Liesaputra, V., Yongchareon, S., & Chaisiri, S. (2015). A process mining technique using pattern recognition. In Proceeding of the CAiSE 2015 Forum at 27th International Conference on Advanced Information Systems Engineering co-located with 27th International Conference on Advanced Information Systems Engineering (CAiSE 2015) (Vol. CEUR-WS 1367, pp. 57–64). Stockholm, Sweden: CAiSE Forum.
Permanent Research Commons link: http://hdl.handle.net/10289/10975
Several works have proposed process mining techniques to discover process models fromevent logs. With the existing works, mined models can be built based on analyzing the relationship between any two events seen in event logs. Being restricted by that, they can only handle special cases of routing constructs and often produce unsound models that do not cover all of the traces in the logs. In this paper, we propose a novel technique for process mining based on using a pattern recognition technique called Maximal Pattern Mining (MPM). Our MPM technique can handle loops (of any length), duplicate tasks, non-free choice constructs, and long distance dependencies. Furthermore, by using the MPM, the discovered models are generally much easier to understand.
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