一种超大数据库优化图像索引生成的新方法

B. Jitkar, S. Raut
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引用次数: 0

摘要

随着多媒体技术、计算能力和NAS等存储设备的不断快速发展,通过许多学术、商业和社交网站,图像数据库的规模日益增加。因此,快速、高效的图像检索成为图像数据库研究的热点。像索引和哈希表这样的机制可以用来解决这些问题。主要的挑战是找到一种适当的索引技术来提高查询性能。本文研究了一种基于B+树的高效索引技术的设计与开发方法。通过对节点应用AES加密算法来保持隐私。采用遗传算法生成优化后的B+树。结果分析表明,基于节点数、内存、搜索时间、插入时间等不同的分析参数,本文方法生成的优化B+树的结果优于B+树。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Novel Approach for Generation of Optimized Image Index for Very Large Database
The rapid and continuous growth in the multimedia technology, computational power and the availability of storage devices like NAS, the size of image databases increases day by day over the Internet through many academic, commercial and social websites. So, these image databases attracted the attention of the researchers for fast and efficient image retrieval. The mechanisms like indexing and hash table can be used to solve these problems. The main challenge is to find a proper index technique which improves query performance. This paper focuses on the novel approach for design and development of efficient indexing technique based on B+ tree. The privacy has been maintained by applying the AES encryption algorithm for the nodes. Genetic algorithm has been applied to generate the optimized B+ tree. The result analysis shows that the optimized B+ tree generated by our novel approach gives better result than the B+ tree based on different analysis parameters like number of nodes, memory, search time, insertion time etc.
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