|Abstract|| Process mining is relatively young research area that meets the gap between businesses processes and various IT systems. Event logs are the primary sources for a process mining project and they are captured by different data sources including databases, ERP systems, CRM systems, audit trails, hospital information systems, bank transaction logs, etc. The extracted knowledge from this log enable us to discover the actual process and existing process model for further analysis, evaluation and continuous improvement in their quality. This way, various process mining tools have been developed in the market. Nevertheless, there is a lack of sufficent and comprehensive evaluation frameworks that assist users in selecting the right tool.
This thesis proposes a framework that enables the comparison of process mining tools in terms of their functional features. The proposed operations are linked to typical problems reported in existing process mining use cases. Using this framework, the thesis compares three process mining tools, namely ProM, Disco and Celonis The comparison shows that while these tools provide comparable functionality they differ in terms of the way the functionality is provided.|