A process mining technique using pattern recognition
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Authors
Liesaputra, Veronica
Yongchareon, Dr. Sira
Chaisiri, Sivadon
Yongchareon, Dr. Sira
Chaisiri, Sivadon
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Grantor
Date
2015-06
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Type
Conference Contribution - Paper in Published Proceedings
Ngā Upoko Tukutuku (Māori subject headings)
Keyword
Maximal Pattern Mining (MPM)
process mining
discovery algorithms
process mining
discovery algorithms
ANZSRC Field of Research Code (2020)
Citation
Liesaputra, V., Yongchareon, S., & Chaisiri, S. (2015, June). A Process Mining Technique Using Pattern Recognition. In J. Grabis and K. Sandkuhl (Ed.), Proceedings of the CAiSE Forum, 27th International Conference on Advanced Information Systems Engineering (CAiSE) (pp.57-64)
Abstract
Several works have proposed process mining techniques to discover process models from event 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.
Publisher
Conference on Advanced Information Systems Engineering (CAiSE)
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Conference on Advanced Information Systems Engineering (CAiSE)
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