构建边缘数据湖架构，高效处理智能交通复杂数据。
Towards Edge-Based Data Lake Architecture for Intelligent Transportation System
- 在边缘侧构建数据湖，实现交通数据的实时集成与分析。
- 支持可扩展、容错强、高性能，提升决策效率与服务创新。
- 适用于车联网、移动网络和驾驶员识别等典型场景。
快速的城市化发展凸显了提升交通效率与安全性的迫切需求。智能交通系统（ITS）在此背景下成为有前景的解决方案。然而，传统数据处理系统难以应对ITS产生的海量且复杂的数据。本文提出一种基于边缘的数据湖架构，以高效整合与分析来自ITS的复杂数据。该架构具备可扩展性、容错性与高性能，有助于提升决策能力并推动智能交通生态系统的创新服务。通过三个应用场景验证了其有效性：(i) 车辆传感器网络，(ii) 移动网络，(iii) 驾驶员识别。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。