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）社群在许多共同的应用领域（如制造业与医疗保健）中共存。物联网社群高度关注硬件、连接性与数据；BPM社群则主要致力于发现、控制并增强流程中物联网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，但物联网传感器所产生的数据通常比这些流程级事件的粒度更低。从物联网传感器数值流中提取和抽象流程相关数据的基本问题在于：（1）哪些传感器数值可作为流程事件的一部分被聚类？（2）哪些传感器数值标志着此类事件的开始与结束？（3）哪些传感器数值相关但非必需？本研究提出一个框架，通过一系列结构化步骤半自动地将低层物联网传感器数据转换为适用于流程挖掘的高层流程事件。该框架旨在提供一套通用的抽象步骤序列，以指导事件提取、抽象与关联，其中包含可插入特定分析技术与算法的可变点，用于各个步骤。为评估框架的完整性，我们提出了一系列挑战、说明如何通过框架应对这些挑战，并给出一个在智能制造领域的实际演示中实例化框架的示例。基于此框架，未来研究可通过细化和改进各个步骤，以结构化的方式展开。