skip to main content
10.1145/3697355.3697389acmotherconferencesArticle/Chapter ViewAbstractPublication PagesbdiotConference Proceedingsconference-collections
research-article

A Distributed Cloud Hybrid Storage System for Port Transportation Infrastructure Data

Published: 12 December 2024 Publication History

Abstract

To address the real-time storage and retrieval challenges of massive data in port transportation infrastructure, a distributed cloud hybrid storage system with multiple temporal data is designed to improve the storage and retrieval speed of large-scale data. Input the data generated by port transportation facilities into the ClickHouse, Redis, and Hive distributed hybrid databases, use the hybrid database to improve the fault tolerance and retrieval response speed of the storage model, combined with the index tree structure, and introduce the Presto search engine to achieve efficient and reliable indexing function.

References

[1]
Zheng Zhang A C, Dan Feng A, Zhipeng Tan A, Laurence T. Yang A B, and Jiayang Zheng A. 2018. A light-weight log-based hybrid storage system. J. Parallel and Distrib. Comput. 118 (2018), 307–315.
[2]
Yu Yang Deng Min. 2021. Large scale panoramic data concurrency control and storage technology for urban rail transit comprehensive monitoring system. In Research on Urban Rail Transit, Vol. 24. 195–199.
[3]
Yan Chenghua Guo Jingjing, Liang Yingjie. 2018. Research on Hadoop based Transportation Big Data Storage System. The 19th China Annual Conference on System Simulation Technology and Its Applications (2018).
[4]
Nusrat Sharmin Islam, Xiaoyi Lu, Md. Wasi-Ur-Rahman, Dipti Shankar, and Dhabaleswar K. Panda. 2015. Triple-H: A Hybrid Approach to Accelerate HDFS on HPC Clusters with Heterogeneous Storage Architecture. IEEE (2015), 101–110.
[5]
Longbin Lai, Linfeng Shen, Yanfei Zheng, Kefei Chen, and Jing Zhang. 2012. Analysis for REPERA: A Hybrid Data Protection Mechanism in Distributed Environment. International Journal of Cloud Applications & Computing 2, 1 (2012), 71–82.
[6]
N. Nataraj and R. V. Nataraj. 2023. A novel hybridmeta-heuristic-oriented latency sensitive cloud object storage system. Concurrency and computation: practice and experience 35, 21 (2023), e7672.1–e7672.22.
[7]
Zhou Chao Ning Qunyi. 2018. A Transportation Big Data Storage and Analysis Platform Based on Kudu+Impala. In Computer programming skills and maintenance. 91–92.
[8]
Sridhar Reddy Vulapula and Hima Bindu Valiveti. 2022. Secure and efficient data storage scheme for unstructured data in hybrid cloud environment. Soft Computing 26, 23 (2022), 13145–13152.
[9]
Yao Leiyue Wang taotao. 2022. Big Data Distributed Storage Algorithm for Intelligent Transportation Systems. 39, 01 (2022), 138–142.
[10]
Jiashu Wu, Yang Wang, Jinpeng Wang, Hekang Wang, and Taorui Lin. 2023. How does solid-state drives cluster perform for distributed file systems: An empirical study. Concurrency and computation: practice and experience21 (2023), 35.

Index Terms

  1. A Distributed Cloud Hybrid Storage System for Port Transportation Infrastructure Data

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Other conferences
      BDIOT '24: Proceedings of the 2024 8th International Conference on Big Data and Internet of Things
      September 2024
      412 pages
      ISBN:9798400717529
      DOI:10.1145/3697355
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 12 December 2024

      Check for updates

      Author Tags

      1. port transportation infrastructure
      2. Distributed storage
      3. mixed retrieval

      Qualifiers

      • Research-article

      Conference

      BDIOT 2024

      Acceptance Rates

      Overall Acceptance Rate 75 of 136 submissions, 55%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 8
        Total Downloads
      • Downloads (Last 12 months)8
      • Downloads (Last 6 weeks)1
      Reflects downloads up to 02 Mar 2025

      Other Metrics

      Citations

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Full Text

      View this article in Full Text.

      Full Text

      Figures

      Tables

      Media

      Share

      Share

      Share this Publication link

      Share on social media