A multi-components approach to monitoring process structure and customer behaviour concept drift. (30th December 2022)

• Record Type: Journal Article
• Authors: Yang, Lingkai; McClean, Sally; Donnelly, Mark; Burke, Kevin; Khan, Kashaf
• Is Part Of: Expert systems with applications. Volume 210(2022)
• Publication Date: 2022-12-30
• Subjects: Business process -- Concept drift -- Behavioural drift -- Discrete-time Markov chains -- Sliding window; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2022.118533
• Languages: English
• ISSNs: 0957-4174

Abstract

Abstract: 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. Graphical abstract: Highlights: A method for detecting sudden and gradual drifts in process event data. The method can detect structural and behavioural process drifts. A sliding window framework associated with the proposed drift detector. The method can detect, localize and rationalize process drifts.