ISCRAM Digital Library -- Query Results

Yasir Javed, & Tony Norris. (2013). Computerized system to enhance situation awareness: Key challenges associated with the design, evaluation, and extension of a prototype. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 951–961). KIT; Baden-Baden: Karlsruher Institut fur Technologie. Abstract: Successful decision making and task execution in emergency management require appropriate levels of situation awareness (SA). This paper proposes an ontology-based model for the design of a computer-based system, Situation Aware Vigilant Emergency Reasoner (SAVER) that supports the individual, shared and team SA of managers in emergency situations. SAVER is evaluated in simulated experiments that demonstrate the improvements in SA performance. The paper provides a complete description of the SAVER design, implementation, evaluation and its proposed extension from a proof-of-concept to a production environment. Keywords: Design; Information systems; Risk management; Tsunamis; Computer-based system; Computerized systems; Emergency management; Production environments; Saver; Simulated experiments; Situation awareness; Team situation awareness; Human resource management Track: Open Track http://idl.iscram.org/show.php?record=623
Niels Netten, & Maarten Van Someren. (2006). Automated support for dynamic information distribution in incident management. In M. T. B. Van de Walle (Ed.), Proceedings of ISCRAM 2006 – 3rd International Conference on Information Systems for Crisis Response and Management (pp. 230–237). Newark, NJ: Royal Flemish Academy of Belgium. Abstract: For all emergency response personnel involved in crisis situations it is essential to timely acquire all information critical to their task performance. However, in practice errors occur in the distribution of information between these collaborating actors leading to mistakes and subsequently more damage to the situation. In this paper we present a prototype system for dynamic information distribution able to support the information flow between collaborating crisis actors. The system has been evaluated by means of simulated experiments that use data from a real incident scenario. The results indicate that automated support by means of Machine Learning method works well. Especially, when actor work context features are included, then the performance on selecting and distributing relevant information is high. Furthermore, actors acquire relevant information much faster making group communication much more efficient. Keywords: Artificial intelligence; Information systems; Adaptive information; Automated support; Dynamic information; Emergency response personnels; Group communications; Incident Management; Machine learning methods; Simulated experiments; Learning systems Track: COMMAND AND CONTROL http://idl.iscram.org/show.php?record=807

Abstract: Successful decision making and task execution in emergency management require appropriate levels of situation awareness (SA). This paper proposes an ontology-based model for the design of a computer-based system, Situation Aware Vigilant Emergency Reasoner (SAVER) that supports the individual, shared and team SA of managers in emergency situations. SAVER is evaluated in simulated experiments that demonstrate the improvements in SA performance. The paper provides a complete description of the SAVER design, implementation, evaluation and its proposed extension from a proof-of-concept to a production environment.

Abstract: For all emergency response personnel involved in crisis situations it is essential to timely acquire all information critical to their task performance. However, in practice errors occur in the distribution of information between these collaborating actors leading to mistakes and subsequently more damage to the situation. In this paper we present a prototype system for dynamic information distribution able to support the information flow between collaborating crisis actors. The system has been evaluated by means of simulated experiments that use data from a real incident scenario. The results indicate that automated support by means of Machine Learning method works well. Especially, when actor work context features are included, then the performance on selecting and distributing relevant information is high. Furthermore, actors acquire relevant information much faster making group communication much more efficient.

Keywords: Artificial intelligence; Information systems; Adaptive information; Automated support; Dynamic information; Emergency response personnels; Group communications; Incident Management; Machine learning methods; Simulated experiments; Learning systems

Track: COMMAND AND CONTROL

http://idl.iscram.org/show.php?record=807