Marc Solé
ABSTRACT
As the complexity of information systems evolves, there is a growing interest in defining suitable process models than can overcome the limitations of traditional formalisms like Petri nets or related. Causal nets may be one of such process models, since their declarative semantics and simple graph structure deviates from existing formalisms. Due to their novelty, few discovery algorithms exist for Causal nets. Moreover, the existing ones offer poor guarantees regarding the produced outcome. We describe an algorithm that can be applied as a second step to any discovery technique to improve the quality of the Causal net derived. We have tested the technique in combination with the existing algorithms in the literature, noticing a considerable improvement.
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