摘 要
随着信息技术的迅猛发展，多模态数据在各个领域中日益增多，如医疗影像与文本报告、社交网络中的图像和评论等，传统的单一模态数据库难以满足对多模态数据高效处理的需求。为此，本文旨在设计并实现一种适用于多模态数据融合的数据库架构。该研究基于现有数据库理论和技术，提出了一种新型架构模型，通过引入统一的数据表示方法，将不同类型的数据映射到同一特征空间，实现了异构数据的有效整合。采用分布式存储技术确保数据的高可用性和扩展性，并结合机器学习算法优化查询性能。实验结果表明，在大规模多模态数据集上，所提架构能够显著提高数据检索效率，降低响应时间，相较于传统方案具有明显优势。此外，本研究创新性地提出了跨模态关联规则挖掘机制，增强了不同模态间语义关联的理解能力，为后续智能分析提供了坚实基础。这一成果不仅丰富了数据库领域的理论体系，也为实际应用场景下的多模态数据分析提供了有效工具，推动了相关技术的发展进程。
关键词：多模态数据融合；分布式存储；跨模态关联规则挖掘
Abstract
With the rapid development of information technology, multimodal data are increasingly prevalent across various domains, such as medical images and text reports, images and comments in social networks, etc. Traditional unimodal databases struggle to meet the demand for efficient processing of multimodal data. This paper aims to design and implement a database architecture suitable for multimodal data fusion. Based on existing database theories and technologies, this study proposes a novel architectural model that introduces a unified data representation method to map different types of data into the same feature space, thereby achieving effective integration of heterogeneous data. Distributed storage technology is employed to ensure high availability and scalability of the data, while machine learning algorithms are integrated to optimize query performance. Experimental results demonstrate that the proposed architecture significantly improves data retrieval efficiency and reduces response time on large-scale multimodal datasets, showing clear advantages over traditional solutions. Additionally, this research innovatively proposes a cross-modal association rule mining mechanism, enhancing the understanding of semantic relationships between different modalities and providing a solid foundation for subsequent intelligent analysis. This achievement not only enriches the theoretical fr amework of the database field but also offers an effective tool for multimodal data analysis in practical application scenarios, advancing the development of related technologies.
Keywords：Multimodal Data Fusion；Distributed Storage；Cross-modal Association Rule Mining
目 录
引 言 1
第一章 多模态数据融合需求分析 2
1.1 多模态数据特征解析 2
1.2 数据融合应用场景 2
1.3 融合目标与挑战 3
第二章 数据库架构设计原则 5
2.1 架构设计目标确立 5
2.2 关键技术选型依据 5
2.3 系统性能要求分析 6
第三章 数据存储与管理方案 8
3.1 存储结构规划 8
3.2 数据一致性保障 8
3.3 高效索引机制构建 9
第四章 数据融合实现方法 11
4.1 数据预处理流程 11
4.2 融合算法选择与优化 11
4.3 结果验证与评估体系 12
结 论 14
参考文献 15
致 谢 16
随着信息技术的迅猛发展，多模态数据在各个领域中日益增多，如医疗影像与文本报告、社交网络中的图像和评论等，传统的单一模态数据库难以满足对多模态数据高效处理的需求。为此，本文旨在设计并实现一种适用于多模态数据融合的数据库架构。该研究基于现有数据库理论和技术，提出了一种新型架构模型，通过引入统一的数据表示方法，将不同类型的数据映射到同一特征空间，实现了异构数据的有效整合。采用分布式存储技术确保数据的高可用性和扩展性，并结合机器学习算法优化查询性能。实验结果表明，在大规模多模态数据集上，所提架构能够显著提高数据检索效率，降低响应时间，相较于传统方案具有明显优势。此外，本研究创新性地提出了跨模态关联规则挖掘机制，增强了不同模态间语义关联的理解能力，为后续智能分析提供了坚实基础。这一成果不仅丰富了数据库领域的理论体系，也为实际应用场景下的多模态数据分析提供了有效工具，推动了相关技术的发展进程。
关键词：多模态数据融合；分布式存储；跨模态关联规则挖掘
Abstract
With the rapid development of information technology, multimodal data are increasingly prevalent across various domains, such as medical images and text reports, images and comments in social networks, etc. Traditional unimodal databases struggle to meet the demand for efficient processing of multimodal data. This paper aims to design and implement a database architecture suitable for multimodal data fusion. Based on existing database theories and technologies, this study proposes a novel architectural model that introduces a unified data representation method to map different types of data into the same feature space, thereby achieving effective integration of heterogeneous data. Distributed storage technology is employed to ensure high availability and scalability of the data, while machine learning algorithms are integrated to optimize query performance. Experimental results demonstrate that the proposed architecture significantly improves data retrieval efficiency and reduces response time on large-scale multimodal datasets, showing clear advantages over traditional solutions. Additionally, this research innovatively proposes a cross-modal association rule mining mechanism, enhancing the understanding of semantic relationships between different modalities and providing a solid foundation for subsequent intelligent analysis. This achievement not only enriches the theoretical fr amework of the database field but also offers an effective tool for multimodal data analysis in practical application scenarios, advancing the development of related technologies.
Keywords：Multimodal Data Fusion；Distributed Storage；Cross-modal Association Rule Mining
目 录
引 言 1
第一章 多模态数据融合需求分析 2
1.1 多模态数据特征解析 2
1.2 数据融合应用场景 2
1.3 融合目标与挑战 3
第二章 数据库架构设计原则 5
2.1 架构设计目标确立 5
2.2 关键技术选型依据 5
2.3 系统性能要求分析 6
第三章 数据存储与管理方案 8
3.1 存储结构规划 8
3.2 数据一致性保障 8
3.3 高效索引机制构建 9
第四章 数据融合实现方法 11
4.1 数据预处理流程 11
4.2 融合算法选择与优化 11
4.3 结果验证与评估体系 12
结 论 14
参考文献 15
致 谢 16
