Paper Type
Complete
Paper Number
1233
Description
Process mining extracts knowledge about business processes from event logs, offering process enhancement by identifying inefficiencies, bottlenecks, redundancies, and improvement opportunities. AI advances process mining and drives digital transformation. This article's literature analysis forms the basis for a taxonomy development with which the AI-enabled process mining methods can be classified. Four meta-characteristics: Objective, Data Handling, Method, and Usage are more closely examined. This framework enables researchers and practitioners to analyze and discuss various process mining methods, aiding in selecting the most appropriate approach for their goals, needs, and available resources. While there are plenty of articles on process mining and artificial intelligence, a comprehensive overview of the various dimensions and characteristics of the available methods must be provided. This article fills that research gap, providing a valuable resource for understanding and comparing the different approaches.
Recommended Citation
Neis, Nicolas; Gwinner, Fabian; and Haueisen, Carolin, "A Taxonomy of Artificial Intelligence for Process Mining enhancement" (2024). PACIS 2024 Proceedings. 10.
https://aisel.aisnet.org/pacis2024/track09_digittrans/track09_digittrans/10
A Taxonomy of Artificial Intelligence for Process Mining enhancement
Process mining extracts knowledge about business processes from event logs, offering process enhancement by identifying inefficiencies, bottlenecks, redundancies, and improvement opportunities. AI advances process mining and drives digital transformation. This article's literature analysis forms the basis for a taxonomy development with which the AI-enabled process mining methods can be classified. Four meta-characteristics: Objective, Data Handling, Method, and Usage are more closely examined. This framework enables researchers and practitioners to analyze and discuss various process mining methods, aiding in selecting the most appropriate approach for their goals, needs, and available resources. While there are plenty of articles on process mining and artificial intelligence, a comprehensive overview of the various dimensions and characteristics of the available methods must be provided. This article fills that research gap, providing a valuable resource for understanding and comparing the different approaches.
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