Mining Process Model Descriptions of Daily Life Through Event Abstraction

  • N. Tax
  • N. Sidorova
  • R. Haakma
  • W. van der Aalst
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 751)


Methods from the area of Process Mining traditionally focus on extracting insight in business processes from event logs. In this paper we explore the potential of Process Mining to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions when applied on data from smart home environments. Events in smart home environments are recorded at the level of sensor triggers, which is too low to mine habit-related behavioral patterns. Process discovery algorithms produce then overgeneralizing process models that allow for too much behavior and that are difficult to interpret for human experts. We show that abstracting the events to a higher-level interpretation can enable discovery of more precise and more comprehensible models. We present a framework to automatically abstract sensor-level events to their interpretation at the human activity level. Our framework is based on the XES IEEE standard for event logs. We use supervised learning techniques to train it on training data for which both the sensor and human activity events are known. We demonstrate our abstraction framework on three real-life smart home event logs and show that the process models that can be discovered after abstraction improve on precision as well as on F-score.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • N. Tax
    • 1
  • N. Sidorova
    • 1
  • R. Haakma
    • 2
  • W. van der Aalst
    • 1
  1. 1.Technische Universiteit EindhovenEindhovenThe Netherlands
  2. 2.Philips ResearchEindhovenThe Netherlands

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