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Handling Concept Drift in Process Mining
R.P. Jagadeesh Chandra Bose 1 , 2 , Wil M.P. van der Aalst 1 , Indre Zliobaite 1 ,
and Mykola Pechenizkiy 1
1 Department of Mathematics and Computer Science, University of Technology,
Eindhoven, The Netherlands
2 Philips Healthcare, Veenpluis 5-6, Best, The Netherlands
{ j.c.b.rantham.prabhakara,w.m.p.v.d.aalst,m.pechenizkiy } @tue.nl,
zliobaite@gmail.com
Abstract. Operational processes need to change to adapt to changing
circumstances, e.g., new legislation, extreme variations in supply and de-
mand, seasonal effects, etc. While the topic of flexibility is well-researched
in the BPM domain, contemporary process mining approaches assume
the process to be in steady state. When discovering a process model
from event logs, it is assumed that the process at the beginning of the
recorded period is the same as the process at the end of the recorded pe-
riod. Obviously, this is often not the case due to the phenomenon known
as concept drift . While cases are being handled, the process itself may be
changing. This paper presents an approach to analyze such
second-order
dynamics . The approach has been implemented in ProM 1 and evaluated
by analyzing an evolving process.
Keywords: process mining, concept drift, flexibility, change patterns.
1
Introduction
In order to retain their competitive advantage in today's dynamic marketplace,
it is increasingly necessary for enterprises to streamline their processes so as to
reduce costs and to improve performance. Moreover, today's customers expect
organizations to be flexible and adapt to changing circumstances. New legisla-
tion is also forcing organizations to change their processes. It is clear that the
economic success of an organization is highly dependent on its ability to react
to changes in its operating environment. Therefore, flexibility and change have
been studied in-depth in the context of Business Process Management (BPM).
For example, process-aware information systems have been extended to be able
to flexibly adapt to changes in the process. State-of-the-art Workflow Manage-
ment (WFM) and BPM systems provide flexibility. Moreover, in processes not
driven by WFM/BPM systems there is even more flexibility as processes are
controlled by people.
Although flexibility and change have been studied in-depth in the context of
WFM and BPM systems, existing process mining techniques assume processes
1 ProM is an extensible framework that provides a comprehensive set of
tools/plugins for the discovery and analysis of process models from event logs. See
http://www.processmining.org for more information and to download ProM.
 
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