Risk Assessment

Abstract

The chapter analyses knowledge management paradigms for the understanding and prioritisation of risks (risk assessment), leading to decision making amongst policymakers. Studies and approaches on knowledge-based risk assessment and, in general, risk management vary depending on perceptions of risk, and these perceptions affect the knowledge scope and, ultimately, affect decisions on policy. Departing from the problems of big data in aviation, the shortcomings of the existing knowledge management paradigms and the problems of data conversion to knowledge in aviation risk management approaches are discussed. The chapter argues that there is a need for transciplinarity and interdisciplinarity for greater understanding of context deriving from the challenges in the big data era and in aviation policymaking. In order to address the challenging dynamic context in aviation, the chapter proposes a strength/knowledge-based inquiry that involves public sector and high-power organisations in order to gain holistic knowledge and to aid the decision analysis of policymakers.

The Impact of Emerging Technologies in the Aviation Manufacturing Industry: A Case Study of the Gradual-Change Factors in Shaanxi, PR China

“…Numerous studies show that the GC of emerging technologies is highly complex. For instance, artificial intelligence is used to improve control systems for autonomous aircraft motion and collaboration for the navigation-related decision-making of flight management operators during emergency landings with conflict detection and resolution [32]- [34]; Big Data is used for topological data analysis, persistent homology, disaster information, flight ticket booking, and air logistics [35]- [38]; training eye tracking is used for automation instructor assistance systems [39]- [41]; computer vision is used to improve alert systems and augmented navigation systems [42]- [44]; specialized aviation computer networks are used for global airport and in-flight connectivity [44]- [46]; smart bag tracking based on the Internet of Things is used in combination with additional sensors in aeroplane cabins to improve passenger's experiences [47]. Magistretti [48] made a deduction that we are in a stage that finding the appropriate existing technology for solving today's problem may be the best approach instead of trying to develop new ones by showing the dichotomies in process and knowledge levels.…”

An Innovative Deep Architecture for Flight Safety Risk Assessment Based on Time Series Data

“…Risk assessment techniques for flight data monitoring fall into three categories: physical, statistical, and artificial intelligence. Based on the changing nature of security and risk in the Big Data era, the physical modeling approach relies on converting data into knowledge [5]. However, there are more stringent requirements in the knowledge and data transformation paradigm, and few studies have performed risk analysis on normal data.…”