A Systematic Review of Process Discovery Methods in Process Mining: Trends, Approaches, and Challenges

Document Type : Review article

Authors

1 Prof, Department of Industrial Engineering, University of Qom, Qom, Iran.

2 Ph.D Student, Knowledge and Information Science, University of Qom, Qom, Iran.

Abstract

Purpose: Process discovery, as a core component of process mining, plays a crucial role in extracting process models from real-life event data and transforming event logs into structured representations of organizational processes. These models help analysts and decision-makers understand how processes operate in practice. Although many methods have been proposed for process discovery, the existing literature remains fragmented across different methodological perspectives. This fragmentation makes it difficult to obtain a comprehensive understanding of dominant approaches, emerging trends, and key challenges. Therefore, this study aims to provide a systematic and structured review of research on process discovery methods.
Methodology: This study follows the PRISMA 2020 guidelines for systematic literature reviews. A comprehensive search was conducted across major national and international scientific databases for publications between 2000 and 2023. The initial search identified 4,940 records. After removing duplicates and performing title and abstract screening, full-text assessments were conducted based on predefined inclusion and exclusion criteria. Finally, 138 studies were selected for analysis. The selected studies were examined using qualitative synthesis and conceptual classification.
Findings: The results show that process discovery methods can be categorized into four main approaches: algorithmic and rule-based methods, heuristic approaches, evolutionary approaches, and data-driven methods based on machine learning. The analysis also reveals increasing interest in hybrid approaches, noisy event logs, and online process discovery, while highlighting challenges related to scalability, interpretability, and evaluation frameworks.
Conclusion: This study provides an integrated overview of methodological developments, research trends, and challenges, and offers guidance for future research and practical applications.

Keywords

Main Subjects

References

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Journal of Data Analytics and Intelligent Decision-making
Volume 2, Issue 1 - Serial Number 4
January 2026
Pages 59-73

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