A Big Data Approach to Support Information Distribution in Crisis Response

Abstract

Crisis response organizations operate in very dynamic environments, in which it is essential for responders to acquire all information critical to their task execution in time. In reality, the responders are often faced with information overload, incomplete information, or a combination of both. This hampers their decisionmaking process, workflow, situational awareness and, consequently, effective execution of collaborative crisis response. Therefore, getting the right information to the right person at the right time is of crucial importance.The task of processing all data during crisis response situations and determining for whom at a particular moment the information is relevant is not straightforward. When developing an information system to support this task, some important challenges have to be taken into account. These challenges relate to the structure and truthfulness of the used data, the assessment of information relevance, and the dissemination of relevant information in time. While methods and techniques from big data can be used to collect and integrate data, machine learning can be used to build a model for relevance assessments. An example implementation of such a framework of big data is the TAID software system that collects and integrates data communicated between first responders and may send information to crisis responders that were not addressed in the initial communication. As an example of the impact of TAID on crisis response, we show its effect in a simulated crisis response scenario.

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