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Discovering commute patterns via process mining

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Abstract

Ubiquitous computing has proven its relevance and efficiency in improving the user experience across a myriad of situations. It is now the ineluctable solution to keep pace with the ever-changing environments in which current systems operate. Despite the achievements of ubiquitous computing, this discipline is still overlooked in business process management. This is surprising, since many of today’s challenges, in this domain, can be addressed by methods and techniques from ubiquitous computing, for instance user context and dynamic aspects of resource locations. This paper takes a first step to integrate methods and techniques from ubiquitous computing in business process management. To do so, we propose discovering commute patterns via process mining. Through our proposition, we can deduce the users’ significant locations, routes, travel times and travel modes. This information can be a stepping-stone toward helping the business process management community embrace the latest achievements in ubiquitous computing, mainly in location-based service. To corroborate our claims, a user study was conducted. The significant places, routes, travel modes and commuting times of our test subjects were inferred with high accuracies. All in all, ubiquitous computing can enrich the processes with new capabilities that go beyond what has been established in business process management so far.

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Notes

  1. www.trylive.com.

  2. http://edition.cnn.com/2014/09/17/world/europe/scoot-pedestrian-technology.

  3. www.promtools.org.

  4. https://developers.google.com/android/reference/com/google/android/gms/location/FusedLocationProviderApi.

  5. https://developers.google.com/android/reference/com/google/android/gms/location/ActivityRecognition.

  6. https://developers.google.com/maps/documentation/directions/.

  7. https://developers.google.com/maps/documentation/distance-matrix/.

  8. https://developers.google.com/maps/documentation/geocoding.

  9. https://developers.google.com/places/supported_types.

  10. www.xes-standard.org/openxes/resources.

  11. https://fluxicon.com/disco.

  12. https://firebase.google.com/docs/cloud-messaging.

  13. www.awareframework.com.

  14. Test Subject.

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Authors and Affiliations

  1. Hasso Plattner Institute, University of Potsdam, Potsdam, Germany

    Alaaeddine Yousfi & Mathias Weske

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  1. Alaaeddine Yousfi
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  2. Mathias Weske
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Correspondence to Alaaeddine Yousfi.

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Yousfi, A., Weske, M. Discovering commute patterns via process mining. Knowl Inf Syst 60, 691–713 (2019). https://doi.org/10.1007/s10115-018-1255-1

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  • DOI: https://doi.org/10.1007/s10115-018-1255-1

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