MOQ-QR:基于QR-树的连续K近邻查询算法研究*

综合分析了R-树和四叉树在处理移动对象的连续K近邻（简称CKNN）查询算法中的不足,提出了一种基于R树和四叉树索引结构,去解决移动对象连续K近邻查询算法。该算法通过对移动对象分配静态空间,并在研究区域内利用QR-树和hash表作为索引去存储移动对象以此计算查询点与移动对象之间的空间距离。实验证明,该算法与现有算法相比,不仅提高了数据的查询效率,而且降低了系统资源的消耗。     

EIR：具有超级结点的非结构化P2P中多维数据搜索框架研究

在具有超级结点的非结构化P2P系统中，研究了复杂多维数据的查询搜索策略，提出了一个应用于具有超级结点的非结构化P2P网络的综合框架，在该框架中，能够实现对多维数据共享、索引以及查询等操作的处理。以R^＊-tree索引树为基础，提出了一种能够应用于P2P的扩展R^＊-tree索引树，即EIR-tree树，研究了系统中集群信息的收集与维护、EIR-tree树的构建与维护等方法和措施。     

ATC-GIS数据插入方法的研究和改进

现有ATC-GIS采用one-by-one的插入方式将海量新数据插入R树索引中,效率较低,并且不能较好地维护R树查询性能。针对该问题,研究并设计一种基于SCB方法的改进数据插入方法,采用种子树指导聚类并构建输入R树来批量插入新数据,利用再压缩过程优化R树结构,通过实验比较选择STR压缩算法构建输入R树。在ATC-GIS上的实验证明,改进后的方法在插入时间和查询效率的维护方面优于现有系统。     

The effect of buffering on the performance of R-trees

Past R-tree studies have focused on the number of nodes visited as a metric of query performance. Since database systems usually include a buffering mechanism, we propose that the number of disk accesses is a more realistic measure of performance. We develop a buffer model to analyze the number of disk accesses required for spatial queries using R-trees. The model can be used to evaluate the quality of R-tree update operations, such as various node splitting and tree restructuring policies, as measured by query performance on the resulting tree. We use our model to study the performance of three well-known R-tree loading algorithms. We show that ignoring buffer behavior and using number of nodes accessed as a performance metric can lead to incorrect conclusions, not only quantitatively, but also qualitatively. In addition, we consider the problem of how many levels of the R-tree should be pinned in the buffer     

一种支持多维数据范围查询的对等计算索引框架

如何有效地支持多维数据范围查询是传统数据管理领域的研究热点之一.但是,在大规模分布式系统中,这仍然是一个具有挑战性的研究工作.VBI-tree是一个对等计算环境下基于平衡树的索引架构,在该架构上可以实现集中式环境下的多种支持多维数据索引的层次化树结构,例如R-tree,X-tree和M-tree等.VBI-tree设计的查询算法保证查询可以从树的任意位置开始,而不是像集中式环境下层次化树结构那样采用从树的根节点开始查询的方法,从而成功地避免了根节点引起的系统性能瓶颈问题.对于有N个节点的网络,索引方法可以保证查询效率是O(log N).VBI-tree提出了基于AVL-tree旋转的网络重构负载均衡策略可以有效地均衡负栽.另外,在数据操作频繁的情况下,为了提高索引的性能,在VBI-tree上建立特殊的祖先-子孙链接形成VBI-tree的结构.通过使用祖先-子孙链接,可保证对于相关查询区域的探索尽量发生在同层节点之间,而不是一直往根节点方向发送,从而减轻上层节点的查询负担,并且显著地降低了更新代价.模拟实验验证了提出的方法的有效性.     

Merging R-Trees: Efficient Strategies for Local Bulk Insertion

A lot of recent work has focussed on bulk loading of data into multidimensional index structures in order to efficiently construct such structures for large datasets. In this paper, we address this problem with particular focus on R-trees—which are an important class of index structures used widely in commercial database systems. We propose a new technique, which as opposed to the current technique of inserting data one by one, bulk inserts entire new datasets into an active R-tree. This technique, called STLT (for small-tree-large-tree), considers the new dataset as an R-tree itself (small tree), identifies and prepares a suitable location in the original R-tree (large tree) for insertion, and lastly performs the insert of the small tree into the large tree. Besides an analytical cost model of STLT, extensive experimental studies both on synthetic and real GIS data sets are also reported. These experiments not only compare STLT against the conventional technique, but also evaluate the suitability and limitations of STLT under different conditions, such as varying buffer sizes, ratio between existing and new data sizes, and skewness of new data with respect to the whole spatial region. We find that STLT does much better (in average, about 65%) than the existing technique for skewed datasets as well for large sizes of both the large tree and the small tree in terms of insertion time, while keeping comparable query tree quality. STLT consistently outperforms the alternate technique in all other circumstances in terms of bulk insertion time, especially, even up to 2,000% for the cases when the area of new data sets covers up to 4% of the global region covered by the existing index tree; however, at the cost of a deteriorating resulting tree quality.     

数据分发管理匹配算法的R-树实现

数据分发管理（DDM）是高层体系结构（HLA）接口规范的6类服务之一，高效的区域匹配算法是DDM研究的重点和难点．当前的多种匹配算法往往只适用于特定的应用环境，且效率不够理想．R-树法是在空间索引技术的基础上提出的一种新的匹配算法，该方法用R-树对DDM区域的矩形进行组织，并利用Hash索引对其叶结点的组织方式进行了改进．实验结果表明R树法可有效减少动态DDM的维护开销，提高分布交互仿真的实时性,通过调整R-树的相关参数，可以进一步改善匹配算法的性能．     

电缆三维模型可视化及数据高效索引研究

针对电缆三维可视化场景的加载速度慢问题, 提出一种用于电缆工程场景下的三维模型外表面提取简化算法与多细节层次R-树索引数据调度组织方法. 首先对占据三维场景中大量内存的电缆井和管沟模型进行LOD层级简化, 实验结果显示数据量大幅度减小; 然后根据多细节层次的R-树索引结构对简化后的数据进行组织调度, 与传统R-树相比, 该方法构建的R-树在节点筛选和节点分裂时构造了更优的树形, 使得在进行数据的索引和调度时, 对电缆工程三维场景加载速度的提高有明显效果, 有效地实现了电缆工程中三维模型的流畅展示.     

空间数据库系统中树的并发控制设计与实现

R树是实现快速空间数据处理的重要索引结构之一,但由于其并发控制的复杂性,虽研究已久但仍然很少真正地集成到商用数据库中。树是为了实现并发控制而提出的一种树结构的变形,但它仍然存在幻像等问题。文章分析了树中存在的这一问题并通过设R-linkRR-link计一个基于内存的操作控制列表来预先避免可能造成幻像的并发操作,从而实现完全的并发控制。实验证明所(Operation Control List, OCList)提方案是正确的、低开支的,而且有利于提高系统性能。     

基于R树的协同过滤推荐算法

R树是一个高度平衡树,也是目前应用最为广泛的空间索引结构.本文以用户行为的历史数据之间的相似度构造R树,提出一种基于R树的协同过滤推荐算法（R_CF）;另外,从用户的隐式反馈着手,构建用户兴趣行为数据模型,并进行数据标准化处理.仿真实验表明:较之传统的协同过滤推荐算法（CF）,本文提出的R_CF算法可以极大提升推荐top-n个相似度最高的用户时的查询速度.     

Generic and efficient framework for search trees on flash memory storage systems

Tree index structures are crucial components in data management systems. Existing tree index structure are designed with the implicit assumption that the underlying external memory storage is the conventional magnetic hard disk drives. This assumption is going to be invalid soon, as flash memory storage is increasingly adopted as the main storage media in mobile devices, digital cameras, embedded sensors, and notebooks. Though it is direct and simple to port existing tree index structures on the flash memory storage, that direct approach does not consider the unique characteristics of flash memory, i.e., slow write operations, and erase-before-update property, which would result in a sub optimal performance. In this paper, we introduce FAST (i.e., Flash-Aware Search Trees) as a generic framework for flash-aware tree index structures. FAST distinguishes itself from all previous attempts of flash memory indexing in two aspects: (1) FAST is a generic framework that can be applied to a wide class of data partitioning tree structures including R-tree and its variants, and (2) FAST achieves both efficiency and durability of read and write flash operations through memory flushing and crash recovery techniques. Extensive experimental results, based on an actual implementation of FAST inside the GiST index structure in PostgreSQL, show that FAST achieves better performance than its competitors.     

基于R*-tree的时空数据库索引VC-tree

在时空数据的索引结构中,HR-tree可以高效处理时间片查询,但对时间段查询效率低下,同时存在存储冗余。3D-tree索引的效率较低,双树结构使索引维护较为困难,且磁盘访问开销大。该文提出一种新的基于R*-tree的索引结构VC-tree,便于管理维护,可以高效满足时空查询,并满足有效时间内的未来查询。     

基于结点分裂优化的R-树索引结构

针对R-树索引空间查询效率低下的问题,提出一种基于结点分裂优化的R-树索引结构：SR-树索引。SR-树索引在结点分裂过程中,通过增加叶子结点的空间数据聚集性来减少叶子结点最小外接矩形的覆盖面积。为了有效降低磁盘读写消耗,SR-树结点在写入索引时,首先将索引树在内存中建好,然后在文件中写入树信息,最后通过递归的方式写入结点。实验结果表明,与R-树索引相比,SR-树索引可以在减少最小外接矩形重叠面积的同时,有效降低查询响应时间,从而达到提高查询效率的目的。     

一种结合空间聚类算法的R树优化算法

研究R树特点,考虑了离群点对R树结点构造的影响,结合改进的k-medoids聚类算法提出了一种新的R树构造算法。与传统R树相比,新算法下构造的R树结点更加紧凑。通过实验证明,该优化算法构造的R树在查询性能方面的改进是明显的。     

KDT树:一种多维空间数据索引结构

通过扩展KD树索引结构,提出了一种新的多维空间数据索引结构——KDT树,给出了数据结构和算法描述,并通过与当前流行的空间数据索引结构——R树的对比,对其性能进行了测试与评估。实验表明,作为一种主存索引结构,KDT树在时间效率方面明显优于R树,并且此种优势随着索引记录数量的增多而越加明显。此外,KDT树亦能较好地解决常规KD树在索引占据一定空间范围的空间对象(如:线、面、体等)时存在的问题。     

一种用于空间数据多尺度表达的R树索引结构

针对现有R树无法支持空间数据多尺度表达的问题,提出了一种用于空间数据多尺度表达的R树变形索引结构:(1)允许空间对象出现在非叶结点上;(2)利用树的深度反映空间分辨率的变化,提供分辨率维的支持;(3)树的分支结构考虑对自动制图综合算法的支持.分析了该变形R树索引结构的空间数据多尺度查询过程,并着重对该索引结构生成算法中的约束条件、插入算法和分裂算法进行了讨论.针对相同数据源,使用该方法与基于四叉树的空间数据多尺度索引方法进行了对比实验,结果表明,该索引方法能有效检索多分辨率形式组织的空间数据,具有综合结果记忆功能,效率明显.     

基于惰性聚类分裂的动态R树实现方法

R^＊树是目前公认查询效果很好的R树变体，但是其构造代价较原始R树增加数倍，对于插入删除和更新频繁的空间数据效果不好。为此，本文提出一种基于惰性聚类分裂技术的R树动态实现方法（LR树）。惰性聚类分裂技术是在对象插入节点导致溢出时不立即进行分裂，而是尝试将其插入到邻近的未满节点中，直到邻近节点均已满时，再利用聚类技术进行节点分裂，在邻近节点和分裂节点之间重组入口项。LR树在确保查询性能的前提下，大大降低了构造代价，并且大幅提高了索引结构的空间利用率。最后的分析和实验证明了LR树的高效性。     

Parallel multi-dimensional range query processing with R-trees on GPU

The general purpose computing on graphics processing unit (GP-GPU) has emerged as a new cost effective parallel computing paradigm in high performance computing research that enables large amount of data to be processed in parallel. Large scale scientific data intensive applications have been playing an important role in modern high performance computing research. A common access pattern into such scientific data analysis applications is multi-dimensional range query, but not much research has been conducted on multi-dimensional range query on the GPU. Inherently multi-dimensional indexing trees such as R-Trees are not well suited for GPU environment because of its irregular tree traversal. Traversing irregular tree search path makes it hard to maximize the utilization of massively parallel architectures. In this paper, we propose a novel MPTS (Massively Parallel Three-phase Scanning) R-tree traversal algorithm for multi-dimensional range query, that converts recursive access to tree nodes into sequential access. Our extensive experimental study shows that MPTS R-tree traversal algorithm on NVIDIA Tesla M2090 GPU consistently outperforms traditional recursive R-trees search algorithm on Intel Xeon E5506 processors.     

High-dimensional similarity joins

Many emerging data mining applications require a similarity join between points in a high-dimensional domain. We present a new algorithm that utilizes a new index structure, called the ε tree, for fast spatial similarity joins on high-dimensional points. This index structure reduces the number of neighboring leaf nodes that are considered for the join test, as well as the traversal cost of finding appropriate branches in the internal nodes. The storage cost for internal nodes is independent of the number of dimensions. Hence, the proposed index structure scales to high-dimensional data. We analyze the cost of the join for the ε tree and the R-tree family, and show that the ε tree will perform better for high-dimensional joins. Empirical evaluation, using synthetic and real-life data sets, shows that similarity join using the ε tree is twice to an order of magnitude faster than the R⁺ tree, with the performance gap increasing with the number of dimensions. We also discuss how some of the ideas of the ε tree can be applied to the R-tree family. These biased R-trees perform better than the corresponding traditional R-trees for high-dimensional similarity joins, but do not match the performance of the ε tree     

一种基于R-树的空间索引结构

为了有效构建R-树,通过分析数据矩形的性质,结合改进的K-均值算法,提出一种用于构建R-树的数据矩形聚类新方法,给出基于R-树和四叉树的空间索引结构以及该空间索引结构的构造算法和节点插入算法。研究结果表明,该索引结构具有更紧凑的结构和更高的空间查询效率。