steady state
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2022 ◽  
Vol 97 ◽  
pp. 49-55
Author(s):  
Maryam Sadeghijam ◽  
Abdollah Moossavi ◽  
Mahdi Akbari ◽  
Abbas Yousefi ◽  
Hamid Haghani

2022 ◽  
Vol 54 (9) ◽  
pp. 1-38
Author(s):  
Denise Maria Vecino Sato ◽  
Sheila Cristiana De Freitas ◽  
Jean Paul Barddal ◽  
Edson Emilio Scalabrin

Concept drift in process mining (PM) is a challenge as classical methods assume processes are in a steady-state, i.e., events share the same process version. We conducted a systematic literature review on the intersection of these areas, and thus, we review concept drift in PM and bring forward a taxonomy of existing techniques for drift detection and online PM for evolving environments. Existing works depict that (i) PM still primarily focuses on offline analysis, and (ii) the assessment of concept drift techniques in processes is cumbersome due to the lack of common evaluation protocol, datasets, and metrics.


Author(s):  
Oriol Gomis-Bellmunt ◽  
Jie Song ◽  
Marc Cheah-Mane ◽  
Eduardo Prieto-Araujo

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