Publicação
Leveraging Large Language Models for Process Analytics Assistants: Assessing Accuracy in Process Mining Tasks
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | pt_PT |
| dc.contributor.advisor | Caldeira, João Carlos Palmela Pinheiro | |
| dc.contributor.author | Reis, Diogo Alexandre Mousinho dos | |
| dc.date.accessioned | 2025-11-12T10:57:03Z | |
| dc.date.embargo | 2028-10-29 | |
| dc.date.issued | 2025-10-29 | |
| dc.description | Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics | pt_PT |
| dc.description.abstract | The integration of Large Language Models with Process Mining has the potential to enhance business outcomes by improving data-driven decision-making. The employment of visual analytics allows the translation of Process Mining insights for non-technical users, enabling them to make deeper and well-informed decisions, while also supporting integration with decision support systems. This study introduces a framework designed to evaluate the current state of research in this field by employing the latest Large Language Models and prompt engineering techniques, assessing their capability to perform visualisation tasks aligned with Process Mining, from process discovery to predictive analytics. Among the most recent models provided by OpenAI and Anthropic, GPT4.1 demonstrated the strongest performance, achieving an LLMs-as-Judges score of 8,18 ± 2,3 and a Visualisation Error Rate of 25%. Although these results are promising, the findings also indicate that the existing Large Language Models still face challenges in handling domain-specific libraries such as PM4Py, having an increased performance when using widely adopted libraries. This highlights the need for further improvements of these models to fully support automated visual insight generation in Process Mining contexts. | pt_PT |
| dc.identifier.tid | 204072026 | |
| dc.identifier.uri | http://hdl.handle.net/10362/190570 | |
| dc.language.iso | eng | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Large Language Models | pt_PT |
| dc.subject | LLMs-as-Judges | pt_PT |
| dc.subject | Process Mining | pt_PT |
| dc.subject | Prompt Engineering | pt_PT |
| dc.subject | Visual Analytics | pt_PT |
| dc.subject | Visualisation Error Rate | pt_PT |
| dc.subject | SDG 8 - Decent work and economic growth | pt_PT |
| dc.subject | SDG 9 - Industry, innovation and infrastructure | pt_PT |
| dc.title | Leveraging Large Language Models for Process Analytics Assistants: Assessing Accuracy in Process Mining Tasks | pt_PT |
| dc.type | master thesis | |
| dspace.entity.type | Publication | |
| rcaap.embargofct | Aguardo informação sobre irei publicar o artigo numa conferência- | pt_PT |
| rcaap.rights | embargoedAccess | pt_PT |
| rcaap.type | masterThesis | pt_PT |
| thesis.degree.name | Mestrado em Ciência de Dados e Métodos Analíticos Avançados, especialização em Business Analytics | pt_PT |