Development of new KPIs and Processfilters for Event Logs

Process Analytics and Process Mining have grown in interest in recent years. When analyzing processes, analysts often use a combination of process mining and filtering of specific process characteristics. Throughout this thesis, commonly used process filter concepts and important process KPIs should be identified from both literature and existing process mining software. In this thesis, novel KPI and Filter approaches with a focus on other process perspectives such as the resource-perspective, the time-perspective and the data-perspective should be developed. The identified and newly designed KPIs and Filters must be implemented, preferably as a Python package, to enable a fast and intuitive application on event logs in the form of Pandas DataFrames/PM4PY or XES-Files.