Complex system tacit knowledge extraction trough a formal method

Mickael Wajnberg (1, 2) , Mario Lezoche (1) , Blondin Alexandre Massé (2) , Petko Valtchev (3) , Hervé Panetto (1)

1 CRAN - Centre de Recherche en Automatique de Nancy
2 DMS - Département de Mathématiques et de statistique [UdeM- Montréal]
3 DIRO - Département d'Informatique et de Recherche Opérationnelle [Montreal]

A complex system, integrates multiple sub-systems. (Carney, Fisher, & Place, 2005) Each sub-system contains some form of domain knowledge but the main difference that can be seen between a complex system and a set of simple system is that the knowledge the complex system presents is larger than the sum of the knowledge that each sub-system contains (Billaud, Daclin, & Chapurlat, 2015). In both cases, each sub-system is made of elements that are exploitable together, but two different sub-systems have information that can’t be used in concert. Therefore, the tacit knowledge, the knowledge contained in the interaction between the sub-system, is usually harder to extract than the knowledge contained in the sub-systems (Yahia, Lezoche, Aubry, & Panetto, 2011). In “simple cases”, the sub-system can be presented as a formal context, a cross-table of objects and their attributes, and the links between the diverse sub-systems can be represented as relational context, a cross-table containing the objects of two sub-systems, and modeling if two objects are in relation. In this paper, we aim to present the process to extract knowledge from such a model of complex system. We will show how to apply it to a use case of real data from the neurology domain.

Domaines: Ingénierie assistée par ordinateur

Mickael Wajnberg, Mario Lezoche, Blondin Alexandre Massé, Petko Valtchev, Hervé Panetto. Complex system tacit knowledge extraction trough a formal method. INSIGHT - International Council on Systems Engineering (INCOSE), 2017, 20 (4), pp.23-26. ⟨10.1002/inst.12176⟩. ⟨hal-01673069⟩