CONDA-PM -- A Systematic Review and Framework for Concept Drift Analysis in Process Mining

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.Comment: 45 pages, 11 tables, 13 figure

Comparing Concept Drift Detection with Process Mining Software

Organisations have seen a rise in the volume of data corresponding to business processes being recorded. Handling process data is a meaningful way to extract relevant information from business processes with impact on the company's values. Nonetheless, business processes are subject to changes during their executions, adding complexity to their analysis. This paper aims at evaluating currently available process mining tools and software that handle concept drifts, i.e. changes over time of the statistical properties of the events occurring in a process. We provide an in-depth analysis of these tools, comparing their differences, advantages, and disadvantages by testing against a log taken from a Process Control System. Thus, by highlighting the trade-off between the software, the paper gives the stakeholders the best options regarding their case use

Relying on heterogeneous data sources to detect business process change in process models

Due to changing customer needs, regulations, protocols, and technologies, an organi zation’s business processes must regularly change and improve. The Business Process Management (BPM) discipline guides organizations to perform these changes through the BPM life-cycle, in which business processes are modeled, analyzed, redesigned, and implemented. However, sometimes these changes bypass the BPM life-cycle, happening directly at the implementations’ operational level. Consequently, the respective process models need to be updated. Business process event logs can be analyzed to identify which models need updates, but not all implementations generate event logs. One possible approach to help detect business process changes is monitoring external sys tems, participants, documents, and other items used or produced by a business process. These items are observable entities, which are components required for a business pro cess execution. Monitoring change in these entities turns them into heterogeneous data sources, named as such because their data cannot easily be merged with event logs. We show that these entities can be used to create a framework for assisting in updating out dated process models, though it demands a method for identifying these entities. It also requires the mapping between entities and process models, allowing process analysts to quickly identify outdated models when the linked entities have suffered changes. In this thesis, we assess the feasibility of creating this framework. We evaluated and compared different frameworks of organizational change, business process analysis, and redesign with an investigation of the changes required to update 25 real process models. This comparison guided us to define a taxonomy of observable entities related to business process change, which we applied to manually classify 1329 process elements originating from 88 process models. The classification frequency of the process models was 57% on average. The classification was also used to train automated classifiers using machine learning. The best automated classifiers achieved F1-scores of up to 95.4%. Our method of semi-automated manual classification of process elements with process analysts is the primary method for identifying observable entities as required by our sug gested framework. In addition, we defined a set of recommendations to help build the mapping between entities and process models and ensure it stays consistent, as well as instructions on how to use the framework to identify outdated process models