scholarly journals Evaluation Goals for Online Process Mining: a Concept Drift Perspective

Author(s):  
Paolo Ceravolo ◽  
Gabriel Marques Tavares ◽  
Sylvio Barbon Junior ◽  
Ernesto Damiani
Author(s):  
Rafael Gaspar de Sousa ◽  
Sarajane Marques Peres ◽  
Marcelo Fantinato ◽  
Hajo Alexander Reijers

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 161
Author(s):  
Ghada Elkhawaga ◽  
Mervat Abuelkheir ◽  
Sherif I. Barakat ◽  
Alaa M. Riad ◽  
Manfred Reichert

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.


Author(s):  
Gabriel Marques Tavares ◽  
Paolo Ceravolo ◽  
Victor G. Turrisi Da Costa ◽  
Ernesto Damiani ◽  
Sylvio Barbon Junior

Author(s):  
R. P. Jagadeesh Chandra Bose ◽  
Wil M. P. van der Aalst ◽  
Indrė Žliobaitė ◽  
Mykola Pechenizkiy
Keyword(s):  

2021 ◽  
Author(s):  
Lingkai Yang ◽  
Sally McClean ◽  
Mark Donnelly ◽  
Kevin Burke ◽  
Kashaf Khan

2020 ◽  
Author(s):  
Rafael Gaspar De Sousa ◽  
Sarajane Marques Peres

Most process mining techniques assume stationary processes and are not well equipped to deal with concept drift. Online detection, localization and characterization of concept drift in business processes can support process mining techniques and analysts to improve organizations flexibility and adaptability. In this research, we propose a method to detect, locate and characterize concept drift in an online setting using trace clustering. The hypothesis is that the method can benefit from the trace clustering capacity to simplify complex problems through grouping similar patterns. In preliminary experiments, trace clustering was performed in a windowing setting showing that concept drift can be detected by analyzing the variation of clustering over time.


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