面向智能交通系统的基于边缘的数据湖架构
Towards Edge-Based Data Lake Architecture for Intelligent Transportation System
AI总结：
针对智能交通系统数据处理难题，本文提出一种基于边缘的数据湖架构，通过车载传感器网络、移动网络和驾驶员识别三个用例验证其在可扩展性、容错性和性能方面的优势。
摘要：
快速的城市化增长凸显了对创新解决方案的需求，以提高交通效率和安全性。智能交通系统（ITS）在这一背景下已成为一种有前景的解决方案。然而，分析和处理ITS产生的大规模且复杂的数据给传统数据处理系统带来了重大挑战。本文提出了一种基于边缘的数据湖架构，以高效地整合和分析来自ITS的复杂数据。该架构具有可扩展性、容错性和高性能，能够改进决策制定，并为更智能的交通生态系统增强创新服务。我们通过分析三个不同的用例来证明该架构的有效性：（i）车载传感器网络，（ii）移动网络，以及（iii）驾驶员识别应用。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
