LLM-based event abstraction and integration for IoT-sourced logs
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866917934824161280 |
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| author | Shirali, Mohsen Sani, Mohammadreza Fani Ahmadi, Zahra Serral, Estefania |
| author_facet | Shirali, Mohsen Sani, Mohammadreza Fani Ahmadi, Zahra Serral, Estefania |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_03478 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLM-based event abstraction and integration for IoT-sourced logs Shirali, Mohsen Sani, Mohammadreza Fani Ahmadi, Zahra Serral, Estefania Databases Emerging Technologies Machine Learning 68M14 I.2.1; H.4.0 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. |
| title | LLM-based event abstraction and integration for IoT-sourced logs |
| topic | Databases Emerging Technologies Machine Learning 68M14 I.2.1; H.4.0 |
| url | https://arxiv.org/abs/2409.03478 |