TY - JOUR
T1 - A multi-components approach to monitoring process structure and customer behaviour concept drift
AU - Yang, Lingkai
AU - McClean, Sally
AU - Donnelly, Mark
AU - Burke, Kevin
AU - Khan, Kashaf
N1 - Publisher Copyright:
© 2022 Elsevier Ltd
PY - 2022/12/30
Y1 - 2022/12/30
N2 - Concept drifts within business processes are viewed as variations in the business circumstances, such as structural and behavioural changes in the control-flow, which necessitate process refinement and model updating. Existing approaches, such as relation-based precedence rules, tuned to detect drifts in the process structure are often not well suited to detecting changes in customer behaviour. This paper proposes a concept drift detector employing multi-components originating from Discrete-time Markov chains to detect, localize and reason about concept drifts in both process structure and customer behaviour of the control-flow. The approach was compared with three commonly used methods using 52 artificial event logs representing various types of drift (sudden and gradual, structural and behavioural). Experimental results demonstrated desirable performance with average F1 scores of 0.871 and 0.893 under structural and behavioural drifts, respectively. The approach was also employed in a real-life hospital billing dataset. The main contribution of this paper is a concept drift detector that is able to detect and explain root causes of control-flow changes whether such variations occurred suddenly or gradually.
AB - Concept drifts within business processes are viewed as variations in the business circumstances, such as structural and behavioural changes in the control-flow, which necessitate process refinement and model updating. Existing approaches, such as relation-based precedence rules, tuned to detect drifts in the process structure are often not well suited to detecting changes in customer behaviour. This paper proposes a concept drift detector employing multi-components originating from Discrete-time Markov chains to detect, localize and reason about concept drifts in both process structure and customer behaviour of the control-flow. The approach was compared with three commonly used methods using 52 artificial event logs representing various types of drift (sudden and gradual, structural and behavioural). Experimental results demonstrated desirable performance with average F1 scores of 0.871 and 0.893 under structural and behavioural drifts, respectively. The approach was also employed in a real-life hospital billing dataset. The main contribution of this paper is a concept drift detector that is able to detect and explain root causes of control-flow changes whether such variations occurred suddenly or gradually.
KW - Behavioural drift
KW - Business process
KW - Concept drift
KW - Discrete-time Markov chains
KW - Sliding window
UR - http://www.scopus.com/inward/record.url?scp=85136478676&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2022.118533
DO - 10.1016/j.eswa.2022.118533
M3 - Article
AN - SCOPUS:85136478676
SN - 0957-4174
VL - 210
JO - Expert Systems with Applications
JF - Expert Systems with Applications
M1 - 118533
ER -