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.

中文翻译：

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改进工作流分析中工件异常的检测


工作流管理系统（WfMS）被认为是可以在业务流程和服务架构设计中提供结构化组织的完成平台。该系统在基础上包含工作流模型，它可以并行地提供一个或多个任务序列中的流程控制。对任务序列中或任务序列之间发生的工件进行操作和访问可能会因工作流程设计不当而生成意外的工件状态。工作流模型中的工件异常分为两类，即连续异常和并发异常。当工件被冗余写入或在生产之前被访问时，会发生连续异常。另一方面，当在工作流模型中并行写入工件时，可能会发生并发异常。有几种用于异常分析的方法，但是，由于它们的定义，这些方法无法检测到所有异常，并且它们要么效率低下，要么缺乏正确性的证据。在本文中，我们提出了具有改进的 C 树结构（称为 SP 树）的改进检测方法。基于更新的异常定义，我们的异常检测包括两个阶段：1）转换算法从给定的结构化工作流模型生成等效的 SP 树； 2）基于生成的等效SP树，应用一系列方法来检测异常。