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Predictive Process Monitoring (PPM) in Process Mining (PM) focuses on forecasting future aspects of ongoing business processes. Recent Deep Learning (DL) models excel at these tasks but suffer from case-length distortion, where longer cases dominate training and skew evaluation metrics. We propose the CaLenDiR (Case Length Distribution-Reflective) framework to address this, aligning DL training and evaluation with true case length distributions. CaLenDiR incorporates Uniform Case-Based Sampling (UCBS) and suffix-length-normalized loss functions for balanced training, along case-based metrics for evaluation. Our experiments show that CaLenDiR enhances model robustness and provides new insights into the interaction between log characteristics and model behavior.

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CaLenDiR: Mitigating Case-Length Distortion in Deep-Learning-Based Predictive Process Monitoring

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

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  • Brecht Wuyts,
  • Seppe Vanden Broucke,
  • Jochen De Weerdt

摘要

Predictive Process Monitoring (PPM) in Process Mining (PM) focuses on forecasting future aspects of ongoing business processes. Recent Deep Learning (DL) models excel at these tasks but suffer from case-length distortion, where longer cases dominate training and skew evaluation metrics. We propose the CaLenDiR (Case Length Distribution-Reflective) framework to address this, aligning DL training and evaluation with true case length distributions. CaLenDiR incorporates Uniform Case-Based Sampling (UCBS) and suffix-length-normalized loss functions for balanced training, along case-based metrics for evaluation. Our experiments show that CaLenDiR enhances model robustness and provides new insights into the interaction between log characteristics and model behavior.