Abstract
Process Mining is a technique for extracting process models from execution logs. This is particularly useful in situations where people have an idealized view of reality. Real-life processes turn out to be less structured than people tend to believe. Unfortunately, traditional process mining approaches have problems dealing with unstructured processes. The discovered models are often “spaghetti-like”, showing all details without distinguishing what is important and what is not. This paper proposes a new process mining approach to overcome this problem. The approach is configurable and allows for different faithfully simplified views of a particular process. To do this, the concept of a roadmap is used as a metaphor. Just like different roadmaps provide suitable abstractions of reality, process models should provide meaningful abstractions of operational processes encountered in domains ranging from healthcare and logistics to web services and public administration.
Keywords
- Process Mining
- Mining Algorithm
- Event Class
- Precedence Relation
- Graph Cluster
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Günther, C.W., van der Aalst, W.M.P. (2007). Fuzzy Mining – Adaptive Process Simplification Based on Multi-perspective Metrics. In: Alonso, G., Dadam, P., Rosemann, M. (eds) Business Process Management. BPM 2007. Lecture Notes in Computer Science, vol 4714. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75183-0_24
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DOI: https://doi.org/10.1007/978-3-540-75183-0_24
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