@inproceedings{1061,
  author = {Zhenyu Zhang and Chunhui Guo and Shangping Ren},
  title = {Mining Timing Constraints from Event Logs for Process Model},
  abstract = {Process mining is a technique for extracting process models from event logs. Event logs contain abundant information related to an event such as the timestamp of the event, the actions that triggers the event, etc. Much of existing process mining research has been focused on discoveries of process models behind event logs. How to uncover the timing constraints from event logs that are associated with the discovered process models is not well-studied. In this paper, we present an approach that extends existing process mining techniques to not only mine but also integrate timing constraints with process models discovered and constructed by existing process mining algorithms. The approach contains three major steps, i.e., first, for a given process model constructed by an existing process mining algorithm and represented as a workflow net, extract a time dependent set for each transition in the workflow net model. Second, based on the time dependent sets, develop an algorithm to extract timing constraints from event logs for each transition in the model. Third, extend the original workflow net into a time Petri net where the discovered timing constraints are associated with their corresponding transitions. A real-life road traffic fine management process scenario is used as a case study to show how timing constraints in the fine management process can be discovered from event logs with our approach.},
  year = {2020},
  journal = {2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)},
  month = {07},
  url = {https://par.nsf.gov/biblio/10311282},
  doi = {10.1109/COMPSAC48688.2020.0-139},
}