ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2021, Vol. 58 ›› Issue (2): 338-355.doi: 10.7544/issn1000-1239.2021.20200388

Special Issue: 2021大数据时代的存储系统与智能存储技术专题

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Content Sifting Storage Mechanism for Cross-Modal Image and Text Data Based on Semantic Similarity

Liu Yu1, Guo Chan1, Feng Shuyao1, Zhou Ke1, Xiao Zhili2   

  1. 1(Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan 430074);2(Technology and Engineering Group, Tencent Inc., Shenzhen, Guangdong 518054)
  • Online:2021-02-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China for Young Scientists (61902135) and the Innovation Group Project of the National Natural Science Foundation of China (61821003).

Abstract: With the explosive growth of multimedia data, the data in cloud becomes heterogeneous and large. The conventional storage systems served for data analysis face the challenge of long read latency due to the lack of semantic management of data. To solve this problem, a cross-modal image and text content sifting storage(CITCSS) mechanism is proposed, which saves the read bandwidth by only reading relevant data. The mechanism consists of the off-line and on-line stages. In the off-line stage, the system first uses the self-supervised adversarial Hash learning algorithm to learn and map the stored data to similar Hash codes. Then, these Hash codes are connected by Hamming distances and managed by the metadata style. In the implement, we use Neo4j to construct the semantic Hash code graph. Furthermore, we insert storage paths into the property of node to accelerate reading. In the on-line stage, our mechanism first maps the image or text represented the analysis requirement into Hash codes and sends them to the semantic Hash code graph. Then, the relevant data will be found by the sifting radius on the graph, and returned to the user finally. Benefiting from our mechanism, storage systems can perceive and manage semantic information resulting in advance service for analysis. Experimental results on public cross-modal datasets show that CITCSS can greatly reduce the read latency by 99.07% to 99.77% with more than 98% recall rate compared with conventional semantic storage systems.

Key words: semantic management, Hash code metadata, metadata graph, storage mechanism, read bandwidth

CLC Number: