Extracting Constraints for Process Modeling

Abstract

In this paper, we introduce an approach for extracting constraints on process model construction. We begin by clarifying the type of knowledge produced by our method and how one may apply it. Next, we review the task of inductive process modeling, which provides the required data. We then introduce a logical formalism and a computational method for acquiring scientific knowledge from candidate process models. Results suggest that the learned constraints make sense ecologically and may provide insight into the nature of the modeled domain. We conclude the paper by discussing related and future work.