Scholarlink

While event log data quality is recognized as a crucial concern in process mining, the impact of event log errors on different types of process mining tasks has remained largely unexplored. This paper aims to fill such a gap by analyzing how various errors affect analysis results. In particular, we aim to assess whether and to what extent different types of errors that impact the quality of activity labels affect the performance of predictive process monitoring models, considering the three main tasks of next activity, outcome, and remaining time prediction, using publicly available and simulated event logs. The results of the experiments are used to extract preliminary insights into the design of data preparation pipelines for predictive process monitoring.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

On the Impact of Low-Quality Activity Labels in Predictive Process Monitoring

  • 会议论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年

查看全文
  • Marco Comuzzi,
  • Sungkyu Kim,
  • Jonghyeon Ko,
  • Musa Salamov,
  • Cinzia Cappiello,
  • Barbara Pernici

摘要

While event log data quality is recognized as a crucial concern in process mining, the impact of event log errors on different types of process mining tasks has remained largely unexplored. This paper aims to fill such a gap by analyzing how various errors affect analysis results. In particular, we aim to assess whether and to what extent different types of errors that impact the quality of activity labels affect the performance of predictive process monitoring models, considering the three main tasks of next activity, outcome, and remaining time prediction, using publicly available and simulated event logs. The results of the experiments are used to extract preliminary insights into the design of data preparation pipelines for predictive process monitoring.