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基于日志采样的流程下一事件预测方法

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下一事件预测任务是预测性流程监控的研究重点之一,针对现有基于深度学习的预测方法存在训练时间过长、参数量过大、对硬件要求过高等无法满足业务流程动态性的问题,提出一种基于日志采样的下一事件预测方法(SNEP).通过计算事件重要性和直接跟随活动关系重要性来衡量轨迹重要性,抽取部分重要轨迹表示原事件日志;采用One-hot编码方式对轨迹前缀重新编码,并设计了适用下一事件预测任务的三层长短期记忆网络(LSTM)预测模型.在6个真实事件日志中进行实验,探究所提方法的有效性和不同采样率对模型预测结果的影响,结果表明预先采样的下一事件预测方法在各事件日志中的预测准确率和效率均有提升,验证了该方法的优越性.
Process next event prediction method based on event log sampling
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.

business processprediction of next eventevent log samplingdeep learninglong short term memory network

董乐乐、刘聪、张帅鹏、倪维健、任崇广、曾庆田

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山东理工大学计算机科学与技术学院,山东 淄博 255000

山东科技大学计算机科学与工程学院,山东 青岛 266590

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

国家自然科学基金资助项目国家自然科学基金资助项目山东省泰山学者工程专项基金资助项目山东省泰山学者工程专项基金资助项目山东省自然科学基金优秀青年基金资助项目山东省高等学校青创科技计划创新团队资助项目

6247226452374221tsqn201909109ts20190936ZR2021YQ452021KJ031

2024

10.13196/j.cims.2023.0086

计算机集成制造系统
中国兵器工业集团第210研究所

计算机集成制造系统

CSTPCD北大核心
影响因子：1.092
ISSN：1006-5911
年,卷(期)：2024.30(10)