国家重点研发计划(2019YFB1704003); 国家自然科学基金(62021002); 北京市教育委员会科学研究计划(KM202310028003)
合规性检查是过程挖掘领域的重要场景之一, 其目标是判断实际运行的业务行为与理想的业务行为是否一致, 进而为业务过程管理提供决策依据. 传统的合规性检查方法存在度量指标过多、效率低等问题. 此外, 现有研究在检查过程文本与过程模型之间的合规性时严重依赖专家知识. 为此, 提出面向过程文本的合规性检查方法. 首先, 基于过程模型的执行语义生成图轨迹, 并利用词向量模型提取图轨迹中的结构特征. 同时, 引入霍夫曼树提升词向量模型的效率. 接着, 对过程文本和模型中的活动特征进行提取, 并利用孪生机制提升训练效率. 最后, 对所有特征进行融合, 并利用全连接层预测过程文本与过程模型之间的一致性得分. 实验表明, 所提方法的平均绝对误差值要比已有方法低2个百分点.
Conformance checking is one of the important scenarios in the field of process mining, and its goal is to determine whether the actual running business behavior is consistent with the desired behavior and then provide a basis for business process management decisions. Traditional methods of conformance checking face the problems of too many metrics and low efficiency. In addition, the existing methods for checking the conformance between process text and process model rely heavily on expert-defined knowledge. Therefore, this study proposes a process text-oriented conformance checking method. Firstly, the study generates graph traces based on the execution semantics of the process model and obtains the structural features by the word vector model from graph traces. At the same time, Hoffman trees are introduced to reduce the computational effort. Then, the word vector representation of the process text and the activities is performed. The study also uses the Siamese mechanism to improve training efficiency. Finally, all the features of the text and the model are fused, and then the consistency score between the text and the model is predicted using a fully connected layer. Experiments show that the average absolute error value of the method in this study is two percentage points lower than that of existing methods.