摘 要：随着信息技术的快速发展，数据库规模持续扩大，数据冗余和存储效率问题日益凸显，这对数据管理与处理提出了更高要求。本研究旨在探索有效的数据冗余消除与压缩技术，以提升数据库的存储效率和查询性能。通过分析现有数据冗余消除方法的不足，提出了一种基于内容感知的数据去重算法，并结合先进的无损压缩技术优化存储结构。该算法利用哈希索引与指纹匹配相结合的方式快速识别重复数据块，同时引入自适应压缩策略以平衡压缩比与计算开销。实验结果表明，所提方法在大规模数据库场景下能够显著降低存储空间需求，平均压缩比提升约25%，且对查询响应时间的影响较小。此外，本研究还设计了多级缓存机制以进一步优化读写性能。总体而言，本研究不仅为解决数据库中的数据冗余问题提供了新思路，还为高效数据管理技术的发展奠定了理论基础，具有重要的实际应用价值。
关键词：数据冗余消除；内容感知算法；无损压缩技术
Abstract：With the rapid development of information technology, the scale of databases continues to expand, and issues related to data redundancy and storage efficiency have become increasingly prominent, posing higher requirements for data management and processing. This study aims to explore effective techniques for data deduplication and compression to enhance the storage efficiency and query performance of databases. By analyzing the limitations of existing data deduplication methods, a content-aware data deduplication algorithm is proposed, which is further optimized by integrating advanced lossless compression technologies to improve storage structures. The algorithm employs a combination of hash indexing and fingerprint matching to rapidly identify duplicate data blocks while introducing an adaptive compression strategy to balance the compression ratio and computational overhead. Experimental results demonstrate that the proposed method significantly reduces storage space requirements in large-scale database scenarios, with an average improvement in compression ratio of approximately 25%, and has minimal impact on query response time. Additionally, this research designs a multi-level caching mechanism to further optimize read/write performance. Overall, this study not only provides new insights into addressing data redundancy issues in databases but also lays a theoretical foundation for the development of efficient data management technologies, showcasing significant practical application value.
引言 1
一、数据冗余消除技术概述 1
(一)数据冗余的定义与分类 1
(二)冗余消除的意义与挑战 2
(三)当前冗余消除技术综述 2
二、数据压缩技术原理与方法 3
(一)数据压缩的基本原理 3
(二)无损压缩技术研究 3
(三)有损压缩技术及其适用场景 4
三、冗余消除与压缩技术的协同优化 4
(一)冗余消除与压缩的关系分析 4
(二)协同优化的技术路径探讨 5
(三)实际应用中的性能评估 5
四、数据库环境下的技术实现与案例分析 5
(一)数据库中冗余消除的实现策略 6
(二)数据压缩在数据库中的应用实践 6
(三)典型案例与效果评价 6
结论 7
参考文献 8
致谢 8
关键词：数据冗余消除；内容感知算法；无损压缩技术
Abstract：With the rapid development of information technology, the scale of databases continues to expand, and issues related to data redundancy and storage efficiency have become increasingly prominent, posing higher requirements for data management and processing. This study aims to explore effective techniques for data deduplication and compression to enhance the storage efficiency and query performance of databases. By analyzing the limitations of existing data deduplication methods, a content-aware data deduplication algorithm is proposed, which is further optimized by integrating advanced lossless compression technologies to improve storage structures. The algorithm employs a combination of hash indexing and fingerprint matching to rapidly identify duplicate data blocks while introducing an adaptive compression strategy to balance the compression ratio and computational overhead. Experimental results demonstrate that the proposed method significantly reduces storage space requirements in large-scale database scenarios, with an average improvement in compression ratio of approximately 25%, and has minimal impact on query response time. Additionally, this research designs a multi-level caching mechanism to further optimize read/write performance. Overall, this study not only provides new insights into addressing data redundancy issues in databases but also lays a theoretical foundation for the development of efficient data management technologies, showcasing significant practical application value.
Keywords: Data Redundancy Elimination；Content-Aware Algorithm；Lossless Compression Technology
引言 1
一、数据冗余消除技术概述 1
(一)数据冗余的定义与分类 1
(二)冗余消除的意义与挑战 2
(三)当前冗余消除技术综述 2
二、数据压缩技术原理与方法 3
(一)数据压缩的基本原理 3
(二)无损压缩技术研究 3
(三)有损压缩技术及其适用场景 4
三、冗余消除与压缩技术的协同优化 4
(一)冗余消除与压缩的关系分析 4
(二)协同优化的技术路径探讨 5
(三)实际应用中的性能评估 5
四、数据库环境下的技术实现与案例分析 5
(一)数据库中冗余消除的实现策略 6
(二)数据压缩在数据库中的应用实践 6
(三)典型案例与效果评价 6
结论 7
参考文献 8
致谢 8
