Improving the Detection of Artifact Anomalies in a Workflow Analysis

Abstract

Workflow management systems (WfMS) are considered as accomplish platforms which can provide structured organization in business process and service architecture design. The systems contain workflow models in foundation, which provide flow control in one or more task sequences in parallel. Manipulation and access of artifacts that occur in or between the task sequences can generate unexpected state of artifacts by inappropriate workflow design. The artifact anomalies in a workflow model are classified into two categories, which are types of continuous and concurrent anomalies. A continuous anomaly occurs while an artifact is written redundantly or accessed before production. On the other hand, a concurrent anomaly can occur while an artifact is conflict written in parallel in a workflow model. There are several methods presented for anomaly analysis, however, these methods cannot detect all anomalies due to their definitions and they are either inefficient or lack of proof for the correctness. In this article, we present improved detection methods with an improved C-tree structure, called SP-tree. Based on an updated anomaly definition, our anomaly detection includes two stages: 1) the transformation algorithm generates an equivalent SP-tree from a given structured workflow model; and 2) based on the generated equivalent SP-tree, a series of methods are applied to detect anomalies.