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<p>Analyzing business processes is important for organizations aiming to optimize operations and identify inefficiencies. Traditional discovered process models often lack sufficient contextual depth, limiting the interpretability and actionability of the revealed activity process flows. This paper addresses the challenge of balancing interpretability with complexity in discovered process models by introducing a new context-driven method, namely Contextualized Activity hieRarchies for Process dIscovery (CARPI). CARPI consists of a detailed five-step process to identify, extract, and integrate meaningful contextual variables into core activities flows in the process to enhance model clarity and decision-making support. We implement and validate this method using a real-world case study in manufacturing and the BPI Challenge 2017 dataset, demonstrating how the integration of relevant contextual variables refines process models to make activity flows more interpretable and actionable. This contribution advances the field of process mining by offering a clear and structured method to enrich process models with important context variables, laying the foundation for more insightful and effective business process management and improvement.</p>

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Context-driven process discovery: enhancing process flow interpretability with contextualized activity hierarchies

  • 期刊论文

  • 发表日期：

    2025年8月2日

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  • Zahra Ahmadi,
  • Jochen De Weerdt,
  • Estefanía Serral Asensio

摘要

Analyzing business processes is important for organizations aiming to optimize operations and identify inefficiencies. Traditional discovered process models often lack sufficient contextual depth, limiting the interpretability and actionability of the revealed activity process flows. This paper addresses the challenge of balancing interpretability with complexity in discovered process models by introducing a new context-driven method, namely Contextualized Activity hieRarchies for Process dIscovery (CARPI). CARPI consists of a detailed five-step process to identify, extract, and integrate meaningful contextual variables into core activities flows in the process to enhance model clarity and decision-making support. We implement and validate this method using a real-world case study in manufacturing and the BPI Challenge 2017 dataset, demonstrating how the integration of relevant contextual variables refines process models to make activity flows more interpretable and actionable. This contribution advances the field of process mining by offering a clear and structured method to enrich process models with important context variables, laying the foundation for more insightful and effective business process management and improvement.