其他脚本在提供的数据示例上测试实现的机制，并给出一个基于距离的查询示例，而且与地图图像结合在一起。
Additional scripts test the implemented mechanisms on the provided sample data and show an example of a distance-based query, complete with map images.
对距离的理解，或者说对查询的输出行为的理解，对于为不同查询选择最有效的选项是至关重要的。
Understanding the distance, or the queries' output behavior, is crucial in determining the most effective option for the individual query.
这5个因素都可以在基于位置的应用程序中扮演重要的角色，但是我在这里主要关注距离计算、限定框过滤和查询解析。
Each of these pieces can play an important role in location-based applications, but for now I'll focus on distance calculations, bounding-box filtering, and query parsing.
与Mall的距离在一定范围内的所有饭店都符合第四个查询的用户，而最后一个查询的用户仅对在Mall内部的饭店感兴趣。
Any restaurant within some distance of the Mall would suit the fourth query's user, whereas the last query's user is only interested in results inside the Mall.
要按照距离进行查询，必须使用投影坐标系。
To query by distance, you must use a projected coordinate system.
这使数据库准备好供GIS使用并支持SQL中实现的空间业务逻辑，包括基于距离的查询。
This prepares the database for use by a GIS and for spatial business logic implemented in SQL, including distance-based queries.
本文的最终目标是为进行空间查询(包括基于距离的查询)而准备现有的数据集。
The end goal of this article's exercise was to prepare an existing dataset for spatial queries, including distance-based ones.
对于以上每种情况，我将一个关键字查询与一个基于距离的FunctionQuery结合起来，生成一个包含关键字记录和距离记录的结果集。
In each of these cases, I've combined a keyword query with a distance-based FunctionQuery to produce a result set that factors in both the keyword score and the distance score.
Lucene支持基于编辑距离算法的模糊搜索，你可以使用波浪符号“~”放在查询词的后面，比如搜索一个与“roam”拼写相近的词可以使用。
Lucene supports fuzzy searches based on the Levenshtein Distance, or Edit Distance algorithm. To do a fuzzy search use the tilde, "~", symbol at the end of a Single word Term.
附录A：基于距离的查询。
为了在检索过程中全面表达查询意愿，提出一种结合同义词典和词对共现距离的查询扩展方法。
To fully express the intention of querying in the information retrieval, we propose a query expansion method based on thesaurus and words co-occurrence distance.
利用时空索引结构的特点，应用距离度量来减小平均磁盘访问量是查询处理常用的方法。
Making use of the structure of st indices, and applying distance metrics to decrease average disk access count is the most popular method in st query processing.
为了提高高维数据相似查询的效率，提出一种基于双重距离尺度(DDM)的新型高维索引结构。
To speed up high-dimensional similarity search efficiency, a novel high-dimensional indexing structure based on dual distance metric (DDM) was proposed.
同时本系统具有一定的空间分析功能，如空间查询、距离量算等。
Furthermore, the system has some spatial analyzing functions such as spatial query, distance measuring, etc.
距离连接在空间数据库中有着广泛的应用，而距离连接的选择度估计是优化距离查询的基础。
Distance join is widely used in spatial database. Selectivity estimation for distance join is the basis of optimizing the query of the distance.
客户端具有以用户查询的点为中心显示地图以及计算两点距离的功能。
The client has two main functions, one is displaying a map with the center of the user request, the other is calculating the distance of two points.
最后，抽取小波包直方图作为特征表示并应用直方图相交距离从图像数据库中检索被查询图像。
Finally, the wavelet packet histogram is computed as feature signatures and histogram intersection distance is employed to retrieval queried image from image databases.
通过修改经典的最近邻查询中距离度量的定义，将移动对象轨迹上最近邻查询和区域查询结合起来进行研究是查询处理算法上一次有益的尝试。
Modifying the classical distance metrics definition in nearest neighbor search, it's a beneficial attempt in querying process that research in combining nearest neighbor query with range query.
为了提高化学主题搜索引擎的查询效果，采用距离加权七一近邻分类算法来进行自动分类。
In order to improve the performance of chemistry-focused search engines, an automatic text categorization algorithm is proposed based on the distance-weighted k-nearest neighbor algorithm.
该 算法利用查询模型计算各种特征向量的先验知识，然后动态地选择描述能力较强的特征向量计算模型之间的相似度距离。
The query model first calculates the prior knowledge of the feature vectors and then dynamically chooses the feature vector with the best description.
考虑概念格中的不同“概念结点”的距离，获取查询扩展词汇，并结合产品层次，就给出了基于概念格的产品优先的查询扩展。
According to the distance between concept nodes in a concept lattice and the product hierarchies, we can get product-oriented query expansions.
考虑概念格中的不同“概念结点”的距离，获取查询扩展词汇，并结合产品层次，就给出了基于概念格的产品优先的查询扩展。
According to the distance between concept nodes in a concept lattice and the product hierarchies, we can get product-oriented query expansions.
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