ABSTRACT Process mining (PM) has emerged as a pivotal discipline in data science, bridging traditional process analysis with data‐driven techniques to extract actionable insights from event logs. This study conducts a comprehensive bibliometric analysis of 1764 peer‐reviewed articles from the Web of Science database to map the conceptual structure, trends, and future directions of PM research. Employing citation and co‐word analyses, the research identifies influential studies, dominant themes, and emerging topics. Findings reveal that PM literature is receiving heightened academic focus while increasingly integrating artificial intelligence (AI) and machine learning (ML) techniques. Key influential works focus on foundational algorithms (e.g., Alpha Miner, Heuristic Miner) and practical applications in sectors such as healthcare and finance. The analysis highlights the PM's expanding interdisciplinary reach, particularly in the fields of healthcare and education. This study provides a systematic evaluation of PM's evolution, offering a roadmap for researchers to advance theoretical foundations and practical implementations in this rapidly evolving field. This article is categorized under: Application Areas > Business and Industry
Tokumaci et al. (Sat,) studied this question.
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