Abstract
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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Günther, C.W., Rozinat, A., van der Aalst, W.M.P. (2010). Activity Mining by Global Trace Segmentation. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds) Business Process Management Workshops. BPM 2009. Lecture Notes in Business Information Processing, vol 43. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12186-9_13
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DOI: https://doi.org/10.1007/978-3-642-12186-9_13
Publisher Name: Springer, Berlin, Heidelberg
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