FB I - Wirtschaftswissenschaften
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Process mining is on the rise. It consists of methods, techniques, and tools to discover, monitor and improve processes by extracting knowledge from event logs of information systems. In combination with the rapidly growing availability of event data (Big Data), challenges
arise in the field of data quality. As data quality is the utmost success factor for process mining, there is an urgent need to get familiar with the topic and leverage possibilities to improve the data quality of event logs.
This thesis contributes to the improvement of event log data quality. It outlines criteria and issues for both, data quality in general and for process mining based on a literature review. The aim is to offer an overview of existing challenges and how to approach them with a preliminary framework, which consists of standard- ized BPMN processes. In addition, design science with experimental validation was applied to master the data quality challenges of data noise and
partial-incomplete traces with common tools like ProM and R. The approach was evaluated through real-life and artificial data.
The results reveal a large variety of data quality challenges for process mining and a need to raise awareness for this topic. Due to the early stage of process mining research, the number of methods, techniques, and tools to improve data quality is limited. Nevertheless, possibilities exist to improve the data quality of event logs. In particular, a repairing technique for data noise was positively evaluated.
The author recommends further specification of the framework and development of software features regarding data quality issue detection, data quality assessment, and event log reparation. It is
advisable to monitor new publications, e.g., as most of the leveraged R libraries were launched recently. Finally, further research in the field of data quality awareness shall be conducted to point out the importance of event log data quality for the overall success of process mining.