The continuous flow of data collected by Internet of Things (IoT) devices, has revolutionised our ability to understand and interact with the world across various applications. However, this data must be prepared and transformed into event data before analysis can begin. In this paper, we shed light on the potential of leveraging Large Language Models (LLMs) in event abstraction and integration. Our approach aims to create event records from raw sensor readings and merge the logs from multiple IoT sources into a single event log suitable for further Process Mining applications. We demonstrate the capabilities of LLMs in event abstraction considering a case study for IoT application in elderly care and longitudinal health monitoring. The results, showing on average an accuracy of 90% in detecting high-level activities. These results highlight LLMs' promising potential in addressing event abstraction and integration challenges, effectively bridging the existing gap.

翻译：物联网设备持续采集的数据流，已彻底改变了我们在各类应用中理解世界并与之交互的能力。然而，这些数据在开始分析前必须经过预处理并转换为事件数据。本文揭示了利用大型语言模型进行事件抽象与集成的潜力。我们的方法旨在从原始传感器读数中创建事件记录，并将来自多个物联网源的日志合并为适合进一步进行过程挖掘应用的单一事件日志。我们通过一个物联网在老年护理与长期健康监测中的应用案例研究，展示了LLM在事件抽象方面的能力。结果显示，在检测高层级活动方面平均准确率达到90%。这些结果凸显了LLM在应对事件抽象与集成挑战方面的巨大潜力，有效弥合了现有差距。