Szczegóły publikacji
Opis bibliograficzny
IoT Miner – intelligent extraction of event logs from sensor data for process mining / Edyta BRZYCHCZY, Urszula Jessen, Krzysztof KLUZA, Sridhar Sriram, Manuel Vargas Nettelnstroth // W: Business Process Management workshops : BPM 2025 : international workshops : Seville, Spain, August 31–September 5, 2025 : revised selected papers / eds. Inge van de Weerd, Bedilia Estrada Torres, Han van der Aa. — Cham : Springer, cop. 2026. — ( Lecture Notes in Business Information Processing ; ISSN 1865-1348 ; LNBIP 569 ). — ISBN: 978-3-032-13425-7; e-ISBN: 978-3-032-13426-4. — S. 232–246. — Bibliogr., Abstr. — Publikacja dostępna online od: 2026-04-01
Autorzy (5)
- AGHBrzychczy Edyta
- Jessen Urszula
- AGHKluza Krzysztof
- Sriram Sridhar
- Nettelnstroth Manuel Vargas
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 167678 |
|---|---|
| Data dodania do BaDAP | 2026-06-10 |
| DOI | 10.1007/978-3-032-13426-4_17 |
| Rok publikacji | 2026 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference in Business Process Management 2025 |
| Czasopismo/seria | Lecture Notes in Business Information Processing |
Abstract
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.
