Activity mining by global trace segmentation

C.W. Günther, A. Rozinat, W.M.P. Aalst, van der

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

78 Citations (Scopus)


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.
Original languageEnglish
Title of host publicationBusiness Process Management Workshops (BPM 2009 International Workshops, Ulm, Germany, September 7, 2009. Revised papers)
EditorsS. Rinderle-Ma, S. Sadiq, F. Leymann
Place of PublicationBerlin
ISBN (Print)978-3-642-12185-2
Publication statusPublished - 2010

Publication series

NameLecture Notes in Business Information Processing
ISSN (Print)1865-1348


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