然后，该算法被扩展来处理特殊的二分图查询。
Then, the algorithm is extended to process subgraph queries in form of bipartite graphs.
根据地块识别和位置判断算法以及处方图查询算法，编制计算输出程序。
Design program of calculating and output, According to the algorithm of recognizing plot, estimating location and inquiring prescription.
实验结果证明，该方法能准确地产生候选图集，从而提高图查询的效率。
Experimental results show that this algorithm can accurately generate candidate and improve the efficiency of the graph query.
图3显示的是高级查询统计的基本实现架构。
Figure 3 shows the basic implementation architecture of advanced query statistics.
这个已重写的查询现在如图10 所示。
图3演示了该查询是如何工作的。
图1显示的是高级查询统计配置界面。
Figure 1 displays the advanced query statistics configuration screen.
下面的图阐释了当禁用异步时对查询的处理。
Processing of the query when asynchrony is disabled is illustrated by the figure below.
图13显示了此查询的输出。
图7显示查询响应时间作为表中文档数(范围从1,000到100,000)的函数(不使用索引或索引表)。
Figure 7 shows the query response times as a function of the number of documents in the table, ranging from 1,000 to 100,000 documents (when no indexes or side tables are used).
已经对表中所有行的缺省查询填写了内容(图14)。
A default query for all rows in the table will already be filled in (Figure 14).
对于本文的查询样例，图12显示了索引报告，它描述了引用表的现有索引。
For the sample query in this article, Figure 12 shows the index report, which describes the existing indexes of the referenced tables.
在图13中看到，查询执行了两个排序，花费的时间少于12秒。
In Figure 13, you see that the query performed two sorts and spent less than 12 seconds to complete.
在清单10中，有两个FOAF图被传递给查询。
In Listing 10, two named FOAF graphs are passed to the query.
除了后台图，SPARQL还能查询任意数量的命名图。
In addition to the background graph, SPARQL can query any number of named graphs.
图12确认查询的访问计划中使用了staff表上的新索引。
Figure 12 confirms that the new index on the STAFF table is being used in the query access plan.
图12显示了此查询的输出。
Figure 12 illustrates what the output of this query looks like.
CONSTRUCT非常重要，可以根据SPARQL查询结果构造rdf图。
Construct is quite important because it allows you to construct RDF graphs from the results of the SPARQL query.
图8显示了该查询的结果。
那么解析器将根据图2在SQL查询的遗漏部分中发出文本。
Then the parser should emit text into the missing portion of the SQL query according to Figure 2.
图8 . db 2 batch-查询执行。
点击Next，这样您可以在DepartmentManagerbean中看到所有可用的查询方法(图23)。
Click Next so that you can see all of the available query methods in the DepartmentManager bean (Figure 23).
图11 . db 2 batch-查询执行的改进。
图16显示了此新查询生成的CommandEditor。
Figure 16 shows the Command Editor that results from this new query.
CriteriaAPI支持使用基于对象的查询图以编程方式构建查询。
The Criteria API enables a programmatic construction of queries using an object-based query graph.
图4展示了哪些查询从这些建议中获益最多。
Figure 4 shows which queries benefited most from the recommendations.
值得注意的是，这样的设置会影响到查询相关元素的所有操作，例如浏览图和主题图的查询。
Note that this setting will affect all operations that query for related elements, for instance queries on Browse diagrams and Topic diagrams.
时间折线图：该图显示了查询数和用户数的变化，以及平均和最差响应时间。
Time line chart: This chart shows the changes in number of queries and users along with the average and worst response time.
其中包含如何通过使用查询格式化、注释、报告和访问计划图来更好地理解问题查询。
It includes how to understand the problem query better by using the visual AIDS provided by query formatting, annotation, reports, and the access plan graph.
使用命名图的示例还显示了如何使用SPARQL组合多个图来启用查询选项。
Examples using named graphs have shown you how combining multiple graphs in SPARQL opens up your querying options.
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