D 2025

Integration of Process Mining and Large Language Models—Literature Review

HALAŠKA, Michal; Roman ŠPERKA; Radim DOLÁK and Tomáš BARČÁK

Basic information

Original name

Integration of Process Mining and Large Language Models—Literature Review

Edition

Singapore, Smart Innovation, Systems and Technologies, p. 179-188, 10 pp. 2025

Publisher

Springer

Other information

Language

English

Type of outcome

Proceedings paper

Field of Study

50200 5.2 Economics and Business

Country of publisher

Singapore

Confidentiality degree

is not subject to a state or trade secret

Publication form

electronic version available online

References:

Marked to be transferred to RIV

Yes

Organization unit

School of Business Administration in Karvina

ISBN

978-3-032-11455-6

Keywords in English

LLMs; Process mining; Business process management; AI

Tags

International impact, Reviewed
Changed: 19/10/2026 23:21, doc. RNDr. Ing. Roman Šperka, Ph.D.

Abstract

In the original language

Large language models (LLMs) are a type of deep learning model characterized by an exceptionally high number of parameters. Their significant progress across a wide array of tasks has enabled them to become valuable and versatile tools for a diverse range of applications. Their capabilities present promising opportunities for business process management, although these potential benefits have yet to be thoroughly explored. In this research, we explore the use of LLMs to enhance various aspects of business process management (BPM). The primary focus is on process mining as a subset of BPM. To answer research questions “How are LLMs being integrated with process mining?” and “What are the challenges of integrating LLMs with process mining” we conducted a literature review focused on positioning of LLMs in process mining. In our research we present areas of integration of LLMs in process mining, together with limitations and challenges, and future directions.