TY - JOUR
T1 - Evaluation Goals for Online Process Mining
T2 - A Concept Drift Perspective
AU - Ceravolo, Paolo
AU - Tavares, Gabriel Marques
AU - Junior, Sylvio Barbon
AU - Damiani, Ernesto
N1 - Funding Information:
This study was financed in part by Coordination for the National Council for Scientific and Technological Development (CNPq) of Brazil - Grant of Project 420562/2018-4 and Fundação Araucària (Parana, Brazil). It was also partly supported by the program "Piano di sostegno alla ricerca 2019" funded by Universita degli Studi di Milano.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Online process mining refers to a class of techniques for analyzing in real-time event streams generated by the execution of business processes. These techniques are crucial in the reactive monitoring of business processes, timely resource allocation and detection/prevention of dysfunctional behavior. Many interesting advances have been made by the research community in recent years, but there is no consensus on the exact set of properties these techniques have to achieve. This article fills the gap by identifying a set of evaluation goals for online process mining and examining their fulfillment in the state of the art. We discuss parameters and techniques regulating the balance between conflicting goals and outline research needed for their improvement. Concept drift detection is crucial in this sense but, as demonstrated by our experiments, it is only partially supported by current solutions.
AB - Online process mining refers to a class of techniques for analyzing in real-time event streams generated by the execution of business processes. These techniques are crucial in the reactive monitoring of business processes, timely resource allocation and detection/prevention of dysfunctional behavior. Many interesting advances have been made by the research community in recent years, but there is no consensus on the exact set of properties these techniques have to achieve. This article fills the gap by identifying a set of evaluation goals for online process mining and examining their fulfillment in the state of the art. We discuss parameters and techniques regulating the balance between conflicting goals and outline research needed for their improvement. Concept drift detection is crucial in this sense but, as demonstrated by our experiments, it is only partially supported by current solutions.
KW - concept drift
KW - event stream
KW - Online process mining
KW - requirements and goals
UR - http://www.scopus.com/inward/record.url?scp=85089291632&partnerID=8YFLogxK
U2 - 10.1109/TSC.2020.3004532
DO - 10.1109/TSC.2020.3004532
M3 - Article
AN - SCOPUS:85089291632
SN - 1939-1374
VL - 15
SP - 2473
EP - 2489
JO - IEEE Transactions on Services Computing
JF - IEEE Transactions on Services Computing
IS - 4
ER -