What to submit?
A classification of the most complex test log using the most complex training log in the data set (“pdc_2020_1211111.xes”, called “pdc_2020_1211111_discover.xes” in the downloadable ZIP archive). This classification consists of the classified test log as described for the automated contest: For every trace in the classify log (called “pdc_2020_1211111_classify.xes” in the downloadable ZIP archive), the attribute “pdc:isPos” is added to indicate whether this trace is classified as positive (true) or negative (false) by your model. The order of the traces in the classified test log must be identical to the order of traces in the test log!
You may use any way you see fit to create this classified test log.
Download the ZIP archive containing the logs for this manual contest. The file “pdc_2020_121111_discover.xes” is the training log (from which you need to discover a model) and the file “pdc_2020_1211111_classify.xes” is the test log (which you need to classify using your discovered model). The resulting classified test log can be send to firstname.lastname@example.org.
When to submit?
After August 17th, 2020 (these two most complex logs will be disclosed on August 18th, 2020) but not later than August 31st, 2020.
How to submit?
Please send an email with your classified test log as attachment to email@example.com. You can submit as many times as you like, but a new submission does replace an old submission. Only your latest submission counts.
What feedback do I get?
This contest takes place after the deadline for submission to the automated contest, as it requires the most complex log of the PDC 2020 data set (pdc_2020_1211111.xes) to be disclosed. This log is disclosed so that one can submit a (manual) classification for it within two weeks time.
For this contest, the winner is the one that scores best on this most complex log of the PDC 2020 data set.
By comparing the results from the manual contest with the results for this most complex log from the automated contest, we can get an idea how big the gap-to-bridge is for new discovery algorithms.