Process Mining for Unstructured Data: Challenges and Research Directions

Agnes Koschmider, Milda Aleknonytė-Resch, Frederik Fonger, Christian Imenkamp, Arvid Lepsien +6 more
11/30/2023
cs.DBcs.AIcs.LG

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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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Cite this paper

@article{koschmider2023process,
  title  = {Process Mining for Unstructured Data: Challenges and Research Directions},
  author = {Agnes Koschmider and Milda Aleknonytė-Resch and Frederik Fonger and Christian Imenkamp and Arvid Lepsien and Kaan Apaydin and Maximilian Harms and Dominik Janssen and Dominic Langhammer and Tobias Ziolkowski and Yorck Zisgen},
  year   = {2023},
  eprint = {2401.13677},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2401.13677v1}
}

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