An Interactive Method to Discover a Petri Net Model of an Activity

Benoit Mathern (1, 2) , Alain Mille (1) , Thierry Bellet (2)

1 SILEX - Supporting Interaction and Learning by Experience
2 INRETS/LESCOT - Laboratoire d'Ergonomie et de Sciences Cognitives pour les Transports

This paper focuses on interactive Knowledge Discovery processes in the context of understanding an activity from behavioural data. Data mining provides patterns experts have to interpret and synthesize as new knowledge. Discovering patterns is an analysis task while building new symbolic knowledge is a synthesis task. A previous trace based approach (Abstract) offered a first answer to support analysis. This paper goes one step forward in supporting the synthesis task. We modify an algorithm of automata discovery in order to involve the user in the mining process, exploiting his expert knowledge about the observed activity. We chose the alpha-algorithm (Van Der Aalst et al.) developed for Petri nets discovery in a workflow management context. The modified algorithm is described and illustrated, showing how to use intermediate data to converge interactively to a satisfying automata. Finally, we discuss the use of this approach to contribute to a new knowledge mining process.

Mots clés: Knowledge discovery; Petri nets; Interactive mining

Domaines: Algorithme et structure de données [cs.DS]; Interface homme-machine [cs.HC]; Modélisation et simulation; Informatique

Benoit Mathern, Alain Mille, Thierry Bellet. An Interactive Method to Discover a Petri Net Model of an Activity. 2010. ⟨hal-00544388⟩