Navigating Event Abstraction in Process Mining: A Comprehensive Analysis of Sub-problems, Data, and Process Characteristic Considerations

Jungeun Lim, Minseok Song

Abstract: Process mining technologies assume event logs with an appropriate level of granularity, but many systems generate low-level event logs, resulting in complex process models. Event abstraction addresses this issue by transforming low-level event logs into abstracted event logs, enabling the derivation of business-level process models. However, practitioners often struggle to choose suitable event abstraction methods. This is primarily due to the lack of comparative studies that analyze the differences between methods and the insufficient information regarding the data and relevant process characteristics to be considered. This study conducts a comprehensive literature review on event abstraction to overcome these challenges. The review focuses on summarizing specific sub-problems in event abstraction, identifying types of data that can be utilized, and highlighting important process characteristics that should be considered. The insights and guidance provided by this review will be valuable to practitioners seeking to select and implement effective event abstraction techniques.

External IDs:dblp:conf/bpm/Lim023