Conceptual framework to enrich situation awareness of emergency dispatchers
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Data
2015-01-01
Autores
Souza, Jessica [UNESP]
Botega, Leonardo Castro
Segundo, José Eduardo Santarém
Berti, Claudia Beatriz
de Campos, Márcio Roberto
de Araújo, Regina Borges
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Resumo
Computer-Aided Dispatch (CAD) systems provide powerful resources to support emergency operators (dispatchers) in their activity. However, these dispatchers can work under heavy stress, which can lead to failure to get necessary information, resulting in unsuccessful response to calls. One challenging issue to better support operators in stressing calls is to determine how to generate, score and represent informational quality cues to help them to reason under uncertainties and improve their understanding about an ongoing situation (situational awareness-SAW). In such a context, the poor knowledge about the entities involved in a situation and what is really going on may lead to wrong decision-making. One of the gaps in the state-of-the-art research in this area is the lack of a common ground regarding information quality. This is due to domain-specific demands and the absence of a comprehensive framework of information quality that interface with different levels of knowledge during a situation assessment cycle. Hence, in order to improve dispatchers’ situational awareness, we present a new conceptual framework to support decision making in emergency call situations by enriching situations knowledge with reliable metadata and successive reassessments of information quality. The framework’s requirements elicitation was carried out with police experts as well as the definition and application of information quality scoring criteria and the representation of such scores along with a semantic knowledge representation model. The framework application on real robbery reporting calls has indicated very positive results.
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Data quality framework, Information management, Knowledge management, Situational awareness
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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 9173, p. 33-44.