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Authors: Myriel Fichtner 1 ; Stefan Schönig 2 and Stefan Jablonski 1

Affiliations: 1 University of Bayreuth, Germany ; 2 University of Regensburg, Germany

Keyword(s): Image Mining, Business Process Model Enhancement, Business Process Improvement, Relevant Process Detail, Convolutional Neural Network, LIME Explanation Models.

Abstract: 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 appr oach by experiments and demonstrate the extension of an existing process model by identified details. (More)

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Paper citation in several formats:
Fichtner, M.; Schönig, S. and Jablonski, S. (2022). How LIME Explanation Models Can Be Used to Extend Business Process Models by Relevant Process Details. In Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-569-2; ISSN 2184-4992, SciTePress, pages 527-534. DOI: 10.5220/0011067600003179

@conference{iceis22,
author={Myriel Fichtner. and Stefan Schönig. and Stefan Jablonski.},
title={How LIME Explanation Models Can Be Used to Extend Business Process Models by Relevant Process Details},
booktitle={Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2022},
pages={527-534},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011067600003179},
isbn={978-989-758-569-2},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 24th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - How LIME Explanation Models Can Be Used to Extend Business Process Models by Relevant Process Details
SN - 978-989-758-569-2
IS - 2184-4992
AU - Fichtner, M.
AU - Schönig, S.
AU - Jablonski, S.
PY - 2022
SP - 527
EP - 534
DO - 10.5220/0011067600003179
PB - SciTePress

- Science and Technology Publications, Lda.
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