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Activity Mining by Global Trace Segmentation Christian W. Gunther, Anne Rozinat, and Wil M.P. van der Aalst
 

Summary: Activity Mining by Global Trace Segmentation
Christian W. GĻunther, Anne Rozinat, and Wil M.P. van der Aalst
Information Systems Group, Eindhoven University of Technology,
P.O. Box 513, NL-5600 MB, Eindhoven, The Netherlands.
{c.w.gunther,a.rozinat,w.m.p.v.d.aalst}@tue.nl
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 de-
pendencies between activities. Unfortunately, the quality of a discovered process
model strongly depends on the quality and suitability of the input data. For ex-
ample, 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 pro-
cess 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.
Keywords: Process Mining, Event Log Schema Transformation, Trace Segmentation.
1 Introduction

  

Source: Aalst, W.M.P.van der - Wiskunde en Informatica, Technische Universiteit Eindhoven

 

Collections: Computer Technologies and Information Sciences