Abstract
This paper proposes a trace clustering approach to support process discovery of configurable, evolving process models. The clustering approach allows auditors to distinguish between different process variants within a timeframe, thereby visualizing the process evolution. The main insight to cluster entries is the “distance” between activities, i.e. the number of steps between an activity pair. By observing non-transient modifications on the distance, changes in the original process shape can be inferred and the entries clustered accordingly. The paper presents the corresponding algorithms and exemplifies its usage in a running example.
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Accorsi, R., Stocker, T. (2012). Discovering Workflow Changes with Time-Based Trace Clustering. In: Aberer, K., Damiani, E., Dillon, T. (eds) Data-Driven Process Discovery and Analysis. SIMPDA 2011. Lecture Notes in Business Information Processing, vol 116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34044-4_9
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DOI: https://doi.org/10.1007/978-3-642-34044-4_9
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