论文标题
自动模拟和通过过程挖掘发现的过程模型的验证
Automated simulation and verification of process models discovered by process mining
论文作者
论文摘要
本文提出了一种新的方法，用于使用过程挖掘技术发现的过程模型自动分析。流程挖掘探讨了由各种设备生成的事件数据中隐藏的基础过程。我们提出的归纳机器学习方法用于基于从酒店的物业管理系统（PMS）获得的实际事件日志数据来构建业务流程模型。 PMS可以视为多代理系统（MAS），因为它与各种外部系统和IoT设备集成在一起。收集的活动日志结合了酒店工作人员记录的客人的数据，以及通过电话交换和其他外部物联网设备捕获的数据流。接下来，我们使用正式方法对发现的过程模型进行了自动分析。旋转模型检查器用于模拟过程模型执行并自动验证过程模型。我们提出了一种将发现的过程模型自动转换为验证模型的算法。此外，我们开发了一个正面和负面例子的发生器。在验证阶段，我们还使用线性时间逻辑（LTL）来定义请求的系统规范。我们发现分析结果将非常适合过程模型维修。
This paper presents a novel approach for automated analysis of process models discovered using process mining techniques. Process mining explores underlying processes hidden in the event data generated by various devices. Our proposed Inductive machine learning method was used to build business process models based on actual event log data obtained from a hotel's Property Management System (PMS). The PMS can be considered as a Multi Agent System (MAS) because it is integrated with a variety of external systems and IoT devices. Collected event log combines data on guests stay recorded by hotel staff, as well as data streams captured from telephone exchange and other external IoT devices. Next, we performed automated analysis of the discovered process models using formal methods. Spin model checker was used to simulate process model executions and automatically verify the process model. We proposed an algorithm for the automatic transformation of the discovered process model into a verification model. Additionally, we developed a generator of positive and negative examples. In the verification stage, we have also used Linear temporal logic (LTL) to define requested system specifications. We find that the analysis results will be well suited for process model repair.
