Abstract
The chapter presents a framework for incorporating BDT in a traditional conceptual design of NCMC. The updated conceptual design is validated using the Analytical Hierarchy Process (AHP) and the Quality Function Deployment (QFD) technique. The results show that each requirement of national crisis is supported by a set of integrated modules of the conceptual design in a balanced way. The modules of the highest contribution in the design are the modules most related to BDT. The steps required for implementing the conceptual design are given.
TopBackground
The quality of decision, such as any other engineering product, is built from the very beginning all through the decision processing cycle (Davern et al. 2008). The generic decision cycle in crisis management context starts with data gathering that is processed further to higher levels of information, knowledge, intelligence, wisdom, and decision. Decisions are implemented through a Command, Control, Communication, Computer, Intelligence, Surveillance, and Reconnaissance (C4ISR) System to move a real-world-situation to be more suitable for the next decision or action (Kabil & Kabeil 2014). The model has been updated in response to the evolution of BDT as depicted in Figure 1.
There are several definitions of the term “Big-Data.” Perhaps the most popular definition is based upon the IBM’s differentiation that is based on 3 attributes of the 3 V words, Volume, Variety, and Velocity (IBM, 2016; Sagiroglu & Sinanc 2013). Leverage the traditional “Data” concepts and technologies to “Big-Data” ones affects all higher levels of the model.
One of the most important implications of using BDT in crisis management is crisis crowdsourcing. Developing more new technologies capable of collecting, communicating and disseminating individuals information on macro scales has led to more forms of crowdsourcing in crisis management (Kahl et al., 2012). Recent research efforts on crisis crowdsourcing address new issues around using information and communication technologies to engage and coordinate with the wider public during crisis management. The Crisis Crowdsourcing Framework defined by Liu (2014) uses the same traditional information-gathering six dimensions of why, who, what, when, where, and how to identify the interaction mechanisms that should be considered by designers of crisis crowdsourcing systems.
Key Terms in this Chapter
Radio Frequency Identification: A generic term for technologies that use radio waves to automatically identify entities, whatever inanimate, movable, or live.
Quality Function Deployment: A technique correlates the design modules to common national crisis management requirements and translates them into specific plans to produce products to meet those needs.
Near-Field Communication: A form of short-range wireless communication where the antenna used is much smaller than the wavelength of the carrier signal.
Analytical Hierarchy Process: A technique for pairwise comparisons using judgements of experts to derive priority scales.
Location-Based Service: A technology used to locate living entities or objects across telecommunication networks.
Big-Data Analytics: The systematic use of Bid-Data and related business insights developed through applied analytical disciplines to drive fact-based decision-making.
Crowdsourcing: The process of obtaining knowledge support by soliciting contributions from a large group of people, especially from an online community.
National Crisis: A situation or time at which a nation faces intense difficulty, uncertainty, danger or serious threat to people and national systems and organizations and a need for non-routine rules and procedures emerge accompanied with urgency.
Crisis: A time of danger when important decisions must be developed, communicated and implemented.
Big-Data Technology: A high-resolution representation of real life in such a way that all movements, activities, thoughts, conversations, relations, feelings could be tracked, recorded and processed almost online.