Using Ontology for IT-enabled Comprehensive Management of Mass Gatherings

Management in mass gatherings is a complex process that consists of three major phases: planning, operational response, and debriefing. Each of these phases involves information exchange between different agencies involved in planning and running the event. It has been widely recognized that the data quality and access to critical information impact on the efficiency of the response and overall effectiveness of the emergency management. Therefore, there is a need for solid understanding of the information needs of the agencies pertinent to designing efficient decision support systems, which are truly integrated into the context of business environment and facilitate IT-enabled work of the personnel organizing and running mass gathering events. Introduction of such ubiquitous systems depends on availability of ontology of the problem domain, which provides a common ground for information integration, gathering and exchange. We propose a conceptual architecture for ontology-based, IT-enabled decision support that extends to all phases of mass gatherings. As an example, we describe a case-based reasoning (CBR) system which takes advantage of such an ontology and ontology reasoning and can be used for prediction of medical workload and providing better understanding of mass gathering events during planning or training of new staff.

[1]  Paul Arbon,et al.  The Development of Conceptual Models for Mass-Gathering Health , 2004, Prehospital and Disaster Medicine.

[2]  Ian D. Watson,et al.  An Introduction to Case-Based Reasoning , 1995, UK Workshop on Case-Based Reasoning.

[3]  Anthony L Yoder,et al.  Hospital-based healthcare provider (nurse and physician) integration into an emergency medical services-managed mass-gathering event. , 2007, The American journal of emergency medicine.

[4]  Paul Arbon,et al.  Predicting patient presentation rates at mass gatherings using machine learning , 2011, ISCRAM.

[5]  John Yen,et al.  R-CAST-MED: Applying Intelligent Agents to Support Emergency Medical Decision-Making Teams , 2007, AIME.

[6]  Miquel Sànchez-Marrè,et al.  OntoWEDSS: augmenting environmental decision-support systems with ontologies , 2004, Environ. Model. Softw..

[7]  Roger C. Schank,et al.  Dynamic memory - a theory of reminding and learning in computers and people , 1983 .

[8]  Baisakhi Chakraborty,et al.  Knowledge Management with Case-Based Reasoning applied on Fire Emergency Handling , 2010, 2010 8th IEEE International Conference on Industrial Informatics.

[9]  Jan R Boatright Emergency medical service--mass gathering action plans. , 2004, Journal of emergency nursing: JEN : official publication of the Emergency Department Nurses Association.

[10]  Shonali Krishnaswamy,et al.  Ontology-based service-oriented architecture for emergency management in mass gatherings , 2010, 2010 IEEE International Conference on Service-Oriented Computing and Applications (SOCA).

[11]  Chang-Shing Lee,et al.  Ontology-based Intelligent Decision Support Agent for CMMI Project Monitoring and Control , 2006, NAFIPS 2006 - 2006 Annual Meeting of the North American Fuzzy Information Processing Society.

[12]  Omar El Sawy,et al.  The IS Core IX: The 3 Faces of IS Identity: Connection, Immersion, and Fusion , 2003, Commun. Assoc. Inf. Syst..

[13]  Tao Jin,et al.  A Case-Based Evolutionary Group Decision Support Method for Emergency Response , 2007, PAISI.

[14]  Ian D. Watson,et al.  Applying case-based reasoning - techniques for the enterprise systems , 1997 .

[15]  Alexander E. Berlonghi,et al.  Understanding and planning for different spectator crowds , 1995 .

[16]  Shonali Krishnaswamy,et al.  The Role of Domain Ontology for Medical Emergency Management in Mass Gatherings , 2010, DSS.

[17]  Leslie Lenert,et al.  Information technology and emergency medical care during disasters. , 2004, Academic emergency medicine : official journal of the Society for Academic Emergency Medicine.

[18]  Margo I. Seltzer,et al.  A Dynamic, Data-Driven, Decision Support System for Emergency Medical Services , 2005, International Conference on Computational Science.

[19]  B. Maguire,et al.  Mass-Gathering Medical Care: A Review of the Literature , 2002, Prehospital and Disaster Medicine.

[20]  R O S I N,et al.  Knowledge management in case-based reasoning , 2006 .

[21]  Qu Ying,et al.  Research on Method of CBR and its Application in Emergency Commanding and Decision-Making , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[22]  Dale Dzemydiene,et al.  Ontology-Based Decision Support System for Crime Investigation Processes , 2005 .

[23]  Timothy Burdick Wilderness event medicine: planning for mass gatherings in remote areas. , 2005, Travel medicine and infectious disease.

[25]  C SchankRoger,et al.  Dynamic Memory: A Theory of Reminding and Learning in Computers and People , 1983 .

[26]  Kees Nieuwenhuis,et al.  Information Systems for Crisis Response and Management , 2007, Mobile Response.

[27]  Pedro A. González-Calero,et al.  Lessons Learnt in the Development of a CBR Framework , 2010 .

[28]  C. Zeitz,et al.  Crowd Behavior at Mass Gatherings: A Literature Review , 2009, Prehospital and Disaster Medicine.

[29]  M. Maybauer,et al.  Medical Support for Children's Mass Gatherings , 2003, Prehospital and Disaster Medicine.

[30]  Liu Hong,et al.  Research on Case-Based Reasoning Combined with Rule-Based Reasoning for Emergency , 2007, 2007 IEEE International Conference on Service Operations and Logistics, and Informatics.

[31]  Yuval Shahar,et al.  Synthesis of Research: EON: A Component-Based Approach to Automation of Protocol-Directed Therapy , 1996, J. Am. Medical Informatics Assoc..

[32]  Shah Jahan Miah,et al.  Ontology Development for Context-Sensitive Decision Support , 2007, Third International Conference on Semantics, Knowledge and Grid (SKG 2007).

[33]  Basit Shafiq,et al.  Approach for Discovering and Handling Crisis in a Service-Oriented Environment , 2007, 2007 IEEE Intelligence and Security Informatics.