Using Grid Technology for Maximizing Collaborative Emergency Response Decision Making

Introduction

In general, natural phenomena are considered as normal, unavoidable and necessary planetary actions, which may cause disastrous results to the human environment if they occur in extreme forms (Asimakopoulou et al., 2006). In turn, the emergency management discipline has been formed to organize, analyze, plan, make decisions, and finally assign available resources to mitigate, prepare for, respond to, and recover from all effects of disasters (Nalls, 2003; Trim, 2003; Shaw et al., 2003). In managing disasters, it is apparent that a number of teams and individuals from multiple, geographically distributed organizations are required to communicate, co-operate and collaborate in order to take appropriate decisions and actions (Graves, 2004; Otten et al., 2004). ‘The need for information exchange during an emergency situation is present; however it can be very diverse and complex’ (Carle et al., 2004). Carle et al. (2004) also report that ‘there are frequent quotes regarding the lack and inconsistent views of information shared in emergency operations’. There are communities that ‘do not have the resources, personnel and expertise to develop a set of requirements to assist them in managing their activities as they pertain to emergency response’ (Bui & Lee, 1999).

Moreover, recent emergency management approaches are characterized inefficient because of their unstructured poor resource management and centralized nature with fixed hierarchical instructions. Many scholars in the field point out that for the management of emergency response operations, a number of information and communication technologies (ICT) and relevant collaborative computer-based systems have been developed to assist the requirements of many segmented organizations to bring together their intellectual resources and the sharing of accurate information in a timely manner (Graves, 2004; Howard et al., 2002). However, findings as presented by National Research Council (NRS) (2006) suggest that sustained efforts should be made with respect to data and resource archiving, sharing and dissemination. It refers to it as the hazards and disaster research informatics problem that is not unique to this research specialty, but it demands immediate attention and resolution. That is to say there is inefficiency in emergency managers’ ICT infrastructure.

To tackle these limitations the authors proposed a Grid-Aware Emergency Response Model (G-AERM). This demonstrates the applicability of Grid technology to emergency response by supporting stakeholders in monitoring, planning, controlling and managing actions within emergency situations caused by natural disasters in a far more informed way in terms of effectiveness and efficiency. The G-AERM for natural disasters stands as an attempt to support the collaborative and dynamic provision of all available resources and instrumentation towards the accomplishment of emergency response tasks. This has been achieved by making provision for collecting, storing and integrating data from multiple distributed and heterogeneous ICT sources in a seamless and dynamic way. The approach adopted in the G-AERM architecture allows stakeholders to be part of a wider Virtual Organization (VO) to identify and select choices from the far wider range of resources available. Clearly, this may increase the possibilities for decision makers to take and issue more informed decisions of a collaborative nature towards the accomplishment of issued tasks in a far more effective and efficient way.

Finally, the chapter concludes by presenting the implications of a real-world implementation of the G-AERM. They may uncover scenarios of a cross-disciplinary nature that have been previously regarded as intractable because of organizations’ interaction, size and complexity. For example, there will be a requirement for organizations to share their data with others across the technical infrastructure. It may lead to digitization of paper-based data and manual processes in order to enhance the availability of resources via the G-AERM infrastructure. It will lead to the need for user training in order to take advantage of G-AERM’s full potential. Last but not least, the implementation phase may prove a challenging experience for stakeholders and computer scientists because of G-AERM’s complexity and scale.