“机器学习”在工作流模型设定中的应用

APPLICATION OF “MACHINE LEARNING” IN THE WORKFLOW MODEL ENACTMENT

针对目前工作流建模方法的局限性,以及工作流自适应的需求,采用“机器学习”的思想,基于工作流实例信息,提出了动态设定工作流模型的方法。采用隐马尔卡夫模型(HMM)对工作流模型的结构进行了表示,并定义了相应的合并与分离操作符, 为进一步转换成正确、合适的工作流模型提供了支持。

According to the idea of the “machine learning”, a dynamic method of the workflow model enactment based on the information of the workflow instances is presented for improving the way of workflow modeling and satisfying the requirement of the workflow adaptation. The structure of the workflow model is showed with HMM（ hidden markov models）. Furthermore,the corresponding operators of merging and splitting are defined for acquiring appropriate HMM. It will also supports transforming the HMM into the correct workflow model later.