Enhancing urban resilience via a real-time decision support system for smart cities

The emergence of in-memory database technologies may be seen as a groundbreaking development in the segment of data storage and data analytics enabling end-users using real-time applications on top of big data. In this work, we propose a framework for a real-time decision support system for response during a crisis or disruption of critical infrastructures or their components grounding on in-memory database technologies and smart city data sources. A simulation software which utilizes a multi-agent based model for describing the landscape of a smart city's infrastructure or their components incorporating a generic framework for defining disruption scenarios, generates big data which is stored in a database applying in-memory database technologies. According to current urban status data and the type of disruptions, data including made decisions and strategies which are best in the sense of urban resilience is instantly collected from the database.