With the advent of digital transformation, organisations are increasingly generating large volumes of data through the execution of various processes across disparate systems. By integrating data from these heterogeneous sources, it becomes possible to derive new insights essential for tasks such as monitoring and analysing process performance. Typically, this information is extracted during a data pre-processing or engineering phase. However, this step is often performed in an ad-hoc manner and is time-consuming and labour-intensive. To streamline this process, we introduce a reference model and a collection of patterns designed to enrich production event data. The reference model provides a standard way for storing and extracting production event data. The patterns describe common information extraction tasks and how such tasks can be automated effectively. The reference model is developed by combining the ISA-95 industry standard with the Event Knowledge Graph formalism. The patterns are developed based on empirical observations from event data sets originating in manufacturing processes and are formalised using the reference model. We evaluate the relevance and applicability of these patterns by demonstrating their application to use cases.

翻译：随着数字化转型的推进，组织通过跨异构系统的流程执行，正日益生成海量数据。通过整合这些异构来源的数据，可获取监控与分析流程绩效等任务所需的新洞察。通常，此类信息是在数据预处理或工程阶段提取的。然而，这一步骤往往以临时方式进行，耗时且劳动密集。为简化该流程，我们提出了一种用于增强生产事件数据的参考模型与模式集合。该参考模型提供了存储与提取生产事件数据的标准化方法；模式描述了常见信息提取任务及其高效自动化方式。参考模型通过融合ISA-95行业标准与事件知识图谱形式化构建，而模式则基于对制造流程事件数据集的实证观察形成，并借助参考模型进行形式化描述。我们通过将模式应用于实际场景，验证了其相关性与适用性。