Using a Model-driven, Knowledge-based Approach to Cope with Complexity in Filtering of Notices to Airmen

A Notice to Airmen (NOTAM) is a safety- and time-critical announcement of temporary changes to flight conditions, for example, an airspace closure, and thus is essential to flight operations personnel. Reducing the number of irrelevant NOTAMs presented to personnel is critical to decrease information overload and thus the stress level of flight operations personnel. In this paper we present a multi-level knowledge model as well as the corresponding system architecture for a knowledge-based NOTAM filter and query system which has been developed as part of the Semantic NOTAM (Sem-NOTAM) research project. We identify types of complexity and their drivers relevant for the system and show how they are addressed in the knowledge model and architecture. Further, we describe modelling techniques, filter networks and our choice of technologies as means to cope with the identified types of complexity. The techniques and knowledge model proposed in this paper are not specific to the Sem-NOTAM project, but can be applied to other similar filter and query systems.