Abstract
Business processes evolve over time to adapt to changing business environments. This requires continuous monitoring of business processes to gain insights into whether they conform to the intended design or deviate from it. The situation when a business process changes while being analysed is denoted as Concept Drift. Its analysis is concerned with studying how a business process changes, in terms of detecting and localising changes and studying the effects of the latter. Concept drift analysis is crucial to enable early detection and management of changes, that is, whether to promote a change to become part of an improved process, or to reject the change and make decisions to mitigate its effects. Despite its importance, there exists no comprehensive framework for analysing concept drift types, affected process perspectives, and granularity levels of a business process. This article proposes the CONcept Drift Analysis in Process Mining (CONDA-PM) framework describing phases and requirements of a concept drift analysis approach. CONDA-PM was derived from a Systematic Literature Review (SLR) of current approaches analysing concept drift. We apply the CONDA-PM framework on current approaches to concept drift analysis and evaluate their maturity. Applying CONDA-PM framework highlights areas where research is needed to complement existing efforts.
Highlights
Driven by the need of continuously improving the performance of business processes as well as the emergence of huge amounts of event data for running processes, process mining techniques and tools have been used for more than a decade [1]
We study how current approaches to concept drift analysis contribute to this topic and to what extent the various dimensions of the problem are covered by current research, the systematic literature review (SLR) discusses the advantages and shortcomings of each approach and systemically compares these approaches
This article presents an attempt to categorize and characterize studies dealing with concept drift analysis as a topic associated with process mining
Summary
Driven by the need of continuously improving the performance of business processes as well as the emergence of huge amounts of event data for running processes, process mining techniques and tools have been used for more than a decade [1]. Process mining aims to find a connection between the process model defining how a business process shall be executed and the event log recording data on the actual execution of the process instances by a Process-Aware Information System (PAIS). Process mining aids in gaining insights on actual process behaviour through the analysis of event logs and process models. Process mining has become a fundamental research area during the last decade, there exist many challenges inherited from those parent research fields. One of the fundamental challenges is concept drift analysis. According to the process mining manifesto [1], concept drift refers to the situation
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