基于内容的发布订阅系统中快速匹配算法的研究

高效的匹配算法是大规模基于内容的发布订阅系统所要研究的关键问题之一．建立了订阅语言和事件模型，提出了一个高效、实用的基于内容的匹配算法，它充分利用了多维索引技术加速查询，利用约束间的覆盖关系减少重复匹配．实验表明其效率比其它常用算法具有较大提高，并且扩展性好，适合应用于大规模分布式基于内容的发布订阅系统中．     

一种基于词汇链的关键词抽取方法

关键词在文献检索、自动文摘、文本聚类/分类等方面有十分重要的作用。词汇链是由一系列词义相关的词语组成,最初被用于分析文本的结构。本文提出了利用词汇链进行中文文本关键词自动标引的方法,并给出了利用《知网》为知识库构建词汇链的算法。通过计算词义相似度首先构建词汇链,然后结合词频与区域特征进行关键词选择。该方法考虑了词汇之间的语义信息,能够改善关键词标引的性能。实验结果表明,与单纯的词频、区域方法相比,召回率提高了7.78%,准确率提高了9.33%。     

潜在语义索引在文本分类中的应用

传统的文本分类都是根据文本的外在特征进行的，最常见的就是基于向量空间模型的方法，使用空间向量表示文本，通过相似度比较来确定分类。为了克服向量空间模型中的词条独立性假设，文章提出了一种基于潜在语义索引的文本分类模型，通过对大量的文本集进行统计分析，揭示了词语的上下文使用含义，通过奇异值分解有效地降低了向量空间的维数，消除了同义词、多义词的影响，从而提高了文本分类的精度。     

Fast Term Indexing with Coded Context Trees

Indexing data structures have a crucial impact on the performance of automated theorem provers. Examples are discrimination trees, which are like tries where terms are seen as strings and common prefixes are shared, and substitution trees, where terms keep their tree structure and all common contexts can be shared. Here we describe a new indexing data structure, called context trees, where, by means of a limited kind of context variables, common subterms also can be shared, even if they occur below different function symbols. Apart from introducing the concept, we also provide evidence for its practical value. We show how context trees can be implemented by means of abstract machine instructions. Experiments with benchmarks for forward matching show that our implementation is competitive with tightly coded current state-of-the-art implementations of the other main techniques. In particular, space consumption of context trees is significantly less than for other index structures.     

The Active Vertice method: a performant filtering approach to high-dimensional indexing

The problem of finding nearest neighbors has emerged as an important foundation of feature-based similarity search in multimedia databases. Most spatial index structures based on the R-tree have failed to efficiently support nearest neighbor search in arbitrarily distributed high-dimensional data sets. In contrast, the so-called filtering principle as represented by the popular VA-file has turned out to be a more promising approach. Query processing is based on a flat file of compact vector approximations. In a first stage, those approximations are sequentially scanned and filtered so that in a second stage the nearest neighbors can be determined from a relatively small fraction of the data set.

In this paper, we propose the Active Vertice method as a novel filtering approach. As opposed to the VA-file, approximation regions are arranged in a quad-tree like structure. High-dimensional feature vectors are assigned to ellipsoidal approximation regions on different levels of the tree. A compact approximation of a vector corresponds to the path within the index from the root to the respective tree node. When compared to the VA-file, our method enhances the discriminatory power of the approximations while maintaining their compactness in terms of storage consumption. To demonstrate its effectiveness, we conduct extensive experiments with synthetic as well as real-life data and show the superiority of our method over existing filtering approaches.     

基于内容的图像检索探讨

介绍了基于内容图像检索研究的发展现状,分析了基于内容的图像检索中特征提取、相关反馈及高维度索引的研究成果,提出了需进一步研究的问题.     

逐维聚类的相似度索引算法

随着多媒体信息技术的迅速发展，多维度索引技术在图像、视频等可视信息的存储、检索方面成为一个重要的研究领域，针对“维数危机”难题，提出逐维聚类相似度索引算法，该算法根据数据集的分布特性，对特征矢量的每一维进行聚类，算法在实现检索时可以逐步滤除与查询矢量不相似的数据集，缩小检索范围，进而提高了检索速度，实验结果表明，逐维聚类算法适用于基于相似度的高维数据矢量检索和查询，是一种简单、灵活的索引结构。     

ChIN化学化工资源导航系统的新进展

Internet化学化工资源导航系统ChIN已经被公认为国内最有影响的Internet化学化工资源导航站点,也受到国际同行的肯定和关注。本文从ChIN的最新版本及其专门管理工具ChIN-Manager,基于概念的自然语言检索方法一隐含语义检索ISI应用于ChIN的研究、将ChIN无缝集成到化学化工虚拟社区平台的尝试3个方面来介绍ChIN在最近两年内的新进展。     

半结构化数据相似搜索的索引技术研究

为了在海量、高维、动态的半结构化数据集上进行有效的相似搜索，该文提出一种采用聚类技术进行索引构建与更新的多路平衡树－－CSS－树以及基于CSS－树的相似搜索与动态更新的算法。CSS－树借鉴SS^ -树基于聚类进行节点组织与分裂的基本思想，避免了根据坐标准进行分裂时所要求的维不相关性，同时在节点组织、分裂算法和搜索算法等方面进行了改进，提出了新的搜索剪枝策略，实验表明，该结构及算法对海量半结构化数据相似搜索和效率明显优于传统算法。     

空间索引技术-回顾与展望

空间数据库系统通过引入空间索引机制来提高空间数据操作的效率。迄今人们已经提出了许多空间索引方法,文章回顾了这些方法的基本思想,并根据它们所采用的基础数据结构和存储空间数据的方法将现有的空间索引方法进行分类。最后,简要的讨论了空间索引方法的发展方向。