Process Mining for Unstructured Data: Challenges and Research Directions

• URL: http://arxiv.org/abs/2401.13677v1
• Date: Thu, 30 Nov 2023 12:09:14 GMT
• Title: Process Mining for Unstructured Data: Challenges and Research Directions
• Authors: Agnes Koschmider, Milda Aleknonyt\.e-Resch, Frederik Fonger, Christian Imenkamp, Arvid Lepsien, Kaan Apaydin, Maximilian Harms, Dominik Janssen, Dominic Langhammer, Tobias Ziolkowski, Yorck Zisgen
• Abstract summary: The application of process mining for unstructured data might significantly elevate novel insights into disciplines where unstructured data is a common data format. To efficiently analyze unstructured data by process mining and to convey confidence into the analysis result, requires bridging multiple challenges.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The application of process mining for unstructured data might significantly elevate novel insights into disciplines where unstructured data is a common data format. To efficiently analyze unstructured data by process mining and to convey confidence into the analysis result, requires bridging multiple challenges. The purpose of this paper is to discuss these challenges, present initial solutions and describe future research directions. We hope that this article lays the foundations for future collaboration on this topic.

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