Modern Internet of Things (IoT) systems are equipped with a large quantity of sensors providing real-time data about the current operations of their components, which is crucial for the systems' internal control systems and processes. However, these data are often too fine-grained to derive useful insights into the execution of the larger processes an IoT system might be part of. Process mining has developed advanced approaches for the analysis of business processes that may also be used in the context of IoT. Bringing process mining to IoT requires an event abstraction step to lift the low-level sensor data to the business process level. In this work, we aim to enable domain experts to perform this step using a newly developed domain-specific language (DSL) called Radiant. Radiant supports the specification of patterns within the sensor data that indicate the execution of higher level process activities. These patterns are translated to complex event processing (CEP) applications to be used for detecting activity executions at runtime. We propose a corresponding software architecture that enables online event abstraction from IoT sensor streams using the CEP applications. We evaluate these applications to monitor activity executions in smart manufacturing and smart healthcare. These evaluations are useful to inform the domain expert about the quality of activity detections based on the specified patterns and potential for improvement via additional or modified patterns and sensors.

翻译：现代物联网系统配备了大量传感器，能够提供关于其组件当前运行状态的实时数据，这对于系统的内部控制与流程至关重要。然而，这些数据通常过于细粒度，难以从中推导出对物联网系统可能参与的更宏观流程执行情况的有用洞见。过程挖掘已发展出可用于物联网场景的业务流程分析先进方法。将过程挖掘引入物联网需要一个事件抽象步骤，以将底层传感器数据提升至业务流程层面。本工作旨在使领域专家能够使用一种新开发的领域特定语言（称为Radiant）执行此步骤。Radiant支持在传感器数据中指定指示高层过程活动执行情况的模式。这些模式被转换为复杂事件处理应用程序，用于在运行时检测活动执行。我们提出了一种相应的软件架构，支持利用这些CEP应用程序从物联网传感器流进行在线事件抽象。我们通过评估这些应用程序在智能制造与智慧医疗中的活动执行监控来验证其有效性。这些评估有助于向领域专家反馈基于指定模式的活动检测质量，以及通过增补或修改模式与传感器进行改进的潜力。