Discovering hierarchical consolidated models from process families

Conference Contribution

Assy, N., van Dongen, B.F. & van der Aalst, W.M.P. (2017). Discovering hierarchical consolidated models from process families. Advanced Information Systems Engineering (pp. 314-329). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), No. 10253 LNCS). Dordrecht: Springer. In Scopus Cited 0 times.

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Abstract

 

Process families consist of different related variants that represent the same process. This might include, for example, processes executed similarly by different organizations or different versions of a same process with varying features. Motivated by the need to manage variability in process families, recent advances in process mining make it possible to discover, from a collection of event logs, a generic process model that explicitly describes the commonalities and differences across variants. However, existing approaches often result in flat complex models where it is hard to obtain a comparative insight into the common and different parts, especially when the family consists of a large number of process variants. This paper presents a decomposition-driven approach to discover hierarchical consolidated process models from collections of event logs. The discovered hierarchy consists of nested process fragments and allows to browse the variability at different levels of abstraction. The approach has been implemented as a plugin in ProM and was evaluated using synthetic and real-life event logs.