Towards Edge-Based Data Lake Architecture for Intelligent Transportation System
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arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866929486386167808 |
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| author | Fernandes, Danilo Moura, Douglas L. L. Santos, Gean Ramos, Geymerson S. Queiroz, Fabiane Aquino, Andre L. L. |
| author_facet | Fernandes, Danilo Moura, Douglas L. L. Santos, Gean Ramos, Geymerson S. Queiroz, Fabiane Aquino, Andre L. L. |
| contents | The rapid urbanization growth has underscored the need for innovative solutions to enhance transportation efficiency and safety. Intelligent Transportation Systems (ITS) have emerged as a promising solution in this context. However, analyzing and processing the massive and intricate data generated by ITS presents significant challenges for traditional data processing systems. This work proposes an Edge-based Data Lake Architecture to integrate and analyze the complex data from ITS efficiently. The architecture offers scalability, fault tolerance, and performance, improving decision-making and enhancing innovative services for a more intelligent transportation ecosystem. We demonstrate the effectiveness of the architecture through an analysis of three different use cases: (i) Vehicular Sensor Network, (ii) Mobile Network, and (iii) Driver Identification applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_02808 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Towards Edge-Based Data Lake Architecture for Intelligent Transportation System Fernandes, Danilo Moura, Douglas L. L. Santos, Gean Ramos, Geymerson S. Queiroz, Fabiane Aquino, Andre L. L. Databases Artificial Intelligence Networking and Internet Architecture The rapid urbanization growth has underscored the need for innovative solutions to enhance transportation efficiency and safety. Intelligent Transportation Systems (ITS) have emerged as a promising solution in this context. However, analyzing and processing the massive and intricate data generated by ITS presents significant challenges for traditional data processing systems. This work proposes an Edge-based Data Lake Architecture to integrate and analyze the complex data from ITS efficiently. The architecture offers scalability, fault tolerance, and performance, improving decision-making and enhancing innovative services for a more intelligent transportation ecosystem. We demonstrate the effectiveness of the architecture through an analysis of three different use cases: (i) Vehicular Sensor Network, (ii) Mobile Network, and (iii) Driver Identification applications. |
| title | Towards Edge-Based Data Lake Architecture for Intelligent Transportation System |
| topic | Databases Artificial Intelligence Networking and Internet Architecture |
| url | https://arxiv.org/abs/2409.02808 |