Faced with an increasing level of disruption from natural disasters, terrorist attacks or internal failures, organisations need to ensure their business continuity. Ensuring this continuity depends, among other things, on the continuous assessment, monitoring, and management of their resilience based on the variations of the functionalities. Resilience-assessment methodologies are nowadays used to (1) prepare stakeholders for future crisis management situations and (2) help stakeholders assess past levels of resilience in the aftermath of the crisis. However, continuous, real-time monitoring and assessment of resilience is generally either outside the scope of such methods or limited to raw data representation, lacking effective filtering, interpretation, or integration in the evolving context of the organisation's activities. This paper enhances previous works on resilience assessment. The result is a complementary methodology for continuous, real-time resilience assessment and monitoring based on multiple data-sources and stakeholders. The novelty is (1) in the context of use of the methodology, (2) in the way the functionality analysis model is obtained and (3) in the way the resilience is continuously assessed.

This paper introduces a methodology for resilience assessment of critical infrastructures based on massive data. The methodology is developed for the needs of the RESIIST research project. We start from the observation that the security of large cities has become a major issue. To ensure the proper functioning of critical infrastructures, it is essential to make the right decisions at the right time. To do this, managers are informed in their decision-making processes by several indicators such as resilience. As insecurity becomes more and more threatening with technological, natural and terrorist risks, it is essential to have an indicator of resilience of the infrastructures guaranteeing security. We therefore propose an innovative method of assessing resilience. It is innovative in that it combines both the genericity (it applies to all types of infrastructure), it takes into account several dimensions (economic, technical, social, human, regulatory etc.), it integrates massive data (from cameras, sensors, GIS, and social networks), it allows decision-making in an immersive environment in virtual reality.

Critical infrastructures provide services that are essential to the functioning and well-being of society. Failure to provide these services is unacceptable. This is a problem when considering the unpredictable nature of the environment (leading to crisis, natural disasters, terrorist attacks) and internal failures. The concern is even greater due to the interconnected and interdependent nature of critical infrastructures, which might lead to failure propagation, causing domino and cascade effects. The resilience of a system is the ability to reduce the magnitude and/or duration of disruptive events or their consequences, allowing a satisfactory level of performance and quality of services. Improving critical infrastructures' resilience before any disruption occurs can reassure society's vital needs. The goal of the paper is to define an improved decision support method for resilience by combining resilience assessment and multi-viewpoint modeling methods.

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