TY - GEN
T1 - Improving Workflow Anomaly Detection with a C-Tree
AU - Wang, Feng-Jian
AU - Chang, Alex
AU - Lu, Tennyson
PY - 2017/9/7
Y1 - 2017/9/7
N2 - To guarantee the correctness of workflow execution, it is essential to analyze the structural and artifact integrity of workflows. The current best approach of artifact workflow anomaly detection is O(|E|) for structured workflows, however, each of the anomalies returned in the approach contains (artifact, operator) at each workflow node. In this paper, we present an innovative methodology which contains the following two characteristics: 1) A C-Tree (defined in Section 3) structure which separates sequential and parallel issues in workflow analysis and increases the convenience and elegancy of anomaly detection; and 2) A loop-reduction method which helps lower the size of nodes to be analyzed while not losing the abilities of detecting anomalies within workflow models. The anomaly detection is done by 1) transforming the BPMN into the C-Tree, 2) and detecting the anomaly in the C-tree. Compared with current best approach, 1) Our method can show the first operator and its location of an anomaly detected directly, although it cannot speed up the execution time, 2) The execution times of anomaly detection inside loop is decreased, and 3) Our method can detect concurrent (parallel) workflow anomaly based on C-Tree.
AB - To guarantee the correctness of workflow execution, it is essential to analyze the structural and artifact integrity of workflows. The current best approach of artifact workflow anomaly detection is O(|E|) for structured workflows, however, each of the anomalies returned in the approach contains (artifact, operator) at each workflow node. In this paper, we present an innovative methodology which contains the following two characteristics: 1) A C-Tree (defined in Section 3) structure which separates sequential and parallel issues in workflow analysis and increases the convenience and elegancy of anomaly detection; and 2) A loop-reduction method which helps lower the size of nodes to be analyzed while not losing the abilities of detecting anomalies within workflow models. The anomaly detection is done by 1) transforming the BPMN into the C-Tree, 2) and detecting the anomaly in the C-tree. Compared with current best approach, 1) Our method can show the first operator and its location of an anomaly detected directly, although it cannot speed up the execution time, 2) The execution times of anomaly detection inside loop is decreased, and 3) Our method can detect concurrent (parallel) workflow anomaly based on C-Tree.
KW - Artifact anomaly detection
KW - Structured workflow
UR - http://www.scopus.com/inward/record.url?scp=85032873774&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC.2017.277
DO - 10.1109/COMPSAC.2017.277
M3 - Conference contribution
AN - SCOPUS:85032873774
T3 - Proceedings - International Computer Software and Applications Conference
SP - 437
EP - 444
BT - Proceedings - 2017 IEEE 41st Annual Computer Software and Applications Conference Workshops, COMPSAC 2017
A2 - Demartini, Claudio
A2 - Yang, Ji-Jiang
A2 - Ahamed, Sheikh Iqbal
A2 - Conte, Thomas
A2 - Akiyama, Toyokazu
A2 - Reisman, Sorel
A2 - Takakura, Hiroki
A2 - Hasan, Kamrul
A2 - Claycomb, William
A2 - Nakamura, Motonori
A2 - Tovar, Edmundo
A2 - Zhang, Zhiyong
A2 - Liu, Ling
A2 - Lung, Chung-Horng
A2 - Cimato, Stelvio
PB - IEEE Computer Society
T2 - 41st IEEE Annual Computer Software and Applications Conference Workshops, COMPSAC 2017
Y2 - 4 July 2017 through 8 July 2017
ER -