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Distributed Big Data Storage Infrastructure for Biomedical Research Featuring High-Performance and Rich-Features

Author

Listed:
  • Xingjian Xu

    (College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China)

  • Lijun Sun

    (College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China)

  • Fanjun Meng

    (College of Computer Science and Technology, Inner Mongolia Normal University, Hohhot 010022, China)

Abstract

The biomedical field entered the era of “big data” years ago, and a lot of software is being developed to tackle the analysis problems brought on by big data. However, very few programs focus on providing a solid foundation for file systems of biomedical big data. Since file systems are a key prerequisite for efficient big data utilization, the absence of specialized biomedical big data file systems makes it difficult to optimize storage, accelerate analysis, and enrich functionality, resulting in inefficiency. Here we present F3BFS, a functional, fundamental, and future-oriented distributed file system, specially designed for various kinds of biomedical data. F3BFS makes it possible to boost existing software’s performance without modifying its main algorithms by transmitting raw datasets from generic file systems. Further, F3BFS has various built-in features to help researchers manage biology datasets more efficiently and productively, including metadata management, fuzzy search, automatic backup, transparent compression, etc.

Suggested Citation

  • Xingjian Xu & Lijun Sun & Fanjun Meng, 2022. "Distributed Big Data Storage Infrastructure for Biomedical Research Featuring High-Performance and Rich-Features," Future Internet, MDPI, vol. 14(10), pages 1-13, September.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:10:p:273-:d:924380
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    References listed on IDEAS

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    1. Samina Amin & Muhammad Irfan Uddin & Duaa H. alSaeed & Atif Khan & Muhammad Adnan & Furqan Aziz, 2021. "Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches," Complexity, Hindawi, vol. 2021, pages 1-12, March.
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    Cited by:

    1. Davide Tosi, 2023. "Editorial for the Special Issue on “Software Engineering and Data Science”, Volume II," Future Internet, MDPI, vol. 15(9), pages 1-2, September.

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