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Correlating Unlabeled Events at Runtime
arXiv - CS - Other Computer Science Pub Date : 2020-04-19 , DOI: arxiv-2004.09971
Iman M. A. Helal and Ahmed Awad

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.

中文翻译:

在运行时关联未标记的事件

流程挖掘对于以数据为中心和以流程为中心的系统都非常重要。流程挖掘接收所谓的流程日志,这些日志是部分有序事件的集合。一个事件必须至少拥有三个属性,案例 ID、任务 ID 和时间戳才能使挖掘方法发挥作用。当案例 ID 未知时,该事件称为未标记事件。传统上,流程挖掘是一项离线任务,其中从不同来源收集的事件通常是手动关联的。即,属于同一实例的事件被分配相同的案例 ID。随着当今物联网应用的高容量/高速特性,流程挖掘转变为在线任务。为此,事件关联必须自动化,并且必须在生成数据时发生。在本文中,我们引入了一种在运行时关联未标记事件的方法。给定流程模型、未标记事件流和有关任务持续时间的其他信息,我们的方法可以将案例标识符引入具有信任百分比的一组未标记事件。它还可以检查已识别案例与流程模型的一致性。所提出的方法的原型被实施并根据现实生活和合成日志进行评估。
更新日期:2020-04-22
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