Compression Strategies for Nation-Scale Mobility Data Vaults
DOI:
https://doi.org/10.5281/zenodo.20579566Keywords:
National Transportation Data Platforms, Big Data Storage Optimization, Transportation Data Ecosystems, Data Fusion in Transportation Analytics, Large-Scale Data Storage Architectures, Heterogeneous Transportation Data Integration, High-Velocity Transportation Data Processing, Cloud-Native Transportation Data Systems, National Infrastructure Data Platforms, Scalable Big Data Storage Systems, Data-Driven Transportation Analytics, Distributed Data Storage Technologies, High-Volume Data Management Systems, Transportation Data Engineering Frameworks.Abstract
Most national organizations are establishing national platforms to collect, analyze, and share transportation data. Although data storage is a critical component, it often does not receive the same level of attention as other aspects. This paper addresses big data storage optimization techniques for national transportation data platforms. It examines the data ecosystem of a hypothetical national platform and details the types of anticipated data storage optimization. The research identifies relevant techniques, summarizes representative configurations, and discusses quantifiable effects in existing platforms.
National platforms enable data-driven insights for public and private sectors by offering a unified environment for transportation analytic applications based on data fusion—combining multiple data sources and types into a single data set for analysis. Big data technologies are essential for realizing these platforms, as they require significant computing and storage facilities to process large volumes of heterogeneous data that arrive at high velocities with the potential for high veracity. Even though storage systems are a key component, comparing large-scale storage architecture and optimization techniques remains largely unexplored. Such analysis is particularly important for national platforms because transport constitutes a basic national infrastructure, and disruption and degradation will incur heavy economic losses. Without cheap access costs, deployment design decisions cannot be met.
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