DB 2优化程序使用这些特征和限制来确定处理一个查询的最佳方法。
The DB2 optimizer USES these characteristics and restrictions when determining the best way to process a query.
为了解决那个问题，OODT提供了对分布式资源的透明访问、数据恢复和查询优化功能以及分布式处理和虚拟存档。
To solve that problem, OODT provides transparent access to distributed resources, functionality for data discovery and query optimization, as well as distributed processing and virtual archives.
用于处理具有改进查询执行技术的查询复杂性、数据量和及时期望的查询优化。
Query optimizations to address query complexity, data volumes, and timeliness expectations with improved query execution techniques.
处理程序api的使用完全集成在捕捉和查询组件中，并通过优化数据库存储和查询，显著改进系统的性能。
The use of the handler API is completely integrated into the capture and query component, and it greatly improves the performance of a system by optimizing database storage and retrieval.
Informix可以在用户无法察觉(除了因为响应时间较慢而通知用户)的情况下优化和处理IWA无法优化的任何查询。
Any query that cannot be optimized by IWA is optimized and handled by Informix without users even knowing it, except that they will notice the slower response time.
当然，如果您选择编写同时包括XQuery和SQL表达式的“双语(bilingual)”查询，那么DB 2同样会处理和优化这些查询。
Of course, if you choose to write bilingual queries that include both XQuery and SQL expressions, DB2 will process and optimize these queries, too.
尽管在分布式处理中也使用某些集中式查询处理中的技术和方法，但就其问题的规模和优化的因素，都与集中式查询处理有质的不同。
Although some technique and ways to query in centralized database system can also be used in distributed one, there are basic differences between them in terms of scale and factors of databases.
本文主要对XML数据库的查询语言、XML数据路径表达式查询的优化技术和XML数据的查询处理技术进行了重点研究。
This paper focuses on the query language of XML data, the technologies of path expression optimizing and the query processing of XML data.
查询优化是查询处理研究的一个主要问题，为此着重分析研究了查询优化所涉及的代价估计、查询规划和搜索空间等相关问题。
Query optimization is the main problem of query processing research, thus in particular discusses cost estimated, query plan, search Spaces and so on related problems.
在分布式查询优化中，应同时考虑本地处理代价和传输代价。
In the distributed query optimization, both local processing cost and transmission cost should be thought over simultaneously.
查询优化和处理是并行数据库管理系统的关键组成部分。
Query optimization and processing is a critical component of parallel database systems.
类似于关系系统RTQP提供了在MMDB环境下节省内存的查询处理的实现算法，以及遗传算法和实时数据库规则相结合的查询优化方案。
Similar to relational systems, RTQP provides an algorithm for saving main memory space under the MMDB, and a query optimization integrated the rules in RTDBs and the GAs.
这些算法和相应的查询优化处理器已用于作者自行设计的并行数据库管理系统原型。
A complete set of a algorithms and techniques are proposed. the algorithms and techniques have been used in a prototype parallel database system designed by the author.
本文利用数据分片和并行处理策略，提出一种采用直接连接的查询优化算法，能有效地缩减查询处理的响应时间。
This paper presents an optimized query algorithm, which adopts direct-join using data partition and parallel processing. It can reduce the response time of query process.
实践证明，这些技术和方法不仅能够快速有效地实现并行数据库管理系统，也能够有效地进行并行数据库查询的优化和处理。
It is shown by the prototype system that the proposed algorithms and techniques are very efficient and effective for query optimization and...
实践证明，这些技术和方法不仅能够快速有效地实现并行数据库管理系统，也能够有效地进行并行数据库查询的优化和处理。
It is shown by the prototype system that the proposed algorithms and techniques are very efficient and effective for query optimization and...
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