LLM-based event abstraction and integration for IoT-sourced logs

TL;DR

This paper explores how Large Language Models can be used to transform raw IoT sensor data into structured event logs, demonstrating high accuracy in elderly care applications and facilitating process mining.

Contribution

It introduces a novel approach leveraging LLMs for event abstraction and integration of IoT logs, addressing key challenges in data preparation for analysis.

Findings

Achieved 90% accuracy in detecting high-level activities

Successfully merged multi-source IoT logs into unified event records

Demonstrated potential for LLMs in process mining applications

Abstract

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.