The execution of processes leaves traces of event data in information systems. These event data can be analyzed through process mining techniques. For traditional process mining techniques, one has to associate each event with exactly one object, e.g., the company's customer. Events related to one object form an event sequence called a case. A case describes an end-to-end run through a process. The cases contained in event data can be used to discover a process model, detect frequent bottlenecks, or learn predictive models. However, events encountered in real-life information systems, e.g., ERP systems, can often be associated with multiple objects. The traditional sequential case concept falls short of these object-centric event data as these data exhibit a graph structure. One might force object-centric event data into the traditional case concept by flattening it. However, flattening manipulates the data and removes information. Therefore, a concept analogous to the case concept of traditional event logs is necessary to enable the application of different process mining tasks on object-centric event data. In this paper, we introduce the case concept for object-centric process mining: process executions. These are graph-based generalizations of cases as considered in traditional process mining. Furthermore, we provide techniques to extract process executions. Based on these executions, we determine equivalent process behavior with respect to an attribute using graph isomorphism. Equivalent process executions with respect to the event's activity are object-centric variants, i.e., a generalization of variants in traditional process mining. We provide a visualization technique for object-centric variants. The contribution's scalability and efficiency are extensively evaluated. Furthermore, we provide a case study showing the most frequent object-centric variants of a real-life event log.
翻译:执行过程会留下信息系统中事件数据的痕迹。 这些事件数据可以通过过程采矿技术分析。 对于传统的过程采矿技术, 人们必须把每个事件都与一个精确的物体(例如公司客户)联系起来。 与一个对象相关的事件构成一个事件序列, 称为案件。 一个案例描述一个过程的端到端运行过程。 包含在情况中的案件可以用来发现一个过程模型, 发现常见的瓶颈, 或学习预测模型。 但是, 在现实信息系统中遇到的事件, 例如, ERP系统, 往往可以与多个对象相联系。 传统的连续案件概念比传统目标中心事件数据要少, 因为这些数据显示一个图形结构结构结构结构结构结构结构结构。 但是, 平整数据操作数据, 并删除信息。 因此, 一个类似传统事件日志概念是必要的, 使得不同过程(例如, ERP系统) 能够应用不同过程(例如, ERP系统) 与多个对象中心进程(例如, ) 运行过程的立标概念概念差。 这些数据显示一个基于图表的进度, 以直径直径, 和直径直径等的处决过程, 提供了我们所研究的直径直径的直径 的直路路路路路。