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.
|Title of host publication||Business Process Management Workshops (BPM 2009 International Workshops, Ulm, Germany, September 7, 2009. Revised papers)|
|Editors||S. Rinderle-Ma, S. Sadiq, F. Leymann|
|Place of Publication||Berlin|
|Publication status||Published - 2010|
|Name||Lecture Notes in Business Information Processing|