Using Classification Method for Querying the Relevant Process Models

Operations management is important to a company, so more and more business process models are created. At the same time, how to manage such a large amount of process models is becoming a big challenge for companies. Querying the relevant process models is proposed as a business process management technology and it has attracted more and more attention by researchers. The existing methods query the relevant models for a query process model by measuring their similarities. And most of them measure the similarity by focusing on only one kind of feature, such as the structural features or behavioral features, while ignoring other features. In this paper, we consider both structural features and behavioral features to query the relevant process models for a query process model. In order to reach this goal, we use two classification methods named back propagation neural network (BPNN) and support vector machines(SVM) for classifying the candidate models in the repository into two classes: relevant and irrelevant. For the sake of classification, we summarize 7 features to represent the similar or dissimilar parts of two process models. The experiment result shows the precision and efficiency of the classification methods are acceptable.