用大模型从物联网日志中自动提炼事件，提升养老健康监测效率
LLM-based event abstraction and integration for IoT-sourced logs
- 用大模型将原始传感器数据转化为高阶事件
- 在老人照护场景中事件识别平均准确率达90%
- 适合做物联网日志处理与流程挖掘的研究者参考
物联网设备持续产生的数据流，已深刻改变了我们对世界的理解和互动方式。然而，这些数据需经预处理并转化为事件数据后才能开展分析。本文探讨了利用大语言模型（LLMs）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生成事件记录，并将多个物联网源的日志整合为适用于流程挖掘的统一事件日志。通过老年照护与长期健康监测的应用案例，验证了大模型在事件抽象中的能力。结果表明，高阶活动检测平均准确率达90%，凸显了大模型在解决事件抽象与集成挑战方面的巨大潜力，有效弥合了现有技术差距。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。