In business process management, the monitoring service is an important element that can prevent various problems in advance from before they occur in companies and industries. Execution log is created in an information system that is aware of the enterprise process, which helps predict the process. The ultimate goal of the proposed method is to predict the process following the running process instance and predict events based on previously completed event log data. Companies can flexibly respond to unwanted deviations in their workflow. When solving the next event prediction problem, we use a fully attention-based transformer, which has performed well in recent natural language processing approaches. After recognizing the name attribute of the event in the natural language and predicting the next event, several necessary elements were applied. It is trained using the proposed deep learning model according to specific pre-processing steps. Experiments using various business process log datasets demonstrate the superior performance of the proposed method. The name of the process prediction model we propose is “POP-ON”.

中文翻译：

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POP-ON：基于NLP方法的单向语言模型对过程的预测

在业务流程管理中，监视服务是一个重要元素，可以防止各种问题提前出现在公司和行业中。执行日志是在了解企业过程的信息系统中创建的，该信息系统有助于预测过程。所提出的方法的最终目标是根据正在运行的流程实例预测流程，并根据先前完成的事件日志数据预测事件。公司可以灵活地响应其工作流程中的不必要偏差。在解决下一个事件预测问题时，我们使用完全基于注意力的转换器，该转换器在最近的自然语言处理方法中表现良好。在用自然语言识别事件的名称属性并预测下一个事件之后，应用了一些必要的元素。根据特定的预处理步骤，使用建议的深度学习模型对其进行训练。使用各种业务流程日志数据集的实验证明了该方法的优越性能。我们建议的过程预测模型的名称为“ POP-ON”。