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The paper "How LIME Explanation Models can be used to extend Business Process Models by relevant Process Details" authored by Myriel Fichtner, Stefan Schönig, and Stefan Jablonski has been accepted for presentation and publication at the 24th International Conference on Enterprise Information Systems (ICEIS 2022). Below you can find the abstract of the paper.

How LIME Explanation Models can be used to extend Business Process Models by relevant Process Details
 
Business process modeling is an established method to describe workflows in enterprises. The resulting models contain tasks that are executed by process participants. If the descriptions of such tasks are too abstract or do not contain all relevant details of a business process, deviating process executions may be observed. This leads to reduced process success regarding different criteria, e.g., product quality. Existing improvement approaches are not able to identify missing details in process models that have an impact on the overall process success.
In this work, we present an approach to extract relevant process details from image data. Deep learning techniques are used to predict the success of process executions. We use LIME explanation models to extract relevant features and values that are related to positive process predictions. We show how a general conclusion of these explanations can be derived by applying further image mining techniques. We extensively evaluate our approach by experiments and demonstrate the extension of an existing process model by identified details.