Ricerc@Sapienza

Among the available high-risk settings, aviation is usually referred to be a pioneering sector for higher safety management standards. The ways in which aviation service providers monitor safety performance is strongly influenced by international regulations, standards and agreements, although each State may also add its own local requirements. Particularly in the case of more mature organizations, the regulatory safety performance obligations are merely the tip of the iceberg in the undertaken safety performance activities.
In modern settings, the usage of Business Intelligence (BI) and Machine Learning (ML) solutions might become reference tools under the continuous chasing of strategies to foster organizations¿ safety intelligence capacities in such a high-risk system. The purpose of this project is to develop an integrated data-driven framework for self-service BI and ML on safety data for aviation systems. The proposed framework focuses on the development process of a BI architecture to extract meaningful knowledge from multiple data sources. Then, it progresses discussing how a ML solution may support to gain a deeper understanding of system¿s performance and to delineate specific safety recommendations. The analysis integrates a data-driven analysis into a functional representation of the high-risk world.
The contribution of this research is expected to define a democratized approach to BI and ML for high-risk settings, supporting a safer and more sustainable development of future systems.