同时给出了大容量数据库的数据一致性和安全性解决方案。
The solution to the safety and consistency of data in the giant database is also presented in this paper.
同时，他指出程序员学习并行处理还要知道如何实现与大容量数据库的快速接口。
He added that the new capabilities would also mean that programmers will have to learn how to deal with nearly instant access to large volumes of data.
并行信息检索研究领域的开辟，使快速查询检索系统中的大容量数据库和有效缩短检索响应时间成为可能。
Owing to the achievements in parallel information retrieval research, it is possible to query quickly the large-capacity database in retrieval system and to cut short the response time.
查询优化集中于cpu密集型(CPUbound)执行路径，而全缓存数据库将仍然集中于优化取页到大容量存储器的操作，而这已不再是问题。
Query optimization focuses on CPUbound execution paths, while a fully cached database will still be preoccupied with optimizing page fetches to mass storage that are no longer an issue.
在操作系统级，使用大容量磁盘阵列，通过磁盘映像技术使每一个数据库文件自动分布于每个物理磁盘。
In operating system class, array of disk of use high capacity, make through disk image technology each database file distributings automatically at every physics disk.
该系统对于视频序列和大容量的人脸数据库能够实时进行人脸图像检索。
To the given video sequence, the system can retrieve face image from big face image database on time.
现有的数据库索引技术，普遍不能适应多维空间属性的搜索，特别是无法对大容量的多媒体数据进行基于内容的检索。
The existing indexing technology of databases doesn t adapt to the retrieval of multi-dimensional space, especially the content-based retrieval on large multimedia database.
因此需要一种以计算机为操作管理平台并具有大容量存储功能的数据库为核心的教学评价与分析系统作为依托。
Therefore need a management platform for the operation of computer and mass storage features with a database at the core of the teaching evaluation and analysis system as a support.
大容量多媒体数据库的基于内容相似性的检索本质上是高维特征空间中一定距离函数的K近邻问题。
Searches based on content similarities in large multimedia libraries are essentially K nearest neighbor searches in high dimensional Spaces.
采用按值分支树的多维综合流分类算法支持前缀和范围匹配，可扩展性强，适合大容量规则数据库。
The compositive multi-dimensional packet classification algorithm based on tree divided by value is scalable. It can deal with prefixes match and range match for large rule sets.
采用按值分支树的多维综合流分类算法支持前缀和范围匹配，可扩展性强，适合大容量规则数据库。
The compositive multi-dimensional packet classification algorithm based on tree divided by value is scalable. It can deal with prefixes match and range match for large rule sets.
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