Considerable amounts of business process event logs are collected by information systems,model discovery aims to discover process models from event logs to provide evidence for business process improvement.As the most basic behavior information in the event log,Directly Follow relation(DF)is the basis of the model discovery algo-rithm.According to the frequency of the directly follow relation in the event log,the existing model discovery algo-rithms can be divided into two types:with frequency and without frequency.The existing log sampling methods for model discovery focus on improving the efficiency of model discovery,but lose the DF frequency information in the event log.The sample log obtained changes the behavior of the original log when using the DF frequency-based model discovery algorithm.Therefore,for the DF frequency-based model discovery algorithm,a behavior invariance-oriented event log sampling method was proposed,which included three-stage sampling process of reducing the fre-quency of trace variants,calculating the DF weight of the trace and one-time set coverage sampling method to ensure that the behavior of the process model mined with the sample event log and the original log was consistent.Through the experimental analysis on the public event log data set,compared with the existing log sampling methods,the proposed sample log could more accurately retain the DF frequency information in the original log,thus ensuring a higher quality of model mining.
NETL
NSTL
万方数据
