基于结构分解的因果行为轮廓获取方法  

Capturing Causal Behavioral Profile Based on Structural Decomposition Technique

作　　者：蔡敏[1,2] 汪世义[2] 

机构地区：[1]同济大学计算机科学与技术系,上海201804 [2]巢湖学院计算机与信息工程学院,合肥238000

出　　处：《计算机科学》2014年第1期242-245,249,共5页Computer Science

基　　金：安徽省高等学校省级自然科学基金项目(KJ2013B157;KJ2012Z266)资助

摘　　要：模型因果行为轮廓不仅可用于度量业务模型之间的一致度,还可以监控业务流程的实际执行。针对现有因果行为轮廓获取方法的局限性,提出了基于最小T-不变量分解技术的因果行为轮廓获取方法。该方法首先将工作流系统模型分解成一组完备子系统,再由各完备子系统中的局部关系推导出系统模型中的全局关系,可用于捕获任意sound自由选择工作流系统的因果行为轮廓。The causal behavioral profile can be applied to measure the consistency between two given process models as well as to monitor process execution. To overcome the limitation of existing approach to obtain the causal behavioral profile, a novel approach was presented based on T-invariants decomposition technique. A workflow system is first de- composed into a set of complete subsystems, and then the global relations between transitions are deduced from their lo- cal relations in each complete subsystem. The approach can be used for arbitrary sound free choice workflow systems.

关 键 词：T-不变量 工作流系统 因果行为轮廓 完备子系统 变迁对 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]