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Fichtner, Myriel ; Schönig, Stefan ; Jablonski, Stefan:
Using Image Mining Techniques from a Business Process Perspective.
In: Filipe, Joaquim ; Śmiałek, Michał ; Brodsky, Alexander ; Hammoudi, Slimane
(Hrsg.):
Enterprise Information Systems : 22nd International Conference, ICEIS 2020, Virtual Event, May 5–7, 2020 ; Revised Selected Papers. -
Cham
: Springer International Publishing
,
2021
. - (Lecture Notes in Business Information Processing
; 417
)
ISBN 978-3-030-75417-4
DOI: https://doi.org/10.1007/978-3-030-75418-1_4
Abstract
Business process modeling is an established method to improve business procedures and to provide more insights into internal workflows. Once the process is visualized in a business process model, future process executions correspond to the workflow prescribed by the process model. Process details like input specifications or the order of internal sub-steps are only considered during process execution if contained in the process model. These details may be decisive since they can have an impact on the success of the overall process. In some cases, such important process details are not modeled due to different aspects, like modeling with a high degree of abstraction to preserve the traceability. Nevertheless, it is necessary to identify missing but essential process details that reduce the success rate of a process. In this paper, we present a conceptual approach to use image mining techniques in order to analyze and extract process details from image data recorded during process executions. We propose to redesign business process models considering the analysis results to ensure successful process executions. We discuss different requirements regarding the image analysis output and present an exemplary prototype.