Disaster management platform to support real-time analytics

Abstract

Every year, thousands of disasters impact the world, generating substantial loss in terms of lives and public infrastructure. The recovery phase requires substantial money investment for governments, in order to rebuild the affected region. To minimize the effects of a disaster, applications supporting decision-making through real-time analytics must be fast and accurate. These tools may be supported by stream processing systems that enable the real-time processing of structure-less data; however, their use and configuration require expert programming knowledge and a setup phase that could lead to loss relevant data. This work proposes a platform based on stream processing systems for supporting the analysis of large streams of data emitted in case the event of disasters. This platform facilitates deploying applications tailored for specific disaster scenarios, provides elasticity in case of changes in the amount of traffic and enables applying migration to increase robustness. We have built a study case to detect needs in case of an earthquake and discuss the properties of the system.