Process Mining for Unstructured Data: Challenges and Research Directions
Agnes Koschmider Milda Aleknonytė-Resch Frederik Fonger Christian Imenkamp Arvid Lepsien Kaan Apaydin Maximilian Harms Dominik Janssen Dominic Langhammer Tobias Ziolkowski Yorck Zisgen
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.