@inproceedings{e007379998fc413b930f800bedfcb9e2,
title = "Activity mining by global trace segmentation",
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{\textquoteright}s test process.",
author = "C.W. G{\"u}nther and A. Rozinat and {Aalst, van der}, W.M.P.",
year = "2010",
doi = "10.1007/978-3-642-12186-9_13",
language = "English",
isbn = "978-3-642-12185-2",
series = "Lecture Notes in Business Information Processing",
publisher = "Springer",
pages = "128--139",
editor = "S. Rinderle-Ma and S. Sadiq and F. Leymann",
booktitle = "Business Process Management Workshops (BPM 2009 International Workshops, Ulm, Germany, September 7, 2009. Revised papers)",
address = "Germany",
}