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Analyzing Process Concept Drifts Based on Sensor Event Streams During Runtime

Part of the Lecture Notes in Computer Science book series (LNISA,volume 12168)


Business processes have to adapt to constantly changing requirements at a large scale due to, e.g., new regulations, and at a smaller scale due to, e.g., deviations in sensor event streams such as warehouse temperature in manufacturing or blood pressure in health care. Deviations in the process behavior during runtime can be detected from process event streams as so called concept drifts. Existing work has focused on concept drift detection so far, but has neglected why the drift occurred. To close this gap, this paper provides online algorithms to analyze the root cause for a concept drift using sensor event streams. These streams are typically gathered externally, i.e., separated from the process execution, and can be understood as time sequences. Supporting domain experts in assessing concept drifts through their root cause facilitates process optimization and evolution. The feasibility of the algorithms is shown based on a prototypical implementation. Moreover, the algorithms are evaluated based on a real-world data set from manufacturing.


  • Online process mining
  • Concept drift
  • Sensor event stream
  • Root cause analysis
  • Time sequence
  • Dynamic Time Warping

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This work has been partly funded by the Austrian Research Promotion Agency (FFG) via the “Austrian Competence Center for Digital Production” (CDP) under the contract number 854187. This work has been supported by the Pilot Factory Industry 4.0, Seestadtstrasse 27, Vienna, Austria.

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Correspondence to Florian Stertz .

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Stertz, F., Rinderle-Ma, S., Mangler, J. (2020). Analyzing Process Concept Drifts Based on Sensor Event Streams During Runtime. In: Fahland, D., Ghidini, C., Becker, J., Dumas, M. (eds) Business Process Management. BPM 2020. Lecture Notes in Computer Science(), vol 12168. Springer, Cham.

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