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1.
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   

2.
付仲良  胡玉龙  翁宝凤  彭瑞 《测绘学报》2016,45(11):1342-1351
为了解决基于"键-值"模型的云存储环境仅支持简单的关键字查询,不支持多维空间查询的问题,提出了一种新的分布式空间索引方法——M-Quadtree索引。在索引构建过程中,设计了一种基于改进四叉树的空间数据划分方法,该方法规定了叶节点区域的最小数据量,通过四叉树叶节点的再合并,解决了划分后各子区域间存储量不平衡的问题,并且满足了MapReduce并行化要求。给出了MapReduce框架下M-Quadtree索引的快速构建、查询与更新算法,并在搭建的Hadoop平台进行了关键参数对索引效率的影响以及不同规模数据下索引的创建、查询和更新试验。与现有分布式空间索引的对比试验及分析结果表明,M-Quadtree索引在数据存储量负载均衡、算法并行化和空间查询效率等方面表现得更好。  相似文献   

3.
对MR-tree进行邻近关系信息的存储扩充,引入Voronoi图构建VoMR-tree索引。同时,提出了一种基于VoMR-tree的空间查询算法,讨论了分布式环境下的数据处理和算法并行化问题。实验结果表明,所提出的算法在执行时间和占用存储空间上都优于常用的空间索引方法。  相似文献   

4.
空间索引是解决分布式环境下空间查询的关键。提出一种基于多级R-tree的分布式空间索引,避免存储内容扩充而造成的数据冗余。同时针对由于数据分割造成的拓扑关系信息变化问题,进行基于Voronoi图查询验证的研究,并通过试验证实这种分布式空间索引,以及辅助的查询验证方法在分布式环境下的高效性。  相似文献   

5.
基于层次化P2P协议的网格空间数据库系统模型   总被引:1,自引:0,他引:1  
针对传统空间数据库技术的不足,充分结合网格空间数据管理的新特点,提出了一种适合于网格环境的空间数据库系统模型(grid peer-spatial database management system,GPeer-SDBMS)。该模型运用P2P协议Tapestry构建了基于网格的层次化分布式空间数据库,不仅利用数据库的模式差异将网格空间数据库划分为由相同模式节点组成的多个虚拟节点集合,实现了空间数据的分布式存储,而且还对查询算法进行了有效的改进,并通过实验验证了查询算法的高效性。  相似文献   

6.
并行R树空间索引结构中叶节点的大小是影响索引效率的主要因素,其确定方法是并行R树索引结构性能优劣的关键。本文讨论并设计了一种多层并行R树空间索引结构,文中以系统的查询响应时间作为性能评估指标,给出了并行R树叶节点大小的确定方法,并通过实验验证了该方法的有效性和适用性,同时也论证了本文所设计的多层并行R树索引结构是合理的和高效的。  相似文献   

7.
针对传统的空间数据库管理方式在可扩展性、容错性和成本上难以满足分布式海量数据管理需求的问题,提出了基于开源大数据平台HBase的海量空间数据管理方案。根据空间数据操作方式的局部性特征,对存储于云平台中的空间数据,使用空间四叉树模型组织栅格数据,引入Z序空间填充曲线组织矢量数据,并建立空间索引,利用两步查询法(过滤和精化)进行空间查询。该方案在继承了HBase平台易于横向扩展、伸缩性和容错性强等特性的同时也保证了空间查询效率。基于此方案,设计实现了云空间地图服务系统CGMapServer。测试表明,该系统在高并发情况下对大数据集的空间查询响应具有较好的实时性。  相似文献   

8.
一种面向并行空间数据库的数据划分算法研究   总被引:6,自引:1,他引:6  
面向基于对象关系型数据库而构建的并行空间数据库系统,提出了一种基于Hilbert空间填充曲线的适合于矢量空间数据的数据划分算法。在充分考虑空间信息的海量特征以及矢量数据存储记录的不定长等特点的前提下,该算法可实现并行空间数据库中海量空间数据记录在多个存储设备上的均衡划分,以避免出现数据倾斜现象,从而提高了空间数据的检索与查询效率。  相似文献   

9.
结合mapgis7.0的空间数据模型,本文提出了分布式空间结构化查询语言(DGSQL),设计了DGSQL的查询解释器,给出了分布式空间结构化查询语言分解为空间数据节点的GSQL的方法,建立了全局空间查询语句到局部查询语句的映射模型,从而支持空间数据的分布式查询,实现了分布式网络环境下的空间数据的关系运算(相等、并、交、差等)、合成运算(几何长度、缓冲区、相交区域等)等各种空间运算。  相似文献   

10.
引入VoR-Tree空间索引,并基于传统MQM算法对kANN查询算法进行并行化改造,使得空间数据的存储和计算都迁徙到Hadoop集群上,并通过实验对该算法进行了性能测试和分析。结果表明,与单节点计算相比,基于VoR-Tree索引的并行kANN查询算法程序具有良好的性能和近似直线的加速比。  相似文献   

11.
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   

12.
基于开销代价的网络地理信息服务负载均衡算法研究   总被引:2,自引:0,他引:2  
王浩  喻占武  李锐  曾武 《测绘学报》2009,38(3):0-201
基于单位时间开销代价矢量,提出同时考虑地形数据请求在服务器队列中的等待时间与服务器对请求的处理时间的最小总代价分布式算法#算法计算转发请求给每台服务器的概率空间,并根据地形数据请求到达时临时生成的随机数在概率空间中的落点确定转发请求的目标服务器。通过配置单位时间开销代价矢量!可以灵活地维护与升级集群服务器,具有良好的可扩展性。仿真结果表明最小总代价算法能在大规模$高强度的地形漫游中均衡地分发请求!使集群服务器充分发挥其优势从而获得最小的漫游响应时间。仿真结果还表明在低强度地形漫游时,各种负载均衡算法表现大致相同;在高强度地形漫游时!设计负载均衡算法最好考虑服务器端队列的排队情况。  相似文献   

13.
基于空间分析的送电线路选线方法   总被引:3,自引:0,他引:3  
在研究送电线路选线原则的基础上,利用空间建模方法,结合地形分析与空间分析,通过人工干预得到初选路径,经专家分析确定布线路径,以此确定合理的布线走廊,为送电线路选线提供科学依据。  相似文献   

14.
Developing local measures of spatial association for categorical data   总被引:2,自引:0,他引:2  
This paper describes a procedure for extending local statistics to categorical spatial data. The approach is based on the notion that there are two fundamental characteristics of categorical spatial data; composition and configuration. Further, it is argued that, when considered locally, the latter should be measured conditionally with respect to the former. These ideas are developed for binary, gridded data. Local composition is measured by counting the numbers of cells of a particular type, while local configuration is measured by join counts. The approach is illustrated using a small, empirical data set and an ad hoc procedure is developed to deal with the impact of global spatial autocorrelation on the local statistics.The author gratefully acknowledges financial support from the GEOIDE Network of Centres of Excellence (ENV #4) and the helpful comments of three anonymous reviewers.  相似文献   

15.
Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clustering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.  相似文献   

16.
AutoCAD Map2000在图形接边中的应用   总被引:5,自引:0,他引:5  
图形接边是数字测图或图形矢量化工作中的重要一环。讨论了AutoCAD Map2000的接边原理及利用AutoCAD Map2000的地图查询和图形编辑功能进行多幅相邻图幅间图形接边的方法和步骤,并结合作者的实践经验,提出了图形接边时应注意的几个问题。  相似文献   

17.
DCAD: a Dual Clustering Algorithm for Distributed Spatial Databases   总被引:2,自引:0,他引:2  
Spatial objects have two types of attributes: geometrical attributes and non-geometrical attributes, which belong to two different attribute domains (geometrical and non-geometrical domains). Although geometrically scattered in a geometrical domain, spatial objects may be similar to each other in a non-geometrical domain. Most existing clustering algorithms group spatial datasets into different compact regions in a geometrical domain without considering the aspect of a non-geometrical domain. However, many application scenarios require clustering results in which a cluster has not only high proximity in a geometrical domain, but also high similarity in a non-geometrical domain. This means constraints are imposed on the clustering goal from both geometrical and non-geometrical domains simultaneously. Such a clustering problem is called dual clustering. As distributed clustering applications become more and more popular, it is necessary to tackle the dual clustering problem in distributed databases. The DCAD algorithm is proposed to solve this problem. DCAD consists of two levels of clus- tering: local clustering and global clustering. First, clustering is conducted at each local site with a local clustering algorithm, and the features of local clusters are extracted. Second, local features from each site are sent to a central site where global clustering is obtained based on those features. Experiments on both artificial and real spatial datasets show that DCAD is effective and efficient.  相似文献   

18.
This paper examines the utility of a number of pattern measures for local exploratory analysis of binary spatial data. Based on a review of existing pattern measures in cartography, geography, image analysis, and landscape ecology, two fundamental classes of such measures, termed compositional and configurational, are identified. The paper focuses on configurational measures and it is suggested that as many as five such measures (join counts, patch numbers, patch sizes, patch proximity, and distribution of the classes relative to the focal cell of the window) are required to differentiate between all possible local categorical maps. This suggestion is explored by examining aspects of the statistical behaviour (probability distributions and correlations between extreme values of pairs of measures) of a set of 12 configurational measures. Their use is also demonstrated by means of an empirical example.  相似文献   

19.
利用成都市主城区银行网点数据,运用GIS空间数据分析方法,对银行业空间分布、集聚分布及形成机制等进行研究。结果表明:成都市银行业呈同心圆环状向心分布格局,并向西和向南聚集明显;成都市银行业空间呈现倒"U"形集聚特征,距离为6.5 km处集聚趋势最强;成都市已形成了多个银行集聚区,这些集聚区呈现"中心高度集聚,周围零散分布"的趋势,主要分布在三环内的西北和正南方向。不同的集聚区其形成机制不同,成都市银行业集聚区形成除受交通因素影响外,还主要受商业、商务、居住等因素影响,其中商业-商务中心综合作用、商业中心-居住综合作用是主要的影响因素。  相似文献   

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