Data-driven Process Discovery - Artificial Event Log
Datacite citation style:
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
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite
Dataset
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4TU General Terms of Use
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
organizations
Eindhoven University of Technology, Department of Mathematics and Computer Science
DATA
files
- 52,071,028 bytes md5 data.zip
- 2,367 bytes md5 readme_erratum.txt
- download all files (zip)