Cities produce a significant quantity of land-use, ecological, economical, energy, and transportation data. An interdisciplinary approach to gathering and evaluating such massive amounts of data may provide a solution of the wide range educational, political, and regulatory, administration, and corporate decisions, as well as aid judgement in order to create a smarter ecosystem. This article proposes a cloud-based analytic service to provide an experimental and theoretical viewpoint on smart cities centered on large data gathering and evaluation. In order to showcase the effectiveness of an analytical service for large data mining, a prototype was created and constructed. Hadoop was used to develop the prototype, and the data were consistent. The tool examines the Bristol Open metadata for connections between a number of different urban ecological factors. The outcomes of experiments utilizing Hadoop are given in this article. The data catalogued was analyzed to evaluate the indicators distributed over decades to determine good and detrimental developments in standard of living, namely crimes, safety, economics, and unemployment.
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Mia Ella Grace
Mia Ella Grace
Bachelor of Public Health, University of Wollongong, Wollongong NSW 2522, Australia.
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Cite this article
Mia Ella Grace, “Cloud Computing for Decision Making within the Clinical Sector”, Journal of Biomedical and Sustainable Healthcare Applications, vol.3, no.2, pp. 087-094, July 2023. doi: 10.53759/0088/JBSHA202303009.
