IoT Miner: Intelligent Extraction of Event Logs from Sensor Data for Process Mining
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918136797724672 |
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| author | Brzychczy, Edyta Jessen, Urszula Kluza, Krzysztof Sriram, Sridhar Nettelnstroth, Manuel Vargas |
| author_facet | Brzychczy, Edyta Jessen, Urszula Kluza, Krzysztof Sriram, Sridhar Nettelnstroth, Manuel Vargas |
| contents | This paper presents IoT Miner, a novel framework for automatically creating high-level event logs from raw industrial sensor data to support process mining. In many real-world settings, such as mining or manufacturing, standard event logs are unavailable, and sensor data lacks the structure and semantics needed for analysis. IoT Miner addresses this gap using a four-stage pipeline: data preprocessing, unsupervised clustering, large language model (LLM)-based labeling, and event log construction. A key innovation is the use of LLMs to generate meaningful activity labels from cluster statistics, guided by domain-specific prompts. We evaluate the approach on sensor data from a Load-Haul-Dump (LHD) mining machine and introduce a new metric, Similarity-Weighted Accuracy, to assess labeling quality. Results show that richer prompts lead to more accurate and consistent labels. By combining AI with domain-aware data processing, IoT Miner offers a scalable and interpretable method for generating event logs from IoT data, enabling process mining in settings where traditional logs are missing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05769 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IoT Miner: Intelligent Extraction of Event Logs from Sensor Data for Process Mining Brzychczy, Edyta Jessen, Urszula Kluza, Krzysztof Sriram, Sridhar Nettelnstroth, Manuel Vargas Software Engineering This paper presents IoT Miner, a novel framework for automatically creating high-level event logs from raw industrial sensor data to support process mining. In many real-world settings, such as mining or manufacturing, standard event logs are unavailable, and sensor data lacks the structure and semantics needed for analysis. IoT Miner addresses this gap using a four-stage pipeline: data preprocessing, unsupervised clustering, large language model (LLM)-based labeling, and event log construction. A key innovation is the use of LLMs to generate meaningful activity labels from cluster statistics, guided by domain-specific prompts. We evaluate the approach on sensor data from a Load-Haul-Dump (LHD) mining machine and introduce a new metric, Similarity-Weighted Accuracy, to assess labeling quality. Results show that richer prompts lead to more accurate and consistent labels. By combining AI with domain-aware data processing, IoT Miner offers a scalable and interpretable method for generating event logs from IoT data, enabling process mining in settings where traditional logs are missing. |
| title | IoT Miner: Intelligent Extraction of Event Logs from Sensor Data for Process Mining |
| topic | Software Engineering |
| url | https://arxiv.org/abs/2509.05769 |