基于因果行为轮廓的多重变迁集行为一致性分析

Behavioral Conformance Analysis of Multi-set Transition Based on Causal Behavioral Profile

多重变迁集的行为一致性分析是业务流程管理的关键问题。现有的研究方法主要是通过重名变迁的处理分析相关模型的行为一致性,计算复杂且结果不可信,所以对快速准确地测量含重名变迁的模型间行为一致性程度具有一定的局限性。首先在多重变迁集的基础上扩展了行为约束的4个弱序关系,避免了使用行为轮廓分析行为一致性的不准确性,然后提出了一种测量模型变迁对的行为一致性度的算法,从而得到基于因果行为轮廓的多重变迁集的行为一致性测量方法,最后通过实例验证了该方法的有效性。

Behavioral conformance analysis of multi-set transition is the key issue of business process management.Currently,the research on the behavior conformance analysis between the models with the transitions owning the same name is mainly to deal with the transitions having the same name,and then carry on the behavior conformance analysis,which has some limitation on complex calculation and unreliable results.Firstly,on the basis of multi-set transitions,by analyzing four kinds of weak order relations of transitions in casual behavioral profiles of process models,the inaccuracy of behavioral consistency is avoided by using behavioral profile.Then,an algorithm is proposed to measure the behavioral consistency degree over transition pairs and a method is obtained for computing the degree of consistency of casual behavioral profile of corresponding models containing repeat activities.The specific example shows that the method is very effective.