关键词: 通讯行为轮廓, 行为紧密度, 低频行为, 特征网, 融合, Petri网, 业务流程

Abstract: Low-frequency behavior mining is one of the important contents of business process management.It is especially important to distinguish effective low-frequency and noise in business process optimization.The existing mining methods mostly study low-frequency behavior from data attributes,and less frequently analyze low-frequency behaviors according to behavior attributes among different modules.On this basis,a method of mining and optimizing the low-frequency behavior of business process was proposed based on Petri nets.The log behavioral relationship was illustrated with the direct flow graph cut by process tree and matched with the initial model to find all the low frequency sequences.The behavior distance between the log and the model was calculated.The effective low-frequency log and noise log was divided based on the behavior closeness to optimize the event log.An optimized business process model was obtained by using the event log without noise sequence to merge the interactive module network and the feature network.The feasibility and effectiveness of the proposed method were verified by concrete example analysis and simulation experiments.

Key words: communication behavior profile, behavioral closeness, low frequency behavior, feature network, fusion, Petri net, business process