Unsupervised Event Abstraction using Pattern Abstraction and Local Process Models
This work addresses the issue of overgeneralizing process models in business process mining for practitioners, but it appears incremental as it builds on existing event abstraction methods.
The paper tackles the problem of low-level event granularity in process mining by proposing an unsupervised event abstraction method that first discovers local process models and uses them to lift event logs to a higher abstraction level, resulting in process models with more balanced fitness and precision scores as shown in preliminary results on real-life event logs.
Process mining analyzes business processes based on events stored in event logs. However, some recorded events may correspond to activities on a very low level of abstraction. When events are recorded on a too low level of granularity, process discovery methods tend to generate overgeneralizing process models. Grouping low-level events to higher level activities, i.e., event abstraction, can be used to discover better process models. Existing event abstraction methods are mainly based on common sub-sequences and clustering techniques. In this paper, we propose to first discover local process models and then use those models to lift the event log to a higher level of abstraction. Our conjecture is that process models discovered on the obtained high-level event log return process models of higher quality: their fitness and precision scores are more balanced. We show this with preliminary results on several real-life event logs.