Database management and analysis tools of machine induction

This paper surveys machine induction techniques for database management and analysis. Our premise is that machine induction facilitates an evolution from relatively unstructured data stores to efficient and correct database implementations.     

Locality-preserving hash functions for general purpose parallel computation

Consider the problem of efficiently simulating the shared-memory parallel random access machine (PRAM) model on massively parallel architectures with physically distributed memory. To prevent network congestion and memory bank contention, it may be advantageous to hash the shared memory address space. The decision on whether or not to use hashing depends on (1) the communication latency in the network and (2) the locality of memory accesses in the algorithm.We relate this decision directly to algorithmic issues by studying the complexity of hashing in the Block PRAM model of Aggarwal, Chandra, and Snir, a shared-memory model of parallel computation which accounts for communication locality. For this model, we exhibit a universal family of hash functions having optimal locality. The complexity of applying these hash functions to the shared address space of the Block PRAM (i.e., by permuting data elements) is asymptotically equivalent to the complexity of performing a square matrix transpose, and this result is best possible for all pairwise independent universal hash families. These complexity bounds provide theoretical evidence that hashing and randomized routing need not destroy communication locality, addressing an open question of Valiant.This work was started when the author was a student at Oxford University, supported by a National Science Foundation Graduate Fellowship and a Rhodes Scholarship. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the author and do not necessarily reflect the views of the National Science Foundation or the Rhodes Trust.     

A function for generating nearly balanced binary search trees from sets of non-random keys

A function for generating nearly balanced binary search trees from sets of non-random keys is described. This function can be used in hashing organization where collisions are resolved by construction of binary trees. In particular it can be used as the secondary function in the relatively new technique of dynamic hashing.     

一种基于DHT的动态网格命名解析方案

在分布异构的网格环境下，如何有效地查找网格资源是一个必须解决的问题，网格命名协议是利用全局统一的命名空间来发现网格资源的。该文在网格命名协议中采用了一种查找路由算法DHT的解决方案，可解决网格命名解析服务的可扩展性和灵活性。     

基于兴趣点的图像检索

在基于内容的图像检索中,往往使用颜色、纹理以及形状的全局特征来描述图像,然而全局特征不能描述图像的细节,丢失了图像的空间信息。文章利用兴趣点来灵活描述图像的局部信息,提取兴趣点周围的颜色矩作为局部特征,通过兴趣点的匹配和带权投票来进行相似度量,几何哈希技术的使用增强了兴趣点间的正确匹配。实验证明了这种方法的有效性,具有旋转、平移和部分的尺度不变性。     

基于LSI和SVM分类法的定题邮件过滤研究

潜在语义索引(LSI)是一种有效的信息查询方法,同时也被成功地应用到了文本分类中。LSI能解决同义和多义的问题,通过降低原始文档-术语矩阵的噪声来凸现出词条和文档之间的语义关系。为了识别和过滤有害的、不期望的定题的信息或Email,在双语言环境下(包括中文和英文),提出了一个基于改进的LSI方法的定题邮件类信息过滤系统,该系统采用潜在语义模型来表示被过滤的信息类,通过奇异值分解和正例监护学习方法,选择支持向量机(SVM)来识别和分类预定义的定题信息。实验结果表明:基于LSI的特征选择的SVM分类算法是一种更有效的信息识别和文本分类方法,不但具有较好的分类性能,同时也能大大减小计算的复杂性。     

时态数据的可变Hash索引

索引技术是时态数据库查询优化的重要方法之一。本文提出的可变Hash（VH）索引是建立在时间属性上的一种新的动态索引技术，主要目的是提高时态数据库快照查询的效率。由于时间的不确定性，在时态数据的时间属性上建立Hash索引比较困难。VH索引克服了Hash索引这一难点，提出了索引参数可变的思想，并应用B^＋-树对Hash参数进行组织。查询时由时间值在B^＋-树上获得Hash参数，进而确定数据的存储地址。通过对其时间复杂度和空间复杂度的理论分析以及实验验证，表明该索引技术可以减少索引查找以及读取数据的I／O次数，并具有理想的空间利用率。     

基于自适应最近邻的局部线性嵌入算法

局部线性嵌入算法是一个优异的非线性维数约减方法，但是算法本身是一个无监督学习算法，对于有监督问题的学习效果不是很好。这主要是因为算法使用了K-近邻方法来求解最近邻点。针对这个缺点，提出了一种改进的、基于自适应最近邻法的局部线性嵌入方法，数值实验证明算法对于有监督的学习问题，具有较好的适应性。     

KSLPP：新的人脸识别算法

针对人脸识别中的特征提取问题，提出了一种新的核有监督保局投影人脸识别算法,即KSLPP.该算法通过非线性映射将人脸样本投影到高维空间，通过可调因子有效地结合人脸局部流形的结构信息和样本的类别信息，提取人脸的非线性特征.采用最小近邻分类器估算识别率.采用AT&T人脸库以及Yale人脸库，对该方法进行了测试.结果表明，与Eigenface、Fisherface以及Laplacianface等方法相比，该方法具有较好的识别率.