Mannhardt, Felix (2016): Data-driven Process Discovery - Artificial Event Log. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/uuid:32cad43f-8bb9-46af-8333-48aae2bea037

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Categories

• Business and Management
• Management and Productivity

Keywords

Data mining IEEE Task Force on Process Mining Process mining synthetic event logs Systematic data processing

Licence

4TU General Terms of Use

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by Felix Mannhardt

A synthetic event log with 100,000 traces and 900,000 events that was generated by simulating a simple artificial process model. There are three data attributes in the event log: Priority, Nurse, and Type. Some paths in the model are recorded infrequently based on the value of these attributes. Noise is added by randomly adding one additional event to an increasing number of traces. CPN Tools (http://cpntools.org) was used to generate the event log and inject the noise.

History

• 2016-12-08 first online, published, posted

Publisher

Eindhoven University of Technology

Format

media types: application/x-gzip, application/zip, text/plain, text/xml

References

• https://doi.org/10.1007/978-3-319-59536-8_34
• http://cpntools.org/

Organizations

Eindhoven University of Technology, Department of Mathematics and Computer Science