Article: Design of AI-enhanced hybrid storage engine for multimodal data management Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.28 pp.67 - 83 Abstract: To enhance the management effect of multimodal data and increase the data access speed, this paper first uses dynamic random access memory (DRAM) to complete the caching of non-volatile memory (NVM) in the hybrid storage module. When there is a cache missing in DRAM, a high-speed acquisition card is used to collect historical access records of NVM. After encoding historical access records into access vectors, they are used as the input of the deep learning model. The spatio-temporal attention mechanism is introduced to enhance access coding features and improve the prediction accuracy of the access frequency. The multimodal data with prediction results higher than the set threshold are read into the hybrid storage module for storage. Experimental outcome implies that the average performance of the offered approach in sequential reading is at least 1.1 times that of the benchmark approach, significantly improving the access speed. Inderscience Publishers - linking academia, business and industry through research

Title: Design of AI-enhanced hybrid storage engine for multimodal data management

Authors: Pinjie Liu; Jing Li

Addresses: School of Computer Engineering, Guangzhou Huali College, Guangzhou 511325, China ' School of Computer Engineering, Guangzhou Huali College, Guangzhou 511325, China

Abstract: To enhance the management effect of multimodal data and increase the data access speed, this paper first uses dynamic random access memory (DRAM) to complete the caching of non-volatile memory (NVM) in the hybrid storage module. When there is a cache missing in DRAM, a high-speed acquisition card is used to collect historical access records of NVM. After encoding historical access records into access vectors, they are used as the input of the deep learning model. The spatio-temporal attention mechanism is introduced to enhance access coding features and improve the prediction accuracy of the access frequency. The multimodal data with prediction results higher than the set threshold are read into the hybrid storage module for storage. Experimental outcome implies that the average performance of the offered approach in sequential reading is at least 1.1 times that of the benchmark approach, significantly improving the access speed.

Keywords: multimodal data management; hybrid storage engine; non-volatile memory; NVM; deep learning; attention mechanism.

DOI: 10.1504/IJICT.2025.147711

International Journal of Information and Communication Technology, 2025 Vol.26 No.28, pp.67 - 83

Received: 23 May 2025
Accepted: 08 Jun 2025
Published online: 25 Jul 2025 *

Title: Design of AI-enhanced hybrid storage engine for multimodal data management

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