Insight-driven Crisis Information – Preparing for the Unexpected using Big Data

Hendrik Stange, Sylvia Steenhoek, Sebastian Bothe, François Schnitzler


National information and situation centers are faced with rising information needs and the question of how to prepare for unexpected situations. One promising development is the access to vastly growing data produced by distributed sensors and a socially networked society. Current emergency information systems are limited in the amount of complex data they can process and interpret in real-time and provide only partially integrated prediction and alarming capabilities. In this paper we present a novel approach to build a new type of automated and scalable information systems that intelligently make use of massive streams of structured and unstructured data and incorporate human feedback for automated incident detection and learning. Big data technologies, uncertainty handling and privacy-by-design are employed to match end-user system requirements. We share first experiences analyzing data from the centennial flood in Germany 2013.

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