The next event prediction task is one of the research focuses of predictive process monitoring,and the ex-isting deep learning-based prediction methods suffer from long training time,large amount of parameters and high hardware requirements to meet the dynamic nature of business processes.To address these problems,a Sampling-based Next Event Prediction(SNEP)method based on log sampling was proposed.Specifically,the importance of traces was measured by calculating event importance and direct-following activity relationship importance,and some important traces were extracted to represent the original event log.The prefixes of trace were recoded using the One-hot coding approach and a three-layer Long Short Term Memory(LSTM)network prediction model applicable to the next event prediction task was designed.Experiments were conducted in six real event logs to investigate the effec-tiveness of the proposed method and the effect of different sampling rates on the prediction results of the model.The results showed that the pre-sampled next event prediction method had improved prediction accuracy and efficiency in each event log,which could help practitioners to achieve next event prediction tasks efficiently.
关键词
业务流程/下一事件预测/事件日志采样/深度学习/长短期记忆网络
Key words
business process/prediction of next event/event log sampling/deep learning/long short term memory network
维普
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