Mathematical Architecture of Microservices for Geographic Information System Based Health Management System

Abstract

Objective: Despite the wide adoption of distributed computing with several webs standards and cloud technologies; the building of city-wide health management system for smart city platform is a daunting task. Methods:Owing to the limitations in sparse learning of disease outbreak and its dynamic nature. As it would require the development of a scalable, distributed and evolving architecture on the web; where a sparse machine learning based algorithm will enable authorities collaborate in preventing, controlling, and responding to a specific disease outbreak and its time factor analysis. In this work, a mathematical system model is presented for sparse learning in geographic information system based architecture to support continuity of microservices in health management setting. Results and Conclusion:The model can continually cope with the transformation in architecture to match with the system goals of microservices and anticipate the evolutionary aspects of the architecture configuration.