Advances in Internet-of-Things (IoT) technologies have prompted the integration of IoT devices with business processes (BPs) in many organizations across various sectors, such as manufacturing, healthcare and smart spaces. The proliferation of IoT devices leads to the generation of large amounts of IoT data providing a window on the physical context of BPs, which facilitates the discovery of new insights about BPs using process mining (PM) techniques. However, to achieve these benefits, IoT data need to be combined with traditional process (event) data, which is challenging due to the very different characteristics of IoT and process data, for instance in terms of granularity levels. Recently, several data models were proposed to integrate IoT data with process data, each focusing on different aspects of data integration based on different assumptions and requirements. This fragmentation hampers data exchange and collaboration in the field of PM, e.g., making it tedious for researchers to share data. In this paper, we present a core model synthesizing the most important features of existing data models. As the core model is based on common requirements, it greatly facilitates data sharing and collaboration in the field. A prototypical Python implementation is used to evaluate the model against various use cases and demonstrate that it satisfies these common requirements.

翻译：物联网（IoT）技术的进步促使许多组织在制造、医疗和智能空间等多个领域将物联网设备与业务流程（BPs）相结合。物联网设备的激增导致大量物联网数据的生成，这些数据为业务流程的物理环境提供了观察窗口，从而有助于通过过程挖掘（PM）技术发现关于业务流程的新见解。然而，要实现这些优势，物联网数据需要与传统过程（事件）数据相结合，这由于物联网数据与过程数据在粒度级别等方面存在显著不同的特性而具有挑战性。近年来，已有多种数据模型被提出以整合物联网数据与过程数据，每种模型基于不同的假设和需求，侧重于数据整合的不同方面。这种碎片化阻碍了过程挖掘领域的数据交换与协作，例如使研究人员共享数据变得繁琐。本文提出了一种核心模型，综合了现有数据模型的最重要特征。由于该核心模型基于共同需求，它极大地促进了该领域的数据共享与协作。通过一个原型Python实现，我们评估了该模型在多种用例中的表现，并证明其满足了这些共同需求。