Process Duration Modelling and Concept Drift Detection for Business Process Mining

Lingkai Yang, Sally I McClean, MP Donnelly, Kevin Burke, Kashaf Khan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


Customer behaviour within business processes can change over time, making it difficult for market understanding and decision making. Detecting such variations, also referred to as concept drift, can provide insight into the evolution of the business environment, offer opportunities for model refinement and provide target-oriented services to improve customer satisfaction. Concept drift in the control-flow perspective has been extensively studied but there is a research gap in detecting process duration drift. In this paper, we use gamma mixture models (GMMs) with an expectation-maximization (EM) algorithm to fit process durations and then detect variations in their histogram, density and cumulative distributions. Specifically, three metrics: the overall difference in back-to-back histograms, the Kullback-Leibler (KL) divergence and the maximum difference in cumulative distributions are used to evaluate how different the process durations are. Furthermore, three corresponding statistical tests: the multinomial test, log-likelihood ratio (LLR) test and Kolmogorov-Smirnov (KS) test are applied to determine whether, or not, the differences are statistically significant. The approach is applied to a public real-life hospital billing process where two concept drift occurrences are discovered. The main contribution of this paper is the approach aiming for detecting process duration changes.
Original languageEnglish
Title of host publication2021 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/IOP/SCI)
Number of pages6
ISBN (Electronic)978-1-6654-1236-0
ISBN (Print)978-1-6654-2955-9
Publication statusPublished (in print/issue) - 18 Nov 2021
Event2021 IEEE Smart World Congress - Atlanta, USA, Atlanta, United States
Duration: 18 Oct 202121 Oct 2021


Conference2021 IEEE Smart World Congress
Country/TerritoryUnited States
Internet address


  • Business process
  • Process duration
  • Gamma mixture model
  • EM algorithm
  • Concept drift


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