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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, Process Model Enhancement, Quality Control, Recommendation System, Process Redesign.

Abstract: Business process modeling is a well-established method to define and visualize business processes. In complex processes, related process models may become large and hard to trace. To keep the readability of process models, process details are omitted. In other cases, process designers are not aware which process steps should be modelled in detail. However, the input specification of some process steps or the order of internal sub-steps could have an impact on the success of the overall process. The most straightforward solution is to identify the cause of reduced process success in order to improve the process results. This can be challenging, especially in flexible process environments with multiple process participants. In this paper we tackle this problem through recording image data of process executions and analyzing them with image mining techniques. We propose to redesign business process models considering the analysis results to reach more effective and efficient process exe cutions. (More)

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Paper citation in several formats:
Fichtner, M.; Schönig, S. and Jablonski, S. (2020). Process Management Enhancement by using Image Mining Techniques: A Position Paper. In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-423-7; ISSN 2184-4992, SciTePress, pages 249-255. DOI: 10.5220/0009573502490255

@conference{iceis20,
author={Myriel Fichtner. and Stefan Schönig. and Stefan Jablonski.},
title={Process Management Enhancement by using Image Mining Techniques: A Position Paper},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2020},
pages={249-255},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009573502490255},
isbn={978-989-758-423-7},
issn={2184-4992},
}

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - Process Management Enhancement by using Image Mining Techniques: A Position Paper
SN - 978-989-758-423-7
IS - 2184-4992
AU - Fichtner, M.
AU - Schönig, S.
AU - Jablonski, S.
PY - 2020
SP - 249
EP - 255
DO - 10.5220/0009573502490255
PB - SciTePress