共查询到18条相似文献,搜索用时 890 毫秒
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随着空间信息网格的建设,网格平台上管理的空间信息资源越来越丰富,这促进了空间信息网格中空间数据分布式查询的应用需求,而在分布式空间查询中,空间连接查询操作往往成为性能的瓶颈.根据空间信息的特点,通过利用网格计算资源来优化空间连接查询的执行.首先基于网格服务构建网格平台分布式空间数据查询软件结构,通过设计远程空间连接执行服务利用网格平台中的计算资源;根据空间信息的特点.采用基于Kd-Tree空间分区并行连接的方法提高远程空间数据连接操作执行效率,并给出了远程空间连接执行的查询代价模型;然后根据连接代价模型设计了远程空间连接查询执行计划优化生成算法;最后总结了本文工作并探讨了下一步研究方向. 相似文献
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多源空间数据汇交服务是以兼容性的空间数据存储模型为基础,采用自动化的网络传输方式完成异地空间数据源的交互,是空间数据共享理论的一种具体实现。汇交服务能够有效地提高政务系统内部空间数据传输共享的执行效率,缩短地方政府地理信息综合应用平台的建设周期。本文首先介绍了空间数据汇交服务系统的总体架构和运行模式,然后通过多项测试验证系统的执行效率和稳定性,并验证其实际应用价值。 相似文献
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针对传统的关系型空间数据库已经不能很好地适应于超大规模高并发空间查询访问的处理需要的问题,该文着眼于解决大数据时代下地理信息服务所面临的日益严峻的大规模空间查询访问需求,探索了一套基于Spark架构的空间查询实现技术,并给出相应的解决方案。提出一个基于Spark并提供类SQL访问接口的空间查询实现模型GeoSpark SQL,解决了以下关键问题:数据的外包矩形数据生成和标准地理信息数据对Spark的导入导出方法;Spark空间查询算子实现方法;Spark空间索引与查询优化方法。GeoSpark SQL模型在初步实验中,已可以满足实时性的要求,对复杂的空间查询也能有良好的性能表现。 相似文献
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基于XQuery的GML查询语言研究 总被引:4,自引:0,他引:4
随着GML规范的不断完善及GIS软件厂商的广泛支持,越来越多的空间数据以GML格式存储,GML空间数据的查询已成为GIS研究的热点问题。传统的关系数据库查询语言SQL是针对平面的二维关系数据而设计的,并不适合XML/GML半结构化数据的查询;商品化GIS软件的查询系统只能查询自身的空间数据而无法查询其它GIS系统的空间数据;XML查询的研究为GML查询奠定了一定的基础。首先针对GML查询存在的问题,提出了扩展XQuery是GML查询语言实现的最佳选择;结合XML查询语言和空间数据查询语言,提出了GML查询语言的特征和GML查询语言系统框架;并根据GML空间数据的特点,以XML标准查询语言XQuery为基础,提出了XQuery空间扩展的内容;开发了GML空间数据查询语言GMLXQL,实现了GML空间数据的本原查询。 相似文献
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本文介绍了非关系型数据库MongoDB的空间查询功能,针对海量用户并发服务的应用场景,提出了一种空间近邻信息查询引擎的优化策略。该策略根据短时间内空间数据内容变化较小及相邻位置搜索结果相似度高的特征,利用格网化机制实现在等效查询结果前提下数据库操作次数减少;同时利用内存缓冲机制减少磁盘I/O读写次数,从而显著提高系统的并发数及查询速度。测试结果表明,在相同硬件设施条件下,优化后搜索引擎的效率比原生系统提高了近50倍。 相似文献
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Spatial selectivity estimation is crucial to choose the cheapest execution plan for a given query in a query optimizer. This article proposes an accurate spatial selectivity estimation method based on the cumulative density (CD) histograms, which can deal with any arbitrary spatial query window. In this method, the selectivity can be estimated in original logic of the CD histogram, after the four corner values of a query window have been accurately interpolated on the continuous surface of the elevation histogram. For the interpolation of any corner points, we first identify the cells that can affect the value of point (x, y) in the CD histogram. These cells can be categorized into two classes: ones within the range from (0, 0) to (x, y) and the other overlapping the range from (0, 0) to (x, y). The values of the former class can be used directly, whereas we revise the values of any cells falling in the latter class by the number of vertices in the corresponding cell and the area ratio covered by the range from (0, 0) to (x, y). This revision makes the estimation method more accurate. The CD histograms and estimation method have been implemented in INGRES. Experiment results show that the method can accurately estimate the selectivity of arbitrary query windows and can help the optimizer choose a cheaper query plan. 相似文献
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Selectivity estimation is crucial for query optimizers choosing an optimal spatial execution plan in a spatial database management system.This paper presents an Annular Bucket spatial histogram(AB histogram)that can estimate the selectivity in finer spatial selection and spatial join operations even when the spatial query has more operators or more joins.The AB histogram is represented as a set of bucket-range,bucket-count value pairs.The bucket-range often covers an annular region like a sin-gle-cell-sized photo frame.The bucket-count is the number of objects whose Minimum Bounding Rectangles(MBRs)fall between outer rectangle and inner rectangle of the bucket-range.Assuming that all MBRs in each a bucket distribute evenly,for every buck-et,we can obtain serial probabilities that satisfy a certain spatial selection or join conditions from the operations’ semantics and the spatial relations between every bucket-range and query ranges.Thus,according to some probability theories,spatial selection or join selectivity can be estimated by the every bucket-count and its probabilities.This paper also shows a way to generate an updated AB histogram from an original AB histogram and those probabilities.Our tests show that the AB histogram not only supports the selectivity estimation of spatial selection or spatial join with "disjoint","intersect","within","contains",and "overlap" operators but also provides an approach to generate a reliable updated histogram whose spatial distribution is close to the distribution of ac-tual query result. 相似文献
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基于自然语言的空间查询语言是空间数据库和智能GIS的重要研究领域。针对空间查询语言的特殊性就基于自然语言的空间查询语言的解译机制进行了细致的研究。在空间目标名称库、空间词汇库、空间查询句型模板库、空间语料库、查询结果模板库等空间知识库的支持下,介绍了如何用最大匹配分词技术对空间查询语句进行分词;说明了利用空间查询句型模板库中的句型对查询语句进行句法分析,最终解译出空间查询目标和相应空间操作的过程;详细阐述了如何进一步将这些空间目标和空间操作转换成中间空间查询语句(即扩展空间数据类型和空间函数的SQL),并由关系数据库执行查询;最后就如何选择文字或图形两种语言形式进行查询结果表示做了说明。 相似文献
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Dave Kolas 《Transactions in GIS》2008,12(Z1):5-18
Though the intersection of spatial data and semantic web technologies holds significant promise, there are still many challenges before this promise can be realized. One of these challenges is query representation. History suggests that an appropriate solution is a specialized query language for spatial data; however, with a broad interpretation of the SPARQL specification and extensions that would be useful outside the spatial realm, one can use SPARQL to query spatial concepts effectively. This article establishes a set of desiderata for a query language capable of dealing with spatial Semantic Web‐based data, discusses the challenges facing such a query language, and addresses these challenges with straightforward solutions that are broadly applicable. The effectiveness of these extensions is demonstrated using example queries. 相似文献
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