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.
同样，由于SQL使用集合级别的处理以及DB 2优化查询来确定数据导航逻辑，所以这是可能的。
Again, this is possible because SQL USES set-level processing and DB2 optimizes the query to determine the data-navigation logic.
DB 2 9.7中的查询优化技术能够提高针对XML数据的关系视图的查询处理效率。
Query optimization technology in DB2 9.7 provides increased efficiency for processing queries against relational views of XML data.
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.
当应用程序向设备的主机服务器发送一个查询时，处理流程开始。 主机服务器将编译该查询并创建一个优化的查询执行计划。
The action begins when an application sends a query to the appliance host server, which compiles the query and creates an optimized query execution plan.
关键字'ANY '：该设置表明MQT将被优化器考虑来处理查询。
Keyword 'ANY' : This setting indicates that MQTs will be considered by the optimizer to process queries.
在处理数据库时，查询优化至关重要，而UPDATESTATISTICS正是用于此目的。
Query optimization is crucial in dealing with databases, and UPDATE STATISTICS does just that.
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.
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.
如您所见，优化器创建了一个虚拟表来处理in列表子查询。
As you can see, the optimizer creates a virtual table to process the IN-list sub-query.
从这张访问计划图中，您可以看到优化器对如何处理查询作出何种选择以及理由。
From the access plan graph, you can see what choices the optimizer has made regarding how the query will be processed and the rationale for those choices.
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.
DB 2 9.7中新的优化技术让DB2能够自动地使用底层xml列上定义的xml索引officeidx来处理这个查询。
New optimization technology in DB2 9.7 enables DB2 to automatically use the XML index officeIdx defined on the underlying XML column to process this query.
分布式数据查询是分布式数据库管理系统的核心，而查询优化算法又是查询处理中的关键技术。
The distributed data query is the core of the distributed database managing system, but the optimized query algorithm is the key technology in query processing.
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.
内部查询处理器错误：在查询优化过程中，查询处理器用尽了堆栈空间。
Internal query processor Error: The query processor ran out of stack space during query optimization.
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.
如何高效地处理说明性查询语言中嵌入的用户自定义函数，是查询优化的一个重要内容。
How to process user defined functions incorporated in declarative query languages efficiently is an important aspect of query optimization.
本文描述一个分布式关系数据库系统中具有语义优化的查询处理算法。
In this paper, we describe an algorithm, for query processing with semantic optimization in distributed relational database systems.
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.
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.
移动数据库主要具有以下几种关键技术：移动事务处理技术、移动查询优化技术、复制与缓存技术。
Mobile database is mainly composed of some pacing technologies: mobile transaction processing technology, mobile query optimization technology, copy and cache technology.
然后，分析了在查询处理中可能的优化。
分布式数据查询是分布式数据库管理系统的核心，而查询优化算法又是查询处理中的关键技术。
The distribute data query is the core of the distribute database manages system, but the optimized query algorithm is the key technology in query the processed.
介绍了查询处理的步骤，给出了查询分解的具体方法，并对查询的优化问题进行研究分析。
The paper introduces the steps of query process, gives the particular way of the query decomposition, and also analyses the query optimize method.
提出了一种基于包含与归并的移动查询优化算法，该算法可以优化查询处理，减少查询工作量。
We designed a query optimizing algorithm- A Mobile Query Optimization Algorithm based on Containment and Merging, which can reduce query workload.
数据流管理系统提出了一种通用结构模型，它采用窗口机制，连续查询以及相应的优化策略迅速高效地对实时数据进行在线分析处理。
DSMS is a common model, which adopts window mechanics, continuous query and many optimizing strategy to online analyzing and disposing real-time data quickly and efficiently.
数据流管理系统提出了一种通用结构模型，它采用窗口机制，连续查询以及相应的优化策略迅速高效地对实时数据进行在线分析处理。
DSMS is a common model, which adopts window mechanics, continuous query and many optimizing strategy to online analyzing and disposing real-time data quickly and efficiently.
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