主存空间对象的索引方法

空间索引关系空间数据库和地理信息系统的整体性能。目前，随着计算机主存价格的迅速下降，发展主存空间数据库已经成为可能，主存空间数据库需要相适应的空间索引。本文设计两种主存的空间索引－主存网格索引和主存Ｆ－树索引，并对两者的性能进行比较。在多数应用环境下，Ｆ－树空间索引性能更优。     

基于Realms的主存R树索引的实现

为了充分发挥主存数据库技术的优越性，提高系统性能，需要使用空间索引，并将索引也放在主存中。R树类是目前空间数据索引的研究热点，具有动态性及构造和维护的简单性，在基本R树索引的基础上便于作各种算法改进，文中开发的基于Realms的空间分析数据库管理系统SADBS中实现了主存R树索引的创建及插入、删除、更新、查询等操作。     

常用空间索引技术的分析

空间索引是空间数据库的关键技术,其性能的高低决定了整个空间数据库的效率。本文介绍两类常用空间索引;R树类空间索引和四叉树类空间索引。并详细的分析了各自的优缺点。     

面向多核处理器的空间数据库索引性能分析

空间数据库索引是提高空间查询性能的重要方法,片上多核处理器的出现,在提高数据库性能的同时,也给空间索引结构提出了新的挑战.面向多核处理器,还缺乏对各种主要空间数据库索引结构和性能的综合实验比较,从而无法定量分析影响索引性能的重要因素.针对目前主流的共享cache多核处理器,对R树,Hilbert R树、四又树及CR树在...     

空间数据库索引技术的研究

空间数据库的索引是提高空间数据库存储效率、空间检索性能的关键技术。传统的索引技术不能有效地索引空间数据。在综述现有空间数据库索引技术的基础上，提出了一种面向大型空间数据库的QR-树索引方法。     

R*-树空间索引的优化研究

针对大型空间数据库应用的需求及己有空间索引技术的不足，在论述R-树及R*-树索引技术的相关概念、数据结构、算法描述及性能分析的基础上，提出了一种改进的R*-树空间索引结构。研究结果表明：改进后的R*-树与原始的R*-树相比具有更高的性能。     

空间索引的混合树结构研究

针对大型的空间数据库和多媒体数据库的应用,R-树索引结构是一种有效的高维数据索引结构,但R树也有其缺点,文章提出了一种基于四叉树和R-树的混合树空间索引结构,研究结果表明:在存储开销上,混合树比R-树略高,但插入、删除、特别是查找的性能得到了显著的提高,且索引量越大,混合树的查找性能越明显优于R-树。     

CQRtree空间数据库索引结构及实现算法

空间数据库的索引是提高空间数据库存储效率、空间检索性能的关键技术.本文在R树索引的基础上提出了一种新的空间数据库索引结构CQRtree,给出了CQRtree的数据结构、插入、删除、查询实现算法以及性能分析与比较,最后指出了进一步的研究方向.     

DPsIR^＋：一种基于动态空间槽的分布式并行空间索引树

空间索引是空间数据库的关键组成部分，其性能的优劣直接决定着空间数据操作的效率。为此，在分析了现有各种空间索引的基础上，将分布并行处理技术与空间索引相融合，提出了一种DPsIR^＋树。DPsIR^＋树借助繁衍和返祖，动态分割空间槽，并将它们映射到多个节点机上。每个节点机再将其对应空间槽中的空间对象组织成R树，并将R树分裂成多个残枝，将残枝并行存入本地MultiDisk中；在内存中则按R-link组织空间对象，按R^＋处理节点溢出。实验结果表明DPsIR^＋树具有良好的查询特性。     

一种基于R树和四叉树的移动对象空间数据库混合索引结构

由于实时跟踪移动的空间对象需要不断的更新空间数据库的索引从而导致系统执行效率非常低下，传统静态的空间索引结构性能并不是很好的工作在动态环境下。提出了一种称为Q＋R树的移动空间对象混合索引结构，阐述了该数据结构的生成、更新、查询方面算法思想，最后给出了Q＋R树的试验分析结论。     

基于R树的空间数据索引技术的探索

目前,基于空间数据库管理系统的空间数据索引技术的研究与应用正不断地兴起,且日趋成熟。分析经典的空间数据索引技术R-树和R^＊-树的优势与特点,在R^＊-树索引结构的基础上融合了传统的四叉树索引方法的精髓,提出了一种改进的R^＊-树索引技术一即R^＊Q-树索引方法。设计并实现了R^＊Q-树索引方法中索引构造算法的主要改进部分,并采用大量的随机实验数据验证了改进算法对提高空间数据索引效率的有效贡献。     

An efficient spatial access method for spatial images containing multiple non-overlapping features

In this paper we propose and analyze a new spatial access method, namely the S*-tree, for the efficient secondary memory encoding and manipulation of images containing multiple non-overlapping features (i.e., coloured images). The S*-tree is based on a non-straightforward and space efficient extension to coloured images of its precursor, namely the S⁺-tree, which was explicitly designed for binary images. To assess experimentally the qualities of the S*-tree, we test it against the HL-quadtree, a previous spatial access method for coloured images, which is known to be space and time efficient. Our experiments show that the S*-tree reaches up to a 75% of space saving, and performs constantly less I/O accesses than the HL-quadtree in solving classical window queries.     

R*-树空间索引的改进

R*-树基于局部调整的思想对节点进行管理,存在时间与效率上的不足。为克服R*-树的不足,给出了一种新型的存储结构,并给出新型存储结构的插入、溢出、分裂等空间索引常用操作的算法。分析新的存储结构的优点,并通过实验结果说明该方案比R*-树具有更高的效率。     

一种基于关键词搜索的空间连接查询

许多实际的应用需要同时支持空间连接查询和关键词搜索。在给出基于关键词的空间连接（KSJ）查询定义的基础上,对参与KSJ查询的空间数据集建立MIR2-树索引结构,并结合一些高效的搜索剪枝策略,提出一种基于宽度优先的KSJ查询算法。实验结果表明该算法可有效支持基于关键词的空间连接查询处理。     

Indexing moving objects for directions and velocities queries

Moving object databases are required to support different types of queries with a large number of moving objects. New types of queries namely directions and velocity queries (DV queries), are to be supported and covered. The TPR-tree and its successors are efficient indexes that support spatio-temporal queries for moving objects. However, neither of them support the new DV queries. In this paper, we propose a new index for moving objects based on the TPR*-tree, named Direction and Velocity of TPR*-tree or DV-TPR*-tree, in order to build data a structure based on the spatial, direction and velocity domains. DV-TPR*-tree obtains an ideal distribution that supports and fulfils the new query types (DV queries). Extensive performance studies show that the query performance of DV-TPR*-tree outperforms the TPR-tree and its successors.     

Using B<Superscript>+</Superscript>-trees for processing of line segments in large spatial databases

Points, lines, and regions are the three basic entities for constituting vector-based objects in spatial databases. Many indexing methods (G-tree, K-D-B tree, Quad-tree, PMR-tree, Grid-file, R-tree, and so on) have been widely discussed for handling point or region data. These traditional methods can efficiently organize point or region objects in a space into a hashing or hierarchical directory. They provide efficient access methods to meet the requirement of accurate retrievals. However, two problems are encountered when their techniques are applied to deal with line segments. The first is that representing line segments by means of point or region objects cannot exactly and properly preserve the spatial information about the proximities of line segments. The second problem is derived from the large dead space and overlapping areas in external and internal nodes of the hierarchical directory caused by the use of rectangles to enclose line objects. In this paper, we propose an indexing structure for line segments based on B ⁺ -tree to remedy these two problems. Through the experimental results, we demonstrate that our approach has significant improvement over the storage efficiency. In addition, the retrieval efficiency has also been significantly prompted as compared to the method using R-tree index scheme. These improvements derive mainly from the proposed data processing techniques and the new indexing method.     

Adaptive Indexing of Moving Objects with Highly Variable Update Frequencies

In recent years, management of moving objects has emerged as an active topic of spatial access methods. Various data structures （indexes） have been proposed to handle queries of moving points, for example, the well-known B^x-tree uses a novel mapping mechanism to reduce the index update costs. However, almost all the existing indexes for predictive queries are not applicable in certain circumstances when the update frequencies of moving objects become highly variable and when the system needs to balance the performance of updates and queries. In this paper, we introduce two kinds of novel indexes, named B^y-tree and αB^y-tree. By associating a prediction life period with every moving object, the proposed indexes are applicable in the environments with highly variable update frequencies. In addition, the αB^y-tree can balance the performance of updates and queries depending on a balance parameter. Experimental results show that the B^y-tree and αB^y-tree outperform the B^x-tree in various conditions.     

近似查询中重叠区域的扫描计算

根据R*树节点硬盘主引导记录(MBR)特征,在不改变最小外包矩形特征的前提下,通过区域扫描对正交MBR重叠区域边界建立二叉线段树,以此为基础分别计算面积和周长,有效改善了R*树节点结构。理论分析和实验均表明,与未经过重叠区域计算的R*树相比,经计算的R*树节点比较次数平均下降了近60%,准确率提高了约60%。     

基于真实物体模型的空间对象检索技术研究

针对MBR模型只适合检索联通的空间对象且查准率较低的问题,建立了真实物体模型,依据空间对象在平面上的几何投影的方法建立真实物体模型,采用深度方向关系矩阵表示真实物体模型之间的方向关系,利用该模型可以很好地将矩形代数良好的计算性质应用于空间对象检索技术中,简化了检索的过程,实现了矩形代数与真实物体模型的有效结合,使其检索的范围变的更加广泛.同时提出了一种改进的R*树算法,有效地提高了真实物体模型的检索精确度,降低了错误匹配的发生率.实验结果表明,该算法可以有效地提高检索的查准率,提高空间对象检索的效率.