基于B+树的多关键字密文排序检索方法

针对当前社会信息量大、隐私信息需要加密存储带来的检索难度大等问题,研究分析了现有的基于密文的可搜索加密方案,提出了一种基于B⁺树的多关键字密文排序方法。利用向量模型构建索引和查询陷门,根据相关性分数和关键字匹配度对检索结果进行排序。在真实数据集上进行了实验,结果证明本方案具有较高的检索效率。     

e—B＋树：面向多用户数据库系统优化的索引技术

B^＋树在数据库系统中已成为一种标准的索引结构，其上的并发控制机制对多用户数据库系统的性能有很大的影响．本文提出了一种变种B^＋树──弹性B^＋树－ｅ—B^＋树（ｅｌａｓｔｉｃＢ＋－ｔｒｅｅ），定义了其上的安全点和操作及并发控制方法，对ｅ—B^＋树的重构时机也进行了调整，降低了ｅ—B^＋树上结点的合并／分裂频率，减少了ｅ—B^＋树的维护开销，也缩短了封锁时间，从而使得其上操作的并发度和系统的效率得以提高．     

深海适冷菌Pseudoalteromonas sp. SM9913胞外多糖对Pb2+和Cu2+的吸附性能研究

采用深海适冷菌Pseudoalteromonas sp. SM9913分泌的胞外多糖（EPS）分别对Pb²⁺和Cu²⁺进行吸附,研究了多糖用量、pH、吸附时间和共存离子对EPS吸附性能的影响及EPS对Pb²⁺和Cu²⁺的吸附热力学.结果表明,EPS对Pb²⁺和Cu²⁺的吸附量随EPS投加量的增加而减小.EPS对Pb²⁺和Cu²⁺的最佳吸附pH分别为4.5～5.5和4.5～6.0. EPS对Cu²⁺的吸附平衡时间为90　min,对Pb²⁺的吸附平衡时间则长达180　min.共存离子Ca²⁺、Mg²⁺、Na⁺、K⁺的加入均降低了EPS对Pb²⁺的吸附量,Ca²⁺、Mg²⁺的加入降低了EPS对Cu²⁺的吸附量,但低浓度的Na⁺和实验范围浓度的K⁺不仅没有降低反而增加了EPS对Cu²⁺的吸附量.Freundlich和Dubinin-Radushkevich方程均能较好地描述SM9913胞外多糖吸附Pb²⁺和Cu²⁺的热力学过程,由Dubinin-Radushkevich方程得到SM9913胞外多糖对Pb²⁺和Cu²⁺的最大吸附量分别为243.3 mg/g (10℃) 和36.7 mg/g (40℃).胞外多糖吸附金属离子前后的红外光谱分析表明,多聚糖中C—O—C、乙酰基和羟基是起主要吸附作用的官能团.     

非等间隔GM(1, 1, ????) 幂次时间项模型及其应用

GM(1, 1, ??^??) 幂次时间项模型是灰色GM(1, 1) 模型的推广. 在灰色GM(1, 1) 模型和等间隔GM(1, 1, ??^??) 幂次时间项模型的基础上提出非等间隔GM(1, 1, ??^??) 幂次时间项模型, 并对模型进行求解. 讨论了GM(1, 1, ????) 幂次时间项模型的曲线形状、发展系数以及幂指数间的关系, 研究了非等间隔GM(1, 1, ??^??) 幂次时间项模型的参数空间. 将平均相对误差看成幂指数的函数, 根据序列形状判断幂指数的范围, 并利用粒子群算法求解幂指数. 实际应用验证了所提出模型的有效性.

     

A*算法改进算法及其应用

路径优化问题是现代生活和工作中的一个重要而复杂的问题,路径优化算法则是解决路径优化问题并推广应用路径优化问题的关键。在回顾Dijkstra算法和A^*算法的基础上,提出了A^*改进算法,并结合例子对算法求解过程进行说明。最后编程实现了Dijkstra算法、A^*算法和A^*改进算法,并对运行结果进行比较分析。     

一种基于域知识的协议状态机主动推断算法

现有基于L*算法的协议状态机主动推断方法忽略了协议特有的域知识,将协议报文抽象为相互独立、无意义的符号,并完全随机地生成测试样本进行状态机等价判定,导致产生大量的无效询问和测试样本,在真实网络环境下推断效率较低。在L⁺M算法的基础上提出了一种基于域知识的协议状态机主动推断算法L⁺N,其改进主要体现在:依据会话样本集提取各报文之间的强顺序约束关系来过滤无效的输出询问,构建会话样本集对应的扩展前缀树接受器(Extended Prefix Tree Accepter,EPTA)对输出询问进行预响应,提出了一种基于正例样本变异的等价询问近似判定算法以提升寻找反例的效率。实验结果表明,L⁺N算法能够大幅提高推断效率,并且具有与L⁺M算法相同的推断准确度。     

NF2关系模型与属性继承

将面向对象模型与面向值的模型相结合,可兼得二者的优点．本文基于具有组合元组标识的ＮＦ²关系模型给出了强调嵌套结构特征的关系运算投影II^s和自然连接,并在此基础上研究ＮＦ²关系数据模型中的类、子类及其间的属性继承的语义及形式化定义;引入了Ｓ－子类的概念;研究了公共子类及多属性继承的特征．所有的讨论基于组合元组标识,具有较强的面向对象模型的特征．     

基于DS-Adaboost算法的人脸检测

针对连续Adaboost算法中平滑因子选取的不足,提出了一种动态选取平滑因子的DS-Adaboost算法,该算法对弱分类器输出中的平滑因子ε进行了动态选取,根据W^j₊₁W^j－1比值的大小动态地选择平滑因子,当W^j₊₁W^j－1>1时,ε_j=W^j₊₁,当0j₊₁W^j－1<1时,ε_j=W^j－1。实验表明,DS-Adaboost算法能较好地起到平滑的作用,使得落在同一个区间里面的正样本和负样本的比例都在可以比拟的范围内。     

在消息传递并行机上的高效的最小生成树算法

基于传统的Borǔ vka串行最小生成树算法,提出了一个在消息传递并行机上的高效的最小生成树算法.并且采用3种方法来提高该算法的效率,即通过两趟合并及打包收缩的方法来减少通信开销,通过平衡数据分布的办法使各个处理器的计算量平衡.该算法的计算和通信复杂度分别为O(n²/p)和O((t_sp+t_wn)n/p).在曙光-1000并行机上运行的实际效果是,对于有10 000个顶点的稀疏图,通过16个节点的运行加速比是12.     

基于标记依赖关系集成分类器链的多示例多标签支持向量机算法

ECC-MIMLSVM⁺是多示例多标签学习框架下一种算法,该算法提出了一种基于分类器链的方法,但其没有充分考虑到标签之间的依赖关系,而且当标签数目的增多,子分类器链长度增加,使得误差传播问题凸显. 因此针对此问题,提出了一种改进算法,将ECC-MIMLSVM⁺算法和标签依赖关系相结合,设计成基于标记依赖关系集成分类器链（ELDCT-MIMLSVM⁺）来加强标签间信息联系,避免信息丢失,提高分类的准确率. 通过实验将本文算法与其他算法进行了对比,实验结果显示,本文算法取得了良好的效果.     

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.     

Performance of B+ tree concurrency control algorithms

A number of algorithms have been proposed to access B⁺-trees concurrently, but they are not well understood. In this article, we study the performance of various B⁺-tree concurrency control algorithms using a detailed simulation model of B⁺-tree operations in a centralized DBMS. Our study covers a wide range of data contention situations and resource conditions. In addition, based on the performance of the set of B⁺-tree concurrency control algorithms, which includes one new algorithm, we make projections regarding the performance of other algorithms in the literature. Our results indicate that algorithms with updaters that lock-couple using exclusive locks perform poorly as compared to those that permit more optimistic index descents. In particular, the B-link algorithms are seen to provide the most concurrency and the best overall performance. Finally, we demonstrate the need for a highly concurrent long-term lock holding strategy to obtain the full benefits of a highly concurrent algorithm for index operations.     

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.     

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.     

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.     

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.     

The <Emphasis Type="Italic">B</Emphasis><Superscript><Emphasis Type="Italic">dual</Emphasis></Superscript>-Tree: indexing moving objects by space filling curves in the dual space

Existing spatiotemporal indexes suffer from either large update cost or poor query performance, except for the B ^x -tree (the state-of-the-art), which consists of multiple B ⁺-trees indexing the 1D values transformed from the (multi-dimensional) moving objects based on a space filling curve (Hilbert, in particular). This curve, however, does not consider object velocities, and as a result, query processing with a B ^x -tree retrieves a large number of false hits, which seriously compromises its efficiency. It is natural to wonder “can we obtain better performance by capturing also the velocity information, using a Hilbert curve of a higher dimensionality?”. This paper provides a positive answer by developing the B ^dual -tree, a novel spatiotemporal access method leveraging pure relational methodology. We show, with theoretical evidence, that the B ^dual -tree indeed outperforms the B ^x -tree in most circum- stances. Furthermore, our technique can effectively answer progressive spatiotemporal queries, which are poorly supported by B ^x -trees.     

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.     

多维索引hB树的改进方法——hB*树

本文在hB树基础上提出多属性索引方法——hB^*树.hB*树索引结点溢出时先寻求避免分裂,以期得到较好的空间利用率;通过避免和消除多父结点,使hB^*树成为严格的树形结构.本文表明hB^*树提高了空间利用率,树形化的代价也不高.