Mining process model descriptions of daily life through event abstraction

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    Abstract

    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
    PublisherSpringer
    Pages83-104
    Number of pages22
    ISBN (Electronic)978-3-319-69266-1
    ISBN (Print)978-3-319-69265-4
    DOIs
    Publication statusPublished - 1 Jan 2018
    Event2016 Intelligent Systems Conference (IntelliSys 2016) - London, United Kingdom
    Duration: 21 Sep 201622 Sep 2016
    http://saiconference.com/Conferences/IntelliSys2016

    Publication series

    NameStudies in Computational Intelligence
    PublisherSpringer International Publishing
    Volume751
    ISSN (Print)1860-949X

    Conference

    Conference2016 Intelligent Systems Conference (IntelliSys 2016)
    Abbreviated titleIntelliSys 2016
    Country/TerritoryUnited Kingdom
    CityLondon
    Period21/09/1622/09/16
    Internet address

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