Unsupervised event abstraction using pattern abstraction and local process models

Conference Contribution

Mannhardt, F. & Tax, N. (2017). Unsupervised event abstraction using pattern abstraction and local process models. RADAR+EMISA 2017, June 12-13, 2017, Essen, Germany (pp. 55-63). (CEUR Workshop Proceedings, No. 1859). s.l.: CEUR-WS.org. In Scopus Cited 0 times. Read more: Medialink/Full text

Abstract

 

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 abstraction, 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.