyo, Japan4 Institute of Computer and Communication Engineering, National Cheng Kung University, Taiwan* Corresponding Author
Abstract
The most dramatic factor shaping the future of higher education is Big Data and analytics. In the Big Data era, the explosive growth of massive data manipulations imposes a heavy burden on computation, storage, and communication in data centers. Increasing uncertainties in information system availability have become a daily serious problem. An appropriate evaluation and selection of the right information system disaster recovery (DR) site can ensure business continuity and investment optimization. Therefore, this research aims to establish an analytic framework for evaluating, selecting DR sites for academic Big Data. The proposed analytic framework is consisting of the Decision-Making Trial and Evaluation Laboratory (DEMATEL), DEMATEL-based network process (DNP) and VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) methods. An empirical study based on a real Big Data DR application of an Asian high-performance computer center’s evaluation and selection of DR sites for academic Big Data will be used to illustrate the feasibility of the proposed framework. The analytic results can serve as a foundation for information technology (IT) administrators’ strategies to reduce the performance gaps of a DR site for Big Data manipulations in general, and academic Big Data manipulations in special.
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Article Type: Research Article
EURASIA J Math Sci Tech Ed, 2017, Volume 13, Issue 8, 4553-4589
https://doi.org/10.12973/eurasia.2017.00951a
Publication date: 27 Jul 2017
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Article Downloads: 3476
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How to cite this article
APA
Yang, C.-L., Huang, C.-Y., Kao, Y.-S., & Tasi, Y.-L. (2017). Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data. Eurasia Journal of Mathematics, Science and Technology Education, 13(8), 4553-4589. https://doi.org/10.12973/eurasia.2017.00951a
Vancouver
Yang CL, Huang CY, Kao YS, Tasi YL. Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data. EURASIA J Math Sci Tech Ed. 2017;13(8):4553-89. https://doi.org/10.12973/eurasia.2017.00951a
AMA
Yang CL, Huang CY, Kao YS, Tasi YL. Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data. EURASIA J Math Sci Tech Ed. 2017;13(8), 4553-4589. https://doi.org/10.12973/eurasia.2017.00951a
Chicago
Yang, Chia-Lee, Chi-Yo Huang, Yu-Sheng Kao, and Yi-Lang Tasi. "Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data". Eurasia Journal of Mathematics, Science and Technology Education 2017 13 no. 8 (2017): 4553-4589. https://doi.org/10.12973/eurasia.2017.00951a
Harvard
Yang, C.-L., Huang, C.-Y., Kao, Y.-S., and Tasi, Y.-L. (2017). Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data. Eurasia Journal of Mathematics, Science and Technology Education, 13(8), pp. 4553-4589. https://doi.org/10.12973/eurasia.2017.00951a
MLA
Yang, Chia-Lee et al. "Disaster Recovery Site Evaluations and Selections for Information Systems of Academic Big Data". Eurasia Journal of Mathematics, Science and Technology Education, vol. 13, no. 8, 2017, pp. 4553-4589. https://doi.org/10.12973/eurasia.2017.00951a