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A Hybrid Intelligent Risk Identification Model for Configuration Management in Aerospace Systems
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Author(s): Jose Nava (Pinnacle Aerospace, Inc., Mexico)and Alejandro Osorio (Pinnacle Aerospace, Inc., Mexico)
Copyright: 2016
Pages: 27
Source title:
Handbook of Research on Military, Aeronautical, and Maritime Logistics and Operations
Source Author(s)/Editor(s): Alberto Ochoa-Zezzatti (Juarez City University, Mexico), Jöns Sánchez (Consejo Nacional De Ciencie Y Tecnologia (CONACYT), Mexico), Miguel Gastón Cedillo-Campos (Transportation Systems and Logistics National Laboratory, Mexican Institute of Transportation, Mexico)and Margain de Lourdes (Polytechnic University of Aguascalientes, Mexico)
DOI: 10.4018/978-1-4666-9779-9.ch017
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Abstract
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).
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