Developing Multi-User Social Big Data For Emergency Detection Based On Clustering Analysis And Emergency Management In Edge Computing (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
The processing and analysis of data from cellular and social media in areas including real-time incident identification, emergency administration and personalization before, during, or after natural disasters opens up a new viewpoint and perspectives on the extent of the disaster and its effects on people concerned. The amount of data collected and the sophistication of an applied analysis cannot be addressed by conventional storage and processing systems and thus distributed methods are commonly used. In this paper, we propose an open-source Amazon distributed web services platform (AWS) those can be used for Edge computing while integrating data from spatio-textual user created applications and solutions for emergency detection and management. The computer focuses on scalability and uses an advanced Big-Data networks. It supports currently the most popular websites, and can easily be expanded to any social system. The data can be used to assist in the affected region as a location-based service provided by organizations or some other provider. The experimental assessment of our prototype shows its overall efficiency and scalability even under heavy load with different query types over different cluster dimensions.