Mining process model descriptions of daily life through event abstraction

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Process mining techniques focus on extracting insight in processes from event logs. Process mining has the potential to provide valuable insights in (un)healthy habits and to contribute to ambient assisted living solutions when applied on data from smart home environments. However, events recorded in smart home environments are on the level of sensor triggers, at which process discovery algorithms produce 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 for the extraction of features that can be used for abstraction with supervised learning methods that is based on the XES IEEE standard for event logs. This framework can automatically abstract sensor-level events to their interpretation at the human activity level, after training 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 are more precise indeed.
Original languageEnglish
Title of host publicationIntelligent Systems and Applications
Subtitle of host publicationExtended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2016
EditorsSupriya Kapoor, Rahul Bhatia, Yaxin Bi
Place of PublicationBerlin
Number of pages22
ISBN (Electronic)978-3-319-69266-1
ISBN (Print)978-3-319-69265-4
Publication statusPublished - 1 Jan 2018
Event2016 Intelligent Systems Conference, IntelliSys 2016 - London, United Kingdom
Duration: 21 Sept 201622 Sept 2016

Publication series

NameStudies in Computational Intelligence
PublisherSpringer International Publishing
ISSN (Print)1860-949X


Conference2016 Intelligent Systems Conference, IntelliSys 2016
Abbreviated titleIntelliSys 2016
Country/TerritoryUnited Kingdom
Internet address


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