Research Commentary: Information Systems and Conceptual Modeling--A Research Agenda
Information Systems Research
Toward an OSGi-Based Infrastructure for Context-Aware Applications
IEEE Pervasive Computing
Quality of data model for supporting mobile decision making
Decision Support Systems
Ontology Development for Context-Sensitive Decision Support
SKG '07 Proceedings of the Third International Conference on Semantics, Knowledge and Grid
An Integrated Framework for Comprehensive Collaborative Emergency Management
Proceedings of the 2008 conference on Collaborative Decision Making: Perspectives and Challenges
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Mass gatherings are common events that typically attract large crowds of people. If such events are not properly planned, coordinated and managed with regard to health and safety issues, they can become hazardous and lead to injuries, illnesses and deaths. Conducting a safe and successful mass gathering event requires effective planning and management including the provision of timely medical care and response. To achieve these goals, there is a need for a unifying and formal model/framework of mass gatherings that can be applied across all the emergency agencies and events, and used in support of time-critical decision making for medical emergency management in this context. However, the absence of a common knowledge structure and conceptual model in Medical Emergency Management in Mass Gathering (MEMMG), acknowledged in the literature, limits our understanding of such events and impedes the effectiveness of decision support systems in these environments. In this paper, we propose domain ontology for MEMMG that represents main concepts of mass gatherings and their characteristics and relationships in a standard and formal manner. The proposed domain ontology is an instantiation and extension of DOEM (Domain Ontology for Emergency Management) that represents major generic concepts in the emergency management and can be used as information structure for the development of various emergency management decision support systems. We illustrate application of such an ontology to the classical Intelligence-Design-Choice-Implementation decision support model.