TY - GEN
T1 - A Database File Storage Optimization Strategy Based on High-Relevance Mode Access Data Compression
AU - Gao, Rui
AU - Lu, Yixuan
AU - Liu, Jian
AU - Yu, Jun
AU - Tian, Weiguo
AU - Du, Haiwen
AU - Kang, Chuanmeng
AU - Yin, Weiqi
AU - Zhu, Dongjie
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - With the improvement of social informatization and the popularization of Internet of Things devices, the scale, complexity and diversity of data are currently growing rapidly, and traditional storage solutions have been unable to meet the complex and diverse applications and large-scale new storage requirements. Existing storage solutions still have deficiencies in data compression and adapting to the diversity of system architectures, resulting in a large waste of storage space resources, which in turn increases the total cost of ownership of platform data. Therefore, this paper will study the data compression strategy of database file storage, and propose a high-relevance mode access data compression method. The data request of the write-only instance of the database hosted on the cloud platform is aggregated with the system workload. The data stored in the write-only instance is compressed, which improves data storage efficiency and storage space utilization. The method was validated using data in real enterprise scenarios. The experimental results show that the proposed method has a certain degree of improvement in storage space utilization compared with the original method.
AB - With the improvement of social informatization and the popularization of Internet of Things devices, the scale, complexity and diversity of data are currently growing rapidly, and traditional storage solutions have been unable to meet the complex and diverse applications and large-scale new storage requirements. Existing storage solutions still have deficiencies in data compression and adapting to the diversity of system architectures, resulting in a large waste of storage space resources, which in turn increases the total cost of ownership of platform data. Therefore, this paper will study the data compression strategy of database file storage, and propose a high-relevance mode access data compression method. The data request of the write-only instance of the database hosted on the cloud platform is aggregated with the system workload. The data stored in the write-only instance is compressed, which improves data storage efficiency and storage space utilization. The method was validated using data in real enterprise scenarios. The experimental results show that the proposed method has a certain degree of improvement in storage space utilization compared with the original method.
KW - Data compression
KW - Storage space optimization
UR - https://www.scopus.com/pages/publications/85135060242
U2 - 10.1007/978-3-031-06761-7_29
DO - 10.1007/978-3-031-06761-7_29
M3 - 会议稿件
AN - SCOPUS:85135060242
SN - 9783031067600
T3 - Communications in Computer and Information Science
SP - 353
EP - 363
BT - Advances in Artificial Intelligence and Security - 8th International Conference on Artificial Intelligence and Security, ICAIS 2022, Proceedings
A2 - Sun, Xingming
A2 - Zhang, Xiaorui
A2 - Xia, Zhihua
A2 - Bertino, Elisa
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th International Conference on Artificial Intelligence and Security , ICAIS 2022
Y2 - 15 July 2022 through 20 July 2022
ER -