Improving Workflow Anomaly Detection with a C-Tree

Abstract

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(vertical bar E vertical bar) 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.