a systematic review of process discovery methods in process mining: trends, approaches, and challenges

Purpose: this study aims to provide a systematic and structured review of research on process discovery methods.methodology: the 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, initially identifying 4,940 records. after removing duplicates and performing title and abstract screening, followed by full-text assessment based on predefined inclusion and exclusion criteria, 138 studies were selected for final analysis. the selected studies were analyzed using qualitative synthesis and high-level conceptual classification.findings: the results indicate that process discovery methods can be broadly 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 several notable research trends, including the increasing adoption of hybrid approaches, growing attention to noisy and real-world event logs, and the emerging interest in online process discovery. in addition, several recurring challenges were identified in the literature, particularly those related to scalability, reduced model interpretability, and the lack of standardized evaluation frameworks.conclusion: by synthesizing the fragmented body of research on process discovery, this study provides an integrated overview of methodological developments, research trends, and existing challenges. the findings contribute to a clearer understanding of the current research landscape and offer guidance for future studies aimed at improving the applicability of process discovery methods in engineering and organizational decision-making contexts.