Citation

Mahrin, Mohd Naz’ri and Subbarao, Anusuyah and Chuprat, Suriayati and Abu Bakar, Nur Azaliah (2023) Cloud-based big data framework towards strengthening disaster risk reduction: systematic mapping. Journal of Science and Technology Policy Management. ISSN 2053-4620

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Abstract

Purpose Cloud computing promises dependable services offered through next-generation data centres based on virtualization technologies for computation, network and storage. Big Data Applications have been made viable by cloud computing technologies due to the tremendous expansion of data. Disaster management is one of the areas where big data applications are rapidly being deployed. This study looks at how big data is being used in conjunction with cloud computing to increase disaster risk reduction (DRR). This paper aims to explore and review the existing framework for big data used in disaster management and to provide an insightful view of how cloud-based big data platform toward DRR is applied. Design/methodology/approach A systematic mapping study is conducted to answer four research questions with papers related to Big Data Analytics, cloud computing and disaster management ranging from the year 2013 to 2019. A total of 26 papers were finalised after going through five steps of systematic mapping. Findings Findings are based on each research question. Research limitations/implications A specific study on big data platforms on the application of disaster management, in general is still limited. The lack of study in this field is opened for further research sources. Practical implications In terms of technology, research in DRR leverage on existing big data platform is still lacking. In terms of data, many disaster data are available, but scientists still struggle to learn and listen to the data and take more proactive disaster preparedness. Originality/value This study shows that a very famous platform selected by researchers is central processing unit based processing, namely, Apache Hadoop. Apache Spark which uses memory processing requires a big capacity of memory, therefore this is less preferred in the world of research.

Item Type:	Article
Uncontrolled Keywords:	Big data, Disaster Risk Reduction, Cloud Computing, Disaster Management
Subjects:	H Social Sciences > HD Industries. Land use. Labor > HD28-70 Management. Industrial Management > HD61 Risk in industry. Risk management
Divisions:	Faculty of Management (FOM)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 May 2023 03:34
Last Modified:	02 May 2023 03:34
URII:	http://shdl.mmu.edu.my/id/eprint/11374

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