A Goal-Oriented Evaluation Methodology for Privacy-Preserving Process Mining

Process mining (PM) is a growing field that looks at how to find, analyze, and improve process models using data from information systems. It automates much of the detailed work that usually requires a lot of manual effort. But there are concerns about privacy when dealing with this kind of data. This research introduces a novel, goal-oriented model evaluation methodology leveraging the privacy-preserving process mining (PPPM) cycle for diverse domains. The methodology encompasses the following: establishing goals and questions, targeted data acquisition, data refinement, log inspection, PPPM analysis, question resolution and interpretation, performance assessment, and possible improvement recommendations. The proposed methodology was applied in a case study analyzing four real-life event logs from different domains, yielding quantitative insights into the operational efficiency of the privacy-preserving approaches. To improve how well PPPM approaches work, we identified key issues and errors that affect their results and time utility performance. Our preliminary application of the proposed methodology indicates its potential to facilitate improvements by guiding the implementation of PPPM techniques across various domains.