RFN-B+树索引文件及其有效性

在对比传统的B树和B⁺树的定义和操作算法的基础上,定义了一种新的B⁺树:RFN-B⁺树,以获得更高的空间利用率和可用性.首先比较和分析了RFN-B⁺树与传统B⁺树的空间效率,然后讨论了RFN-B⁺树索引文件的有效性以及支持这种有效性的全链接指针结构和两个备用模块:基于虚拟根结点的随机检索算法和重构结点的算法.     

约束数据库中的索引

本文研究了约束数据库中的索引技术，提出了存储区间常数刺穿集的数据结构S树和S^*树．在刺穿集的最大长度受到限制的条件下，S^*树存储效率是最优的．与M树相比，S树和S^*树有一个明显的改进：可以支持删除操作．     

B+树索引机制的研究及优化

B+树索引是大型数据库系统中较为常用的一种索引技术。当数据庞杂时,B+树索引在查找效率和空间利用率方面还存在不足。针对该问题提出一种改进的B+树结构,首先通过调整叶子结点与非叶子结点的数量关系,以降低树的深度;然后优化原插入算法,在分裂结点前进行平衡处理(BP),以提高树的空间利用率。经试验,改进后的B+树与传统B+树相比,在查找效率和空间利用率上分别提高了10%和6%,证明对B+树的改进具有可行性。     

“伸展树——一种高效的索引树”教学设计

数据结构设计的重要目标之一是提高操作速度,特别是检索速度。局部平衡的红黑树、平衡的AVL树等二叉搜索树具有良好的检索性能,非常适合于基于内存的索引,但为防止树形结构退化为线性结构,在插入和删除结点时经常需要旋转,维护数据结构的操作比较复杂。文章阐述伸展树在检索过程中通过自动调整结构,使访问最频繁的结点靠近树结构的根,从而减少访问代价,指出伸展树可以作为各种线性序列的索引组织方法,能在一些需要高效索引的大工程中加以运用。     

RFN-B+树索引文件及其有效性

在对比传统的B树和B+树的定义和操作算法的基础上,定义了一种新的B+树:RFN-B+树,以获得更高的空间利用率和可用性.首先比较和分析了RFN-B+树与传统B+树的空间效率,然后讨论了RFN-B+树索引文件的有效性以及支持这种有效性的全链接指针结构和两个备用模块:基于虚拟根结点的随机检索算法和重构结点的算法.     

基于Voronoi-R*的隐私保护路网k近邻查询方法

针对已有的保护位置隐私路网k近邻查询依赖可信匿名服务器造成的安全隐患,以及服务器端全局路网索引利用效率低的缺陷,提出基于路网局部索引机制的保护位置隐私路网近邻查询方法.查询客户端通过与LBS服务器的一轮通信获取局部路网信息,生成查询位置所在路段满足l-路段多样性的匿名查询序列,并将匿名查询序列提交LBS服务器,从而避免保护位置隐私查询对可信第三方服务器的依赖.在LBS服务器端,提出基于路网基本单元划分的分段式近邻查询处理策略,对频繁查询请求路网基本单元,构建基于路网泰森多边形和R^*树的局部Vor-R^*索引结构,实现基于索引的快速查找.对非频繁请求路网基本单元,采用常规路网扩张查询处理.有效降低索引存储规模和基于全局索引进行无差异近邻查询的访问代价,在保证查询结果正确的同时,提高了LBS服务器端k近邻查询处理效率.理论分析和实验结果表明,所提方法在兼顾查询准确性的同时,有效地提高了查询处理效率.     

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。     

双数组Trie树算法优化及其应用研究

本文对双数组Trie树(Double-Array Trie)算法提出了一种优化策略,即在采用Trie树构造数组的过程中,优先处理分支结点数更多的结点。这种优化策略可以在保证该算法数据查找效率不变的同时,进一步减少数据稀疏,提高空间利用率。我们基于该优化算法实现了一个词典管理程序,并与利用其他索引机制的词典进行了实验对比。实验结果表明,利用优化的双数组Trie树算法的词典不仅在查询速度上优于用其他索引机制的词典,而且存储数据的空间占用也比较小。     

基于DPSO-AO*算法系统测试序列优化问题研究

为了使复杂装备信息处理系统在进行故障定位过程中耗时最少、成本最低,建立了系统测试序列优化问题的数学模型。基于DPSO-AO^*算法的改进,得到信息处理系统的最优测试策略决策树,根据信息处理系统的相关矩阵,按故障概率,随机生成故障,采用相应的测试序列进行测试,最后利用累计测试费用进行比较,从而证明了改进的DPSO-AO^*算法正确有效。     

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.     

The hB^\Pi-tree: a multi-attribute index supporting concurrency, recovery and node consolidation

We propose a new multi-attribute index. Our approach combines the hB-tree, a multi-attribute index, and the -tree, an abstract index which offers efficient concurrency and recovery methods. We call the resulting method the hB-tree. We describe several versions of the hB-tree, each using a different node-splitting and index-term-posting algorithm. We also describe a new node deletion algorithm. We have implemented all the versions of the hB-tree. Our performance results show that even the version that offers no performance guarantees, actually performs very well in terms of storage utilization, index size (fan-out), exact-match and range searching, under various data types and distributions. We have also shown that our index is fairly insensitive to increases in dimension. Thus, it is suitable for indexing high-dimensional applications. This property and the fact that all our versions of the hB-tree can use the -tree concurrency and recovery algorithms make the hB-tree a promising candidate for inclusion in a general-purpose DBMS. Edited by R. Sacks-Davis. Received 27 June 1994 / Accepted 26 September 1995     

A new indexing method with high storage utilization and retrieval efficiency for large spatial databases

Storing and querying high-dimensional data are important problems in designing an information retrieval system. Two crucial issues, time and space efficiencies, must be considered when evaluating the performance of such a system. The KDB-tree and its variants have been reported to have good performance by using them as the index structure for retrieving multidimensional data. However, they all suffer from low storage utilization problem caused by imperfect “splitting policies.” Unnecessary splits increase the size of the index structure and deteriorate the performance of the system. In this paper, a new data insertion algorithm with a better splitting policy was proposed, which arranges data entries in the leaf nodes as many as possible. Our new index scheme can increase the storage utilization up to nearly 100% and reduce the index size to a smaller scale. As a result, both time and space efficiencies are significantly improved. Analytical and experimental results show that our indexing method outperforms the traditional KDB-tree and its variants.     

Enhancing the B-tree by dynamic node popularity caching

We propose the Cache Coherent B⁺-tree (CCB⁺-tree), an indexing structure that can improve search performance compared to the traditional B⁺-tree. The CCB⁺-tree makes use of the unused space in the internal nodes of a B⁺-tree to cache frequently queried leaf node addresses, thus saving node accesses. An experimental study shows that the CCB⁺-tree can outperform the traditional B⁺-tree on workloads where certain queries are much more popular than the others.     

TheSB-tree an index-sequential structure for high-performance sequential access

A variant of aB-tree known as anSB-tree is introduced, with the object of offering high-performance sequential disk access for long range retrievals. The key to this efficiency is a structure that supports multi-page reads (or writes) during sequential access to any node level below the root, even following significant node splitting. In addition, theSB-tree will support a policy to stripe successive multi-page blocks on multiple disks to achieve maximum parallelism. Compared to traditionalB-tree structures,SB-tree performance characteristics are less subject to degradation resulting from modifications entailed in growing and shrinking;SB-trees are therefore more appropriate for use in situations where frequent reorganization is not possible. A performance analysis reveals the strengths of theSB-tree by comparing its performance under various circumstances to theB ⁺-tree and the bounded disorder (BD) file of [11]. The performance analysis formulates a new useful concept, the effective depth of anSB- orB ⁺-tree, defined as the expected number of pages read from disk to perform a random retrieval search given standard buffering behavior. A graph of effective depth against tree size is shown to have a scalloped appearance, reflecting the changing effectiveness of incremental additions to buffer space.     

基于分布式内存数据库的移动对象全时态索引

针对现有移动索引仅对内存/磁盘两层结构进行优化,忽略了索引节点在内存中的缓存敏感性,提出一种基于分布式内存数据库的全时态索引结构DFTB^x树。该索引结构针对存储器Cache、内存和磁盘3层结构进行优化,根据Cache行、指令数量和TLB失配数等多个条件设计内存索引节点的大小。同时,根据磁盘数据页的大小设计历史数据迁移链节点的大小,使得Cache和内存能够一次读取索引节点和迁移链节点数据,避免多次读取数据带来的延迟。此外,构建历史数据迁移链,实现历史数据持久化,从而支持移动对象全时态索引。实验结果表明:与B^x树、B^dual树、TPR*树和STRIPES算法相比,DFTB^x树具有较高的查询和更新效率。     

Using compressed index structures for processing moving objects in large spatio-temporal databases

This paper develops a novel, compressed B⁺-tree based indexing scheme that supports the processing of moving objects in one-, two-, and multi- dimensional spaces. The past, current, and anticipated future trajectories of movements are fully indexed and well organized. No parameterized functions and geometric representations are introduced in our data model so that update operations are not required and the maintenance of index structures can be accomplished by basic insertion and deletion operations. The proposed method has two contributions. First, the spatial and temporal attributes of trajectories are accurately preserved and well organized into compact index structures with very efficient memory space utilization and storage requirement. Second, index maintenance overheads are more economical and query performance is more responsive than those of conventional methods. Both analytical and empirical studies show that our proposed indexing scheme outperforms the TPR-tree.     

Indexing high-dimensional data for main-memory similarity search

As RAM gets cheaper and larger, in-memory processing of data becomes increasingly affordable. In this paper, we propose a novel index structure, the CSR⁺-tree, to support efficient high-dimensional similarity search in main memory. We introduce quantized bounding spheres (QBSs) that approximate bounding spheres (BSs) or data points. We analyze the respective pros and cons of both QBSs and the previously proposed quantized bounding rectangles (QBRs), and take the best of both worlds by carefully incorporating both of them into the CSR⁺-tree. We further propose a novel distance computation scheme that eliminates the need for decompressing QBSs or QBRs, which results in significant cost savings. We present an extensive experimental evaluation and analysis of the CSR⁺-tree, and compare its performance against that of other representative indexes in the literature. Our results show that the CSR⁺-tree consistently outperforms other index structures.     

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.     

Prefetching J<Superscript>+</Superscript>-Tree: A Cache-Optimized Main Memory Database Index Structure

As the speed gap between main memory and modern processors continues to widen, the cache behavior becomes more important for main memory database systems (MMDBs). Indexing technique is a key component of MMDBs. Unfortunately, the predominant indexes — B⁺-trees and T-trees — have been shown to utilize cache poorly, which triggers the development of many cache-conscious indexes, such as CSB⁺-trees and pB⁺-trees. Most of these cache-conscious indexes are variants of conventional B⁺-trees, and have better cache performance than B⁺-trees. In this paper, we develop a novel J ⁺ -tree index, inspired by the Judy structure which is an associative array data structure, and propose a more cache-optimized index — Prefetching J ⁺ -tree (pJ⁺-tree), which applies prefetching to J⁺-tree to accelerate range scan operations. The J⁺-tree stores all the keys in its leaf nodes and keeps the reference values of leaf nodes in a Judy structure, which makes J⁺-tree not only hold the advantages of Judy (such as fast single value search) but also outperform it in other aspects. For example, J⁺-trees can achieve better performance on range queries than Judy. The pJ⁺-tree index exploits prefetching techniques to further improve the cache behavior of J⁺-trees and yields a speedup of 2.0 on range scans. Compared with B⁺-trees, CSB⁺-trees, pB⁺-trees and T-trees, our extensive experimental study shows that pJ⁺-trees can provide better performance on both time (search, scan, update) and space aspects.