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Activity Mining by Global Trace Segmentation

C.W. Gunther, A. Rozinat and W.M.P. van der Aalst
In S. Rinderle-Ma, S. Sadiq & F. Leymann (Eds.), Business Process Management Workshops (BPM 2009 International Workshops, Ulm, Germany, September 7, 2009. Revised papers). (Lecture Notes in Business Information Processing, Vol. 43, pp. 128-139). Berlin: Springer (DOI 10.1007/978-3-642-12186-9_13)


Process Mining is a technology for extracting non-trivial and useful information from execution logs. For example, there are many process mining techniques to automatically discover a process model describing the causal dependencies between activities . Unfortunately, the quality of a discovered process model strongly depends on the quality and suitability of the input data. For example, the logs of many real-life systems do not refer to the activities an analyst would have in mind, but are on a much more detailed level of abstraction. Trace segmentation attempts to group low-level events into clusters, which represent the execution of a higher-level activity in the (available or imagined) process meta-model. As a result, the simplified log can be used to discover better process models. This paper presents a new activity mining approach based on global trace segmentation. We also present an implementation of the approach, and we validate it using a real-life event log from ASML’s test process.

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