Szczegóły publikacji
Opis bibliograficzny
Enhancement of low-level event abstraction with Large Language Models (LLMs) / Edyta BRZYCHCZY, Krzysztof KLUZA, Leszek Szała // W: Business Process Management workshops : BPM 2024 international workshops : Krakow, Poland, September 1–6, 2024 : revised selected papers / eds. Katarzyna Gdowska, María Teresa Gómez-López, Jana-Rebecca Rehse. — Cham : Springer Nature, cop. 2025. — (Lecture Notes in Business Information Processing ; ISSN 1865-1348 ; LNBIP 534). — ISBN: 978-3-031-78665-5; e-ISBN: 978-3-031-78666-2. — S. 209–220. — Bibliogr. s. 219-220, Abstr. — Publikacja dostępna online od: 2025-02-20
Autorzy (3)
- AGHBrzychczy Edyta
- AGHKluza Krzysztof
- Szała Leszek
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 158384 |
|---|---|
| Data dodania do BaDAP | 2025-03-31 |
| DOI | 10.1007/978-3-031-78666-2_16 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference in Business Process Management 2024 |
| Czasopismo/seria | Lecture Notes in Business Information Processing |
Abstract
Event abstraction enables transformation of low level events into higher level events making process mining (PM) on sensor data available. There are many approaches to event abstraction described in literature, however the main approaches include supervised or unsupervised techniques. We address the challenge of transforming low-level sensor data into high-level activities required for effective process mining, a task traditionally reliant on domain experts. By leveraging LLMs to automate the labelling process of sensor data clusters, we bridge the gap between raw data and process models. Motivated by a mining industry use case, we validated the effectiveness of LLMs in accurately labelling operational phases. Our LLM-generated labelling rules demonstrated high accuracy and interpretability, simplifying the understanding for domain experts. Additionally, we compared our LLM-based approach with a Decision Tree Classifier, highlighting the advantages of LLMs in generating simpler, more understandable labelling functions. Our work underscores the potential of advanced AI techniques to enhance the efficiency and accuracy of PM, contributing to the Industry 4.0 initiative.
