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<p>Business processes (BPs) are more and more enhanced with IoT devices, i.e., sensors monitoring relevant parameters of the physical environment and actuators automating certain tasks. Integrating the data generated by these IoT devices with typical process event data opens the door for the analysis of IoT-enhanced BPs at an unprecedented level of detail. In particular, IoT data enable the contextualisation of BPs to improve the prediction of next activities or process outcomes. However, existing predictive process monitoring techniques are not able to take IoT data as input, due to three challenges: i) the granularity gap between IoT and process data, ii) the uncertain scope of relevance of IoT data, and iii) the dynamicity of IoT data. In this paper, we examine these challenges and put forward three novel approaches for IoT-enhanced predictive process monitoring. These three approaches are evaluated in a real-life manufacturing case study analysing an IoT-enhanced production process where IoT and process event data are combined to predict when an activity of interest, prompted by specific IoT data patterns, will take place. Our evaluation shows that approaches integrating IoT data outperform traditional control-flow based techniques.</p>

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Approaches for IoT-enhanced predictive process monitoring

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月1日

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  • Yannis Bertrand,
  • Alexander Stevens,
  • Boje Deforce,
  • Johannes De Smedt,
  • Jochen De Weerdt,
  • Estefanía Serral

摘要

Business processes (BPs) are more and more enhanced with IoT devices, i.e., sensors monitoring relevant parameters of the physical environment and actuators automating certain tasks. Integrating the data generated by these IoT devices with typical process event data opens the door for the analysis of IoT-enhanced BPs at an unprecedented level of detail. In particular, IoT data enable the contextualisation of BPs to improve the prediction of next activities or process outcomes. However, existing predictive process monitoring techniques are not able to take IoT data as input, due to three challenges: i) the granularity gap between IoT and process data, ii) the uncertain scope of relevance of IoT data, and iii) the dynamicity of IoT data. In this paper, we examine these challenges and put forward three novel approaches for IoT-enhanced predictive process monitoring. These three approaches are evaluated in a real-life manufacturing case study analysing an IoT-enhanced production process where IoT and process event data are combined to predict when an activity of interest, prompted by specific IoT data patterns, will take place. Our evaluation shows that approaches integrating IoT data outperform traditional control-flow based techniques.