# Towards Edge-Based Data Lake Architecture for Intelligent Transportation System

arXiv: https://arxiv.org/abs/2409.02808 · HTML: https://mlsanity.com/paper/2409.02808/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 29 references that are in the map.
- First posted on arXiv 4 Sep 2024.

## Builds on (most cited first, 2 of 2)

- [On data lake architectures and metadata management](https://mlsanity.com/paper/2107.11152/) (2021)
- [This Car is Mine!: Automobile Theft Countermeasure Leveraging Driver Identification with Generative Adversarial Networks](https://mlsanity.com/paper/1911.09870/) (2019)

## Abstract (arXiv)

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.