Process Mining for Unstructured Data: Challenges and Research Directions

• DOI: 10.18420/modellierung2024_012
• Publication Date: 2023-11-30
• Authors: Koschmider, Agnes; Aleknonytė-Resch,...; Fonger, Frederik; Imenkamp, Christian; Lepsien, Arvid; Apaydin, Kaan; Harms, Maximilian; Janssen, Dominik; Langhammer, Dominic; Ziolkowski, Tobias; Zisgen, Yorck
• Keywords: FOS: Computer and information sciences; Computer Science - Machine Learning; Computer Science - Artificial Intelligence; Unstructured Data; Databases (cs.DB); 02 engineering and technology; Machine Learning (cs.LG); Process Mining; Artificial Intelligence (cs.AI); Computer Science - Databases; Directions; 0202 electrical engineering, electronic engineering, information engineering; Challenges

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.