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Process mining has established itself as a method for the data-driven analysis and improvement of business processes. Process mining makes it possible to reconstruct, analyze and improve real process flows from digital recordings in systems (event logs). A distinction is made between three main aspects: Process Discovery to automatically create process models, Conformance Checking to compare actual and target processes, and Process Enhancement to improve existing processes.

At the same time, large language models (LLMs) have opened up new possibilities in the processing and generation of natural language. The combination of both technologies promises innovative approaches for business process management.

The aim of this work is to investigate LLMs for the extension and improvement of process mining. The focus will be on the development and evaluation of new methods that combine the structured process analysis of process mining with the natural language processing capabilities of LLMs.

Possible research priorities:

• Generation of process descriptions from event logs by LLMs
• Automated and natural language interpretation of process mining results
• Integration of unstructured data in process mining through LLM-based pre-processing
• Conformance checking using LLM-supported rule extraction
• Generation of process improvement proposals based on process mining results

If you are interested, please apply via the following link: https: //portal.wiwi.kit.edu/forms/form/Bewerbung_Abschlussarbeit_AIFB-BIS