| 摘要: |
| 云边端协同架构中数据类型多样,各级存储资源与计算资源存在差异,给数据管理带来新的挑战.现有数据模型或者数据模型的简单叠加,都难以同时满足云边端中多模态数据管理和协同管理需求.因此,研究面向云边端协同的多模态数据建模技术成为重要问题.其核心在于,如何高效地从云边端三层架构中得到满足应用所需的查询结果.从云边端三层数据的数据类型出发,提出了面向云边端协同的多模态数据建模技术,给出了基于元组的多模态数据模型定义,设计了6种基类,解决多模态数据统一表征困难的问题;提出了云边端协同查询的基本数据操作体系,以满足云边端业务场景的查询需求;给出了多模态数据模型的完整性约束,为查询优化奠定了理论基础.最后,给出了面向云边端协同多模态数据模型的示范应用,并从数据存储时间、存储空间和查询时间这3个方面对所提出的数据模型存储方法进行了验证.实验结果表明,所提方案能够有效地表示云边端协同架构中的多模态数据. |
| 关键词: 多模态数据模型 云边端协同 查询处理 |
| DOI：10.13328/j.cnki.jos.007070 |
| 分类号: |
| 基金项目:国家自然科学基金(62232005,U1866602);国家重点研发计划(2021YFB3300502) |
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| Multimodal Data Modeling Technology and Its application for Cloud-edge-device Collaboration |
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CUI Shuang-Shuang, WU Xian, WANG Hong-Zhi, WU Hao
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Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
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| Abstract: |
| In the cloud-edge-device collaborative architecture, data types are diverse, and there are differences in storage resources and computing resources at all levels, which bring new challenges to data management. The existing data models or simple superposition of data models are difficult to meet the requirements of multimodal data management and collaborative management in the cloud-edge-device. Therefore, research on multimodal data modeling technology for cloud-edge-device collaboration has become an important issue. The core is how to efficiently obtain the query results that meet the needs of the application from the three-tier architecture of cloud-edge-device. Starting from the data types of the three-layer data of cloud-edge-device, this study proposes a multimodal data modeling technology for cloud-edge-device collaboration, gives the definition of multimodal data model based on tuples, and designs six base classes to solve the problem of unified representation of multimodal data. The basic data operation architecture of cloud-edge-device collaborative query is also proposed to meet the query requirements of cloud-edge-device business scenarios. The integrity constraints of the multimodal data model are given, which lays a theoretical foundation for query optimization. Finally, a demonstration application of the multimodal data model for cloud edge-device collaboration was given, and the proposed data model storage method was verified from three aspects of data storage time, storage space and query time. The experimental results show that the proposed scheme can effectively represent the multimodal data in the cloud-edge-device collaborative architecture. |
| Key words: multimodal data model cloud-edge-device collaboration query processing |
