Downloads

Data set

  • DOI: 10.4121/14626020.v1
  • The 96 models
    • The original workflow nets (.pnml format) used to generate the logs.
    • ZIP archive, 542.84 KB
  • The 192 training logs
    • The logs (.xes format) to discover the models from using the submitted algorithm.
    • ZIP archive, 7.74 MB
  • The 192 test logs
    • The logs (.xes format) to classify using the models as discovered by the submitted algorithm from the training logs.
    • ZIP archive, 7.88 MB
  • The 192 ground truth logs
    • The test logs (.xes format) as classified by the corresponding models.
    • ZIP archive, 8.38 MB

Example discovery algorithms

Discover.bat TrainingLog.xes DiscoveredModel
  • The Alpha Miner
    • ProM package alphaminer-6.9.78
    • Discovers Petri nets (PNML)
    • ZIP archive, 116.9 MB
  • The Directly Follows Miner
    • Base miner.
    • ProM package baseminers-6.10.3
    • Discovers Petri nets (PNML)
    • ZIP archive, 114.7 MB
  • The Flower Miner
    • Base miner.
    • ProM package baseminers-6.10.3
    • Discovers Petri nets (PNML
    • ZIP archive, 114.7 MB
  • The Fodina Miner
    • Fodina-2019-06-17, using ProM
    • Discovers Petri nets (PNML)
    • ZIP archive, 76 MB
  • The Hybrid ILP Miner
    • ProM package hybridilpminer-6.10.15
    • Discovers Petri nets (PNML)
    • ZIP archive, 149.7 MB
  • The Inductive Miner
    • ProM package inductiveminerdeprecated-6.10.64
    • Discovers Petri nets (PNML)
    • ZIP archive, 173.6 MB
  • The Inductive Miner (OR)
    • ProM package inductiveminerdeprecated-6.10.64
    • Discovers Petri nets (PNML)
    • ZIP archive, 173.6 MB
  • The Log Skeleton Miner
    • ProM package logskeleton-6.10.93
    • Discovers log skeletons (LSK)
    • ZIP archive, 139.6 MB
  • The Log Skeleton N5 Miner
    • ProM package logskeleton-6.10.93
    • Discovers log skeletons allowing for 5% of noise (LSK)
    • ZIP archive, 139.6 MB
  • The Split Miner
    • splitminer-0.2.x
    • Discovers BPMN diagrams (BPMN)
    • ZIP archive, 67.6 MB
  • The Trace Miner
    • No discovery, as the resulting Petri nets get too big to be handled by the replayer
    • Classification is done directly on the log (XES): DiscoveredModel = TrainingLog

Submitted discovery algorithms

Discover.bat TrainingLog.xes DiscoveredModel

Classification algorithms

Classify.bat TestLog.xes DiscoveredModel ClassifiedTestLog.xes

Scorer algorithms

Score.bat GroundTruthLog.xes ClassifiedTestLog.xes

The score is appended to the file “E:/SURFdrive/Projects/PDC 2020/scores.csv”, but this can be configured in the Scripts/Score.txt file.