TY - GEN
T1 - An End-to-End Approach for Online Decision Mining and Decision Drift Analysis in Process-Aware Information Systems
AU - Scheibel, Beate
AU - Rinderle-Ma, Stefanie
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Decision mining enables the discovery of decision rules from event logs or streams, and constitutes an important part of in-depth analysis and optimisation of business processes. So far, decision mining has been merely applied in an ex-post way resulting in a snapshot of decision rules for the given chunk of log data. Online decision mining, by contrast, enables continuous monitoring of decision rule evolution and decision drift. Hence this paper presents an end-to-end approach for discovery as well as monitoring of decision points and the corresponding decision rules during runtime, bridging the gap between online control flow discovery and decision mining. The approach is evaluated for feasibility and applicability on four synthetic and one real-life data set.
AB - Decision mining enables the discovery of decision rules from event logs or streams, and constitutes an important part of in-depth analysis and optimisation of business processes. So far, decision mining has been merely applied in an ex-post way resulting in a snapshot of decision rules for the given chunk of log data. Online decision mining, by contrast, enables continuous monitoring of decision rule evolution and decision drift. Hence this paper presents an end-to-end approach for discovery as well as monitoring of decision points and the corresponding decision rules during runtime, bridging the gap between online control flow discovery and decision mining. The approach is evaluated for feasibility and applicability on four synthetic and one real-life data set.
UR - https://www.scopus.com/pages/publications/85164032149
U2 - 10.1007/978-3-031-34674-3_3
DO - 10.1007/978-3-031-34674-3_3
M3 - Conference contribution
AN - SCOPUS:85164032149
SN - 9783031346736
T3 - Lecture Notes in Business Information Processing
SP - 17
EP - 25
BT - Intelligent Information Systems - CAiSE Forum 2023, Proceedings
A2 - Cabanillas, Cristina
A2 - Pérez, Francisca
PB - Springer Science and Business Media Deutschland GmbH
T2 - 35th International Conference on Advanced Information Systems Engineering , CAiSE 2023
Y2 - 12 June 2023 through 16 June 2023
ER -