Publicação
EdgeIM
| Resumo: | The rapid expansion of Internet of Things (IoT) devices has led to an explosion of event data, posing significant challenges for traditional process model discovery techniques in terms of scalability and discovery accuracy. These techniques rely on centralized storage and processing, which are hindered by data transfer limitations, storage capacity, and computational overhead in distributed IoT environments. Edge-based model discovery techniques offer a promising solution for analyzing large-scale IoT data. However, existing techniques suffer from low efficiency and an inability to handle complex process structures. To address these challenges, we propose EdgeIM, an efficient edge-based process model discovery technique that enhances efficiency and model accuracy. EdgeIM operates in three key stages: preprocessing and feature-preserving sampling to eliminate redundant data, local processing at edge nodes to extract key structural features, and global feature aggregation at a central node for model discovery. EdgeIM has been implemented on the open-source process mining platform PM4Py, and experimental results on nine public event logs demonstrate that, compared to existing edge-based model discovery techniques, EdgeIM significantly improves discovery efficiency while maintaining high model quality. |
|---|---|
| Autores principais: | Su, Xuan |
| Outros Autores: | Liu, Cong; Lu, Faming; Cheng, Long; Zeng, Qingtian; Zhang, Shouli |
| Assunto: | Edge Computing Internet of Things Model Discovery Process Mining Information Systems Computer Science Applications Computer Networks and Communications Information Systems and Management Artificial Intelligence |
| Ano: | 2025 |
| País: | Portugal |
| Tipo de documento: | documento de conferência |
| Tipo de acesso: | acesso embargado |
| Instituição associada: | Universidade Nova de Lisboa |
| Idioma: | inglês |
| Origem: | Repositório Institucional da UNL |
| Resumo: | The rapid expansion of Internet of Things (IoT) devices has led to an explosion of event data, posing significant challenges for traditional process model discovery techniques in terms of scalability and discovery accuracy. These techniques rely on centralized storage and processing, which are hindered by data transfer limitations, storage capacity, and computational overhead in distributed IoT environments. Edge-based model discovery techniques offer a promising solution for analyzing large-scale IoT data. However, existing techniques suffer from low efficiency and an inability to handle complex process structures. To address these challenges, we propose EdgeIM, an efficient edge-based process model discovery technique that enhances efficiency and model accuracy. EdgeIM operates in three key stages: preprocessing and feature-preserving sampling to eliminate redundant data, local processing at edge nodes to extract key structural features, and global feature aggregation at a central node for model discovery. EdgeIM has been implemented on the open-source process mining platform PM4Py, and experimental results on nine public event logs demonstrate that, compared to existing edge-based model discovery techniques, EdgeIM significantly improves discovery efficiency while maintaining high model quality. |
|---|
Atividades financiadas
Não foram encontrados projetos financiados.