at Google ScholarTitle data
Fichtner, Myriel ; Schönig, Stefan ; Jablonski, Stefan:
How LIME Explanation Models Can Be Used to Extend Business Process Models by Relevant Process Details.
In: Filipe, Joaquim ; Śmiałek, Michał ; Brodsky, Alexander ; Hammoudi, Slimane
(ed.):
Proceedings of the 24th International Conference on Enterprise Information Systems. Volume 2. -
Portugal
: SciTePress
,
2022
. - pp. 527-534
ISBN 978-989-758-569-2
DOI: https://doi.org/10.5220/0000149900003179
Abstract in another language
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.