论文标题
在运行时关联未标记的事件
Correlating Unlabeled Events at Runtime
论文作者
论文摘要
对于以数据为中心和以过程为中心的系统，过程挖掘至关重要。过程挖掘接收所谓的过程日志，这些过程日志是部分订购事件的集合。一个事件必须至少具有三个属性，案例ID，任务ID和一个用于采矿方法的时间戳。当情况ID未知时，该事件称为未标记。传统上，流程挖掘是一个离线任务，通常手动相关从不同来源收集事件。也就是说，属于同一实例的事件分配了同一情况ID。凭借当今的大容量/高速性质，例如物联网应用程序，流程挖掘转变为在线任务。为此，事件相关必须是自动化的，并且必须在生成数据时发生。在本文中，我们介绍了一种将运行时未标记事件关联的方法。给定过程模型，一系列未标记的事件以及有关任务持续时间的其他信息，我们的方法可以为具有信任百分比的一组未标记的事件诱导案例标识符。它还可以检查确定情况与过程模型的符合性。针对现实生活和合成日志实施并评估了所提出方法的原型。
Process mining is of great importance for both data-centric and process-centric systems. Process mining receives so-called process logs which are collections of partially-ordered events. An event has to possess at least three attributes, case ID, task ID and a timestamp for mining approaches to work. When a case ID is unknown, the event is called unlabeled. Traditionally, process mining is an offline task, where events are collected from different sources are usually manually correlated. That is, events belonging to the same instance are assigned the same case ID. With today's high-volume/high-speed nature of, e.g., IoT applications, process mining shifts to be an online task. For this, event correlation has to be automated and has to occur as the data is generated. In this paper, we introduce an approach that correlates unlabeled events at runtime. Given a process model, a stream of unlabeled events and other information about task duration, our approach can induce a case identifier to a set of unlabeled events with a trust percentage. It can also check the conformance of the identified cases with the process model. A prototype of the proposed approach was implemented and evaluated against real-life and synthetic logs.
