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The Sensor to Decision Chain in Crisis Management

Authors: Moßgraber, Jürgen; Hilbring, Désirée; van der Schaaf, Hylke; Hertweck, Philipp; Kontopoulos, Efstratios; Mitzias, Panagiotis; Karakostas, Anastasios; +2 Authors

The Sensor to Decision Chain in Crisis Management

Abstract

In every disaster and crisis, incident time is the enemy, and getting accurate information about the scope, extent, and impact of the disaster is critical to creating and orchestrating an effective disaster response and recovery effort. Decision Support Systems (DSSs) for disaster and crisis situations need to solve the problem of facilitating the broad variety of sensors available today. This includes the research domain of the Internet of Things (IoT) and data coming from social media. All this data needs to be aggregated and fused, the semantics of the data needs to be understood and the results must be presented to the decision makers in an accessible way. Furthermore, the interaction and integration with existing risk and crisis management systems are necessary for a better analysis of the situation and faster reaction times. This paper provides an insight into the sensor to decision chain and proposes solutions and technologies for each step.

Keywords

Knowledge Base, Sensors, Ontology, Internet of Things, Decision Support, Early Warning, Ontology Visualization

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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