Enabling Process Mining on Sensor Data from Smart Products

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Abstract

In this paper we address the challenge of applying process mining to discover models of human behaviour from sensor data. This challenge is caused by a gap between sensor data and the event logs that are used as input for process mining techniques, so we provide a transformation approach to bridge this gap. As a result, besides the automatic discovery of process models, the transformed sensor data can also be used by various other process mining techniques, e.g. to identify differences between observed behaviour and expected behaviour. We discuss the transformation approach in the context of the design process of smart products and related services, using a case study performed at Philips where a smart baby bottle has been developed. This case study also demonstrates that the use of process mining can add value to the smart product design process.

Original languageEnglish
Title of host publicationIEEE RCIS 2016 - IEEE 10th International Conference on Research Challenges in Information Science, 1-3 may 2016, Grenoble, France
Place of PublicationBrussels
PublisherIEEE Computer Society
Pages1-12
Number of pages12
ISBN (Electronic)978-1-4799-8710-8
ISBN (Print) 978-1-4799-8711-5
DOIs
Publication statusPublished - 23 Aug 2016
Event10th IEEE International Conference on Research Challenges in Information Science, IEEE RCIS 2016 - Grenoble, France
Duration: 1 May 20163 May 2016

Conference

Conference10th IEEE International Conference on Research Challenges in Information Science, IEEE RCIS 2016
CountryFrance
CityGrenoble
Period1/05/163/05/16

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Keywords

  • activity recognition
  • process mining
  • product design
  • sensor data
  • smart products

Cite this

Van Eck, M. L., Sidorova, N., & Van Der Aalst, W. M. P. (2016). Enabling Process Mining on Sensor Data from Smart Products. In IEEE RCIS 2016 - IEEE 10th International Conference on Research Challenges in Information Science, 1-3 may 2016, Grenoble, France (pp. 1-12). [7549355] Brussels: IEEE Computer Society. https://doi.org/10.1109/RCIS.2016.7549355