A Proposed Framework for Incorporating Big-Data Technology in National Crisis Management Center

Background

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.

Figure 1.

From data to decision

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.