English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Process mining techniques focus on extracting insight in processes from eventlogs. Process mining has the potential to provide valuable insights in(un)healthy habits and to contribute to ambient assisted living solutions whenapplied on data from smart home environments. However, events recorded in smarthome environments are on the level of sensor triggers, at which processdiscovery algorithms produce overgeneralizing process models that allow for toomuch behavior and that are difficult to interpret for human experts. We showthat abstracting the events to a higher-level interpretation can enablediscovery of more precise and more comprehensible models. We present aframework for the extraction of features that can be used for abstraction withsupervised learning methods that is based on the XES IEEE standard for eventlogs. This framework can automatically abstract sensor-level events to theirinterpretation at the human activity level, after training it on training datafor which both the sensor and human activity events are known. We demonstrateour abstraction framework on three real-life smart home event logs and showthat the process models that can be discovered after abstraction are moreprecise indeed.

Mining Process Model Descriptions of Daily Life through Event Abstraction