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

Abstract: The next event prediction task is one of the research focuses of predictive process monitoring,and the existing 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 effectiveness 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.

Key words: business process, prediction of next event, event log sampling, deep learning, long short term memory network