Efficient Approach to Store Big Proteomic Data Based on NoSQL Database

被引：0
|
作者：
Zhang, Lin [1 ]
Sang, Zhe [2 ]
Ye, Xue-Ying [2 ]
Shen, Yuan-Hai [2 ]
Tan, Jun [2 ]
Bai, Ming-Ze [2 ]
机构：
[1] Chongqing Univ Posts & Telecommun, Coll Comp Sci & Technol, Chongqing 400065, Peoples R China
[2] Chongqing Univ Posts & Telecommun, Coll Bioinformat, Chongqing 400065, Peoples R China
来源：
COMPUTER SCIENCE AND TECHNOLOGY (CST2016) | 2017年
关键词：
Big data; Proteomics; Non-relational database; MongoDB database;
D O I：
暂无
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
To solve the inefficient bottleneck problems of the traditional relational databases in big data storage and access, this paper proposes an efficient approach to store big proteomic data based on NoSQL (Not only SQL) database. MongoDB, a typical software of NoSQL, is compared with MySQL in data storage and query performance. The experiment results demonstrate that the query and read speed of NoSQL database has been improved significantly as compared to the traditional relational database, especially for mass unstructured and semi-structured data.
引用
收藏
页码：641 / 649
页数：9
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