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

Xingjian Xu (), Lijun Sun and Fanjun Meng
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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

Future Internet, 2022, vol. 14, issue 10, 1-13

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.

Keywords: distributed file system; biomedical big data; big data storage (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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