Article Details

Abstract

In the context of Big Data, the research on Big Data storage and addressing has significant data economic implications for Big Data management. This paper analyzes the current situation of the structuralization of data storage from the perspectives of the physical and logical structures of data storage. With quasi-real-time or real-time data query requirements, based on traditional primary key index query technology, it reorganizes the data storage structure, and pays attention to the prototype of the data model, the rules of data storage and invocation, the data Hash retrieval calculation strategy, the storage and distribution of results, the folding addressing structure model, as well as the improvement of data processing speed and data query and processing capabilities. Applying the Hash folding retrieval strategy and the prototype of the folding function to construct the conceptual model of folding addressing, and applying this concept to the use cases of folding addressing, is of great significance for the research on the Hash folding addressing model for rapid data querying.

Keywords

Hash; folding addressing; storage structure; folding retrieval strategy; Addressing Model

References

1. [1] Sullivan. 2021 China Database Market Report [EB/OL]. (2022-05-10) https://s3.cn-north-1.amazonaws.com.cn/aws-dam-prod/whitepapers/Frost_And_Sullivan_2021_China_Distributed_Database_Market_Report_EN.pdf.
2. [2] Li Guojie, Cheng Xueqi Big data research: major strategic field of future science and technology and economic and social development - research status and scientific thinking of big data [J]. Journal of the Chinese Academy of Sciences, 2013, 27 (6): 647-657
3. [3] Wang Yuanzhuo, Jin Xiaolong, Cheng Xueqi Network Big Data: Current Status and Prospects [J]. Journal of Computer Science, 2013, 36 (6): 1125-1138
4. [4] Wu Haiping, Yu Hongliang, Zheng Weimin, etc Storage and Management Strategies for Massive Data in Networked Audit Systems [J]. Journal of Computer Science, 2006, 29 (4): 4618-4624
5. [5] Liu Haomiao, Wang Ruiping, Shan Shiguang, etc Hash coding learning method based on discrete optimization [J]. Journal of Computer Science, 2019, 42 (5): 1149-1160
6. [6] Li Wujun, Zhou Zhihua Big Data Hash Learning: Current Status and Trends [J]. Scientific Bulletin, 2015, 60 (Z1): 485-490
7. [7] Yan Shuangyong, Liu Changhong, Jiang Aiwen, etc Discriminant cross modal hashing learning algorithm related to semantic coupling [J]. Journal of Computer Science, 2019, 42 (1): 164-175
8. [8] Pei Zefeng, Niu Baoning, Zhang Jinwen Selection of Query Execution Plan under Parallel Query [J]. Computer Applications, 2020, 40 (2): 420-425
9. [9] Meng Xiaofeng, Du Zhijuan Research on Big Data Fusion: Issues and Challenges [J]. Computer Research and Development, 2016, 53 (2): 231-246
10. [10] An Yanzhe, Zhu Yuqing, Wang Jianmin Analysis of the applicability conditions of distributed hash tables in the context of IoT big data [J]. Journal of Computer Science, 2021, 44 (8): 1679-1695
11. [11] Liu Wei, Meng Xiaofeng, Meng Weiyi A Review of Deep Web Data Integration Research [J]. Journal of Computer Science, 2007, 30 (9): 1475-1489
12. [12] Wen Qingfu, Wang Jianmin, Zhu Han, etc Distributed Hash Learning Method for Approximate Neighbor Query [J]. Journal of Computer Science, 2017, 40 (1): 192-206

Funding

Guangdong Education Science Planning Project (Higher Education Research Special) (2022GXJK593); Zhuhai Education and Research Planning Project (2024ZHGHKT339); Zhuhai City Vocational and Technical College Project (KY2024Y04Z)

About the authors

Pang Zhiyun (1979-), male, Han ethnicity, from Zhanjiang, Guangdong, lecturer, visiting scholar at the School of Computer Science and Engineering, South China University of Technology, master's degree, research direction: computer application technology; Xiao Lin (1980-), female, Han ethnicity, from Zigong, Sichuan, associate professor, PhD, research direction: education.