The IoT and Business Process Management (BPM) communities co-exist in many shared application domains, such as manufacturing and healthcare. The IoT community has a strong focus on hardware, connectivity and data; the BPM community focuses mainly on finding, controlling, and enhancing the structured interactions among the IoT devices in processes. While the field of Process Mining deals with the extraction of process models and process analytics from process event logs, the data produced by IoT sensors often is at a lower granularity than these process-level events. The fundamental questions about extracting and abstracting process-related data from streams of IoT sensor values are: (1) Which sensor values can be clustered together as part of process events?, (2) Which sensor values signify the start and end of such events?, (3) Which sensor values are related but not essential? This work proposes a framework to semi-automatically perform a set of structured steps to convert low-level IoT sensor data into higher-level process events that are suitable for process mining. The framework is meant to provide a generic sequence of abstract steps to guide the event extraction, abstraction, and correlation, with variation points for plugging in specific analysis techniques and algorithms for each step. To assess the completeness of the framework, we present a set of challenges, how they can be tackled through the framework, and an example on how to instantiate the framework in a real-world demonstration from the field of smart manufacturing. Based on this framework, future research can be conducted in a structured manner through refining and improving individual steps.

翻译：物联网（IoT）与业务流程管理（BPM）社区在制造、医疗等许多共享应用领域共存。IoT社区主要关注硬件、连接性和数据；而BPM社区则侧重于在流程中发现、控制和增强IoT设备间的结构化交互。尽管流程挖掘领域致力于从流程事件日志中提取流程模型并进行流程分析，但IoT传感器产生的数据通常比流程级事件的粒度更细。从IoT传感器数据流中提取和抽象流程相关数据的基本问题包括：（1）哪些传感器值可作为流程事件的一部分进行聚类？（2）哪些传感器值标志此类事件的开始和结束？（3）哪些传感器值虽相关但不关键？本文提出一个框架，通过半自动执行一组结构化步骤，将低层IoT传感器数据转换为适合流程挖掘的高层流程事件。该框架提供一组通用的抽象步骤序列，用于指导事件提取、抽象和关联，并为每个步骤提供具体分析技术和算法的可插拔变体点。为评估框架的完备性，我们提出一系列挑战及其通过框架解决的方法，并展示了在智能制造领域将框架实例化的真实案例。基于此框架，未来研究可通过细化与改进各个步骤进行结构化推进。