Abstract
Process Discovery and Trace Clustering are used to extract business process-related knowledge from event logs and create models of processes. A non-compensatory approach, involving concordance and discordance settings, can be used to assess trace similarity and form groups. Previous research demonstrated the effectiveness of that approach, but it is time-consuming and requires a deep understanding of the technique’s parameters and desired outcomes. To make the process more efficient, we developed a software tool to assist with parameter definition and analysis of results. The tool provides a user-friendly interface, visual aids, and the ability to adjust parameters to ensure the solution reflects user preferences, allowing users to make more informed decisions. The publicly available tool combines the power and versatility of the R language with the friendly interfaces implemented using the Shiny libraries.
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Acknowledgment
This work was supported by the MPhil program “Advanced Technologies in Informatics and Computers”, hosted by the Department of Computer Science, International Hellenic University, Kavala, Greece.
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Zapoglou, N., Delias, P. (2023). A Tool to Support the Decisions for the Trace Clustering Problem with a Non-compensatory Approach. In: Liu, S., Zaraté, P., Kamissoko, D., Linden, I., Papathanasiou, J. (eds) Decision Support Systems XIII. Decision Support Systems in An Uncertain World: The Contribution of Digital Twins . ICDSST 2023. Lecture Notes in Business Information Processing, vol 474. Springer, Cham. https://doi.org/10.1007/978-3-031-32534-2_2
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