A Hybrid Intelligent Risk Identification Model for Configuration Management in Aerospace Systems
José Nava, Alejandro Osorio · Advances in logistics, operations, and management science book series · 2016
This chapter proposes a multi-dimensional patterns recognition model for Configuration Management's Risk Identification in Aerospace Safety Critical Systems. This work has been designed for Aerospace software systems where companies require full compliance with the Aerospace Standard DO-178b. The solution focuses on Risk Identification for the Configuration Management Process Area. An Anomaly Detection Solution has been designed through the modeling of statistics and artificial intelligence algorithms, following CRISP-DM model standard for data mining solutions. A dimensional architecture was designed to model the problem through three dependent and interconnected dimensions. The first dimension, Behavioral Biometrics, which this model has extended to Human Behavioral Patterns. The second dimension is Infrastructure, which represents all physical specialized equipment, environments, networking, and its configurations. The third dimension is space-time, which in this model represents a time dimension against all geographical information project related (code, files, among others).