TY - GEN
T1 - A Method to Improving Artifact Anomaly Detection in a Temporal Structured Workflow
AU - Abouzeid, Mahmoud M.
AU - Huang, Pei Shu
AU - Wang, Feng Jian
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - During the design phase of a workflow process, detecting anomalous operations on artifacts is important for preventing errors and unexpected behaviors of the process dynamically. For temporal structured workflows (TSWs), which involve specifying the min and max execution time intervals for each activity, there are few studies on analyzing artifact anomalies compared to non-Temporal workflows. Additionally, the existing approaches designed for TSWs are inaccurate in detecting these anomalies either. To improve the analysis of TSWs, this paper presents an improved methodology based on an extended SP-Tree structure, called TSP-Tree. Our approach involves two steps: first, transforming a TSW into a TSP-Tree, and then applying several algorithms to TSP-Tree to detect artifact anomalies.
AB - During the design phase of a workflow process, detecting anomalous operations on artifacts is important for preventing errors and unexpected behaviors of the process dynamically. For temporal structured workflows (TSWs), which involve specifying the min and max execution time intervals for each activity, there are few studies on analyzing artifact anomalies compared to non-Temporal workflows. Additionally, the existing approaches designed for TSWs are inaccurate in detecting these anomalies either. To improve the analysis of TSWs, this paper presents an improved methodology based on an extended SP-Tree structure, called TSP-Tree. Our approach involves two steps: first, transforming a TSW into a TSP-Tree, and then applying several algorithms to TSP-Tree to detect artifact anomalies.
KW - artifact anomalies
KW - temporal artifact anomaly detection
KW - temporal structured workflow
UR - http://www.scopus.com/inward/record.url?scp=85173568301&partnerID=8YFLogxK
U2 - 10.1109/SSE60056.2023.00039
DO - 10.1109/SSE60056.2023.00039
M3 - Conference contribution
AN - SCOPUS:85173568301
T3 - Proceedings - 2023 IEEE International Conference on Software Services Engineering, SSE 2023
SP - 242
EP - 250
BT - Proceedings - 2023 IEEE International Conference on Software Services Engineering, SSE 2023
A2 - Ardagna, Claudio
A2 - Atukorala, Nimanthi
A2 - Chang, Carl K.
A2 - Chang, Rong N.
A2 - Fan, Jing
A2 - Fox, Geoffrey
A2 - Helal, Sumi
A2 - Jin, Zhi
A2 - Lu, Qinghua
A2 - Seceleanu, Tiberiu
A2 - Yau, Stephen S.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Software Services Engineering, SSE 2023
Y2 - 2 July 2023 through 8 July 2023
ER -