基于频繁活动集序列编码业务过程预测性监控

业务流程预测性监控是过程管理的重要内容，已有的研究大部分是基于显式的工作流模型进行预测.但是在实际应用中，企业可能并没有对整个过程实施端到端的工作流建模和管理，或者由于权限原因只能够获得部分执行日志，难以基于完整的业务流程模型进行预测，对此，提出了一种基于频繁活动集的序列编码处理日志中的低频活动，并通过搜寻历史相似数据进行预测的方法.该方法能够随着日志的更新适应由于概念漂移导致的模型改变.在真实的数据集上进行的实验结果验证了算法的有效性.

Business Process Predictive Monitoring Based on Sequence Encoding of Frequent Activity Sets

The problem of predicting an ongoing business process based on sequence is discussed. Business process predictive monitoring is an important part of process mining. Most of the existing research focuses on forecasting based on explicit workflow models. However, the enterprise may not implement end-to-end workflow modeling and management for the process, or only own partial execution logs due to permissions. In these cases, it is difficult to make prediction based on the complete process model. This article proposes a frequent activity set based sequence encoding method to handle the low-frequency activities in the log, and performs prediction by searching historical similar data. As the log is updated, the algorithm will adapt to model changes due to the concept drift. The algorithm is validated on real data sets.