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During the design phase of avionics systems, the activity of selecting the physical components of an IMA platform and allocating the avionics system's applications into the chosen components may lead to several options to configure the given IMA architecture. The aeronautical industry already realized that this activity can be assisted by automated methods. Focusing on the communication allocation, this paper presents an approach for allocating application's data flows into physical communication resources of an IMA system able to deal with the main requirements, such as safety and performance, and also able to be extended to other criteria, as for instance maintainability and extensibility requirements. The approach is based on the Boolean satisfiability theory and linear programming. A case study is presented to evaluate its applicability.

In this paper a method is proposed to be used as the first step in the ontology construction process. This method, specially tailored to ontology construction for knowledge management applications, is based on the use of concept maps as a mean of expression for the expert, followed by an application that assists in the capture of the expert intention with the goal of further formalizing the map. This application analyses the concept map, taking into account the map topology and key words used by the expert. From this analysis a series of questions is presented to the expert that, when answered, reduce the map ambiguity and identify some common patterns in ontological representations, such as generalizations and mereologic relations. This information can be used by the knowledge engineer during further knowledge acquisition sessions or to direct the expert to a further formalization or improvement of the map. The method was tested by a group of volunteers, all of them engineers working at the aerospace sector, and the results suggest that both the use of conceptual mapping as well as the intention capture step are acceptable from the point of view of the end user, supporting the claim that this method is viable as an option to reduce some of the difficulties in large scale ontology construction.

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