查询分解主要侧重于FLWR表达式。
另外，查询分解的XML数据很困难，常常需要多表联结。
Furthermore, querying decomposed XML data can be challenging, often requiring multi-table joins.
并介绍了主要的本体结构和查询分解策略的实现情况。
Then a brief introduction on the main structure of Ontology and the achievement of decomposition algorithm is given by this dissertation.
DB 2II负责把该查询分解成能被每个目标数据源处理的几块。
DB2 II took care of decomposing this query into pieces that could be processed by each of the target data sources.
查询处理包括全局查询语句解析、查询变量绑定、查询分解和查询重写。
Query processing contains parsing of global query, binding of query variable, decomposition of query and rewriting of query.
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.
本文提出了利用关联矩阵进行查询分解优化的具体过程和算法。这种方法极利于在计算机上实现。
The actual procedure and algorithm for query decomposition optimisation with association matrix is proposed, and is convenient to be performed on a computer.
考虑到查询分解及查询优化在异构数据集成系统中的重要意义，主要就这一方面进行了深入研究。
In view of the significance of query decomposition and optimization in the Heterogeneous Data Resource Integration System, in-depth research on this aspect was mainly conducted.
但是，将一个比较复杂的查询分解成多个查询通常很有好处，因为这样产生的代码更加模块化，更易于维护。
However, it often is useful to break up a more complicated query into multiple queries because the resulting code is more modular and easier to maintain.
另外，分解的XML数据的特定部分的运行时查询和更新性能通常是可预知的。
In addition, runtime query and update performance of specific portions of the decomposed XML data is generally predictable.
查询组合或者分解的元素返回结构不同的结果。
Querying composed or decomposed elements returns results with different structures.
如果已经选择了分解，那么接着选择一种最适合查询和报告需求的目标关系模式。
If you have decided to shred, pick a relational target schema that best fits the query and reporting requirements.
在分解性能、查询性能以及报告和分析的容易程度等方面，每种方式都有不同的优点和缺点。
Each has different advantages and disadvantages, related to shredding performance, query performance, and the ease of reporting and analytics.
由于不再需要为了进行查询而分解数据，一些表的数据库模式已经简化了。
The database schema for some of the tables has been simplified as you no longer need to shred out data for querying purposes.
当您对ApacheDerby处理查询时执行的过程进行分解时，该顺序十分直观。
When you break down the process Apache Derby follows when processing a query, this order is intuitive.
作为分解的备选方法，可以简单地使用一个包含xml列的表来存储、索引和查询MIML数据。
As an alternative to shredding, you can simply use a table with a column of type XML to store, index, and query the MIML data.
无需将XML(分解)转换为关系格式，即可轻松存储、索引和查询XML。
To store, index, and query XML easily without needing to convert the XML (shred) to relational format.
如果需要执行高度复杂的查询和非常多的分析处理，在分解的数据上执行纯s QL可以提供更好的性能。
If you need to perform highly complex queries and very heavy analytical processing, plain SQL over shredded data can provide better performance.
如果XML数据被分解到关系表，那么关系查询结果必须被转换回xML。
If the XML data is shredded to relational tables, relational query results must be converted back to XML.
如果分解后的结构名称保持不变，那么查询的惟一区别在于新模式的查询结果是结构化的。
If the name of the structure that is decomposed remains the same, then the only difference in the queries is that the results from the new schema are structured.
对于复杂度较高的查询，被分解的数据上的纯s QL查询可能更易于编写，并且能提供较好性能。
For highly complex queries, plain SQL over shredded data may be easier to write and can provide better performance.
在路径分解和查询计划选择的过程中，利用查询树中的目标节点来避免中间结果的传递。
In the procedure of path decomposition and query plan selection, target node in the query tree is utilized to avoid the transfer of the intermediate results.
全局查询的分解与优化是异构数据库的集成中许多难题之一。
Decomposition and optimization of global query is one of hard problems in heterogeneous database integration.
文章提出了一种基于快速分解模拟退火算法的全局查询优化算法。
This paper proposes a global query optimization algorithm based on a faster decomposition-based simulated annealing algorithm.
利用Z曲线聚类和降维特性，本文给出网格划分方法、搜索区域分解过程，提出一种高维空间范围查询算法。
Based on Z curve, the paper presents a method of grid partition, a procedure of partitioning search region, and a high-dimensional spatial range query algorithm.
在多库系统中，对全局视图的存取操作要分解为针对多个LDB的子操作，可能还要对数据模式及查询语言进行转换。
In multidatabase, access to global view will be decomposed to several sub-operations to several LDB, and perhaps also need data schema translation and query processing.
本文对数据库的查询技术进行分析，给出两种优化方法：分解查询和选择最优存取路径。
In this paper, first the query technology on database is analysed, then two optimization methods are offered: the disintegration query and the best access path selection.
本文对数据库的查询技术进行分析，给出两种优化方法：分解查询和选择最优存取路径。
In this paper, first the query technology on database is analysed, then two optimization methods are offered: the disintegration query and the best access path selection.
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