TY - JOUR
T1 - Real-time probing of control-flow and data-flow in event logs
AU - Ceravolo, Paolo
AU - Damiani, Ernesto
AU - Schepis, Emilio Francesco
AU - Tavares, Gabriel Marques
N1 - Funding Information:
This article was financially supported by the program “Piano di sostegno alla ricerca 2020” funded by Università degli Studi di Milano.
Funding Information:
This article was financially supported by the program ?Piano di sostegno alla ricerca 2020? funded by Universit? degli Studi di Milano.
Publisher Copyright:
© 2021 The Authors. Published by Elsevier B.V.
PY - 2022/1
Y1 - 2022/1
N2 - Traditional Process Mining offers batch analysis of business processes but does not transpose smoothly into online environments due to specific design constraints. Techniques adapted to support online analysis require peculiar adjustments that inherently restrict their focus to a single task. In this work, we extend the Concept Drift in Event Stream Framework (CDESF) tool to handle multiple attributes simultaneously. Our extension promotes CDESF to analyze both control-flow and data-flow characteristics in online event streams. Experiments used real and synthetic data for concept drift and anomaly detections. Results show that additional perspectives should be considered as they contain valuable information about processes.
AB - Traditional Process Mining offers batch analysis of business processes but does not transpose smoothly into online environments due to specific design constraints. Techniques adapted to support online analysis require peculiar adjustments that inherently restrict their focus to a single task. In this work, we extend the Concept Drift in Event Stream Framework (CDESF) tool to handle multiple attributes simultaneously. Our extension promotes CDESF to analyze both control-flow and data-flow characteristics in online event streams. Experiments used real and synthetic data for concept drift and anomaly detections. Results show that additional perspectives should be considered as they contain valuable information about processes.
KW - Anomaly detection
KW - Clustering
KW - Concept drift detection
KW - Event stream
KW - Online process mining
UR - http://www.scopus.com/inward/record.url?scp=85123753700&partnerID=8YFLogxK
U2 - 10.1016/j.procs.2021.12.197
DO - 10.1016/j.procs.2021.12.197
M3 - Conference article
AN - SCOPUS:85123753700
SN - 1877-0509
VL - 197
SP - 751
EP - 758
JO - Procedia Computer Science
JF - Procedia Computer Science
T2 - 6th Information Systems International Conference, ISICO 2021
Y2 - 7 August 2021 through 8 August 2021
ER -