Process concept drift detection based on graph convolutional network
Evolution is inevitable in the business process from the perspective of time dimension.The purpose of drift detection in process mining is to find out the points of changing time in the running log,and then divide different business processes before and after evolution.However,the current drift detection algorithms need to specify the e-volution features that should be monitored manually during implementation,which has the problems of low accuracy and long time consuming as well as increases unnecessary burden for users.A detection algorithm based on Graph Convolutional Network was proposed.The traces were transformed into an activity graph,and all features of the traces were characterized by the node information and topological structure of the graph.The global characteristics of the active graph were obtained by using the aggregation characteristics of graph convolution.All information of the activity graph was expressed by adding a virtual node.Finally,Euclidean distance and K-nearest neighbor algorithm were used to detect the position of drift points.The validity of the proposed method was proved by the experimental tests on real public datasets.
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