@article{2965,
  author = {Anton Yeshchenko, Claudio Di Ciccio, Jan Mendling, Artem Polyvyanyy},
  title = {The Drift Analysis in the Process Mining with the Visual Drift Detection Tool},
  journal = {Electronic Devices},
  year = {2020},
  volume = {9},
  number = {1},
  doi = {https://doi.org/10.6025/ed/2020/9/1/24-28},
  url = {http://www.dline.info/ed/fulltext/v9n1/edv9n1_3.pdf},
  abstract = {The business process models change in the recent past to where the shift from concept drift into process mining occurs over a period of time. The studies conducted till now have not addressed the requirements and not yet addressed the challenges of drift categorization, drilling-down, and quantification. Throughout this work, we present a new software tool to analyze process drifts, called Visual Drift Detection (VDD), which fulfills these requirements. The tool is of benefit to the researchers and practitioners in the business intelligence and process analytics area, and can constitute a valuable aid to those who are involved in business process redesign endeavors.},
}