基于查询片段松弛的XML小枝近似查询方法

为了解决普通用户对XML文档的近似查询问题,提出一种基于查询片段松弛的XML小枝近似查询方法.该方法利用查询历史推测用户偏好,进而根据用户偏好为原始小枝查询中的每个查询片段分配重要程度,然后基于查询片段重要程度对原始小枝查询条件进行松弛处理,最不重要的查询片段最先松弛,从而确保获取最为相关的查询结果;最后,根据对原始查询和用户偏好的满足程度,将得到的满足松弛查询条件的结果进行排序.实验结果表明,本文提出的查询松弛和结果排序方法能够获得较高的查全率和准确率,并且能较好地满足用户需求和偏好.     

一种基于XML小枝查询片段松弛的近似查询与结果排序方法

提出了一种基于XML小枝查询片段松弛的近似查询与结果排序方法来实现用户在XML文档中的近似查询:通过收集用户的查询历史来推测用户偏好,并以此计算原始小枝查询分解得到的查询片段的重要程度,然后按照重要程度的排序进行查询松弛;在松弛方法中,根据查询片段数目的不同采用不同的松弛方法,若片段数目较多则以查询片段为粒度对其松弛,较少则以查询结点为粒度对数值查询与非数值查询采用不同的方法进行松弛,得到最为相关的近似查询结果;最后按近似查询结果对原始查询和用户偏好的满足程度进行排序并输出。实验证明,该近似查询方法能够较好地满足用户的需求和偏好,具有较高的查全率和准确率。     

一种基于结构特征的XML数据查询方法

针对XML数据特有的树型结构模式,提出了一种将树型结构的XML数据和查询语句转化为特定格式的字符串,基于串匹配原理对结构复杂的XML数据进行查询的方法,避免了传统的基于路径的查询方式所必需的路径之间的连接（join）操作,从而提高查询效率。利用本文提出的编码方式,可以建立关于XML数据结构和数据内容舍为一体的索引。实验显示,本文使用的针对XML数据查询的方法比传统的基于连接操作的数据查询方式高效,且本方法具有良好的扩展性。     

模糊XML关键字近似查询方法研究

在精确XML文档上的关键字查询方法的研究大多是基于LCA语义或者其变种语义(SLCA,ELCA等)开展的,将包含所有关键字的最紧致XML子树片段作为查询结果返回。但是这些基于LCA语义产生的查询结果中通常包含了大量的冗余信息,现实世界中存在着大量的不确定和模糊信息,因而如何从模糊XML文档中搜索到高质量的关键字查询结果是一个需要研究的问题。针对模糊XML文档上的关键字近似查询方法进行研究,通过引入最小连接树(MCT)的概念,提出在模糊XML文档上关键字查询的所有GDMCTs问题,并给出解决这一问题的基于栈的算法All fuzzy GDMCTs,该算法可以得到满足用户指定的子树大小阈值和可能性阈值条件的所有GDMCTs结果。实验表明,该算法在模糊XML文档上能够得到较高质量的关键字查询结果。     

一种标签劣质XML数据上的twig查询处理的优化

XML数据中的不正确数据、不一致数据、不精确数据等劣质数据给XML.数据上的有效查询处理带来了挑战.专注于研究标签劣质的XML数据上twig查询处理的优化方法,文中分别给出了优化方法的原理、伪代码、正确性证明和复杂度分析,并通过例子加以解释.通过实验验证了优化方法的效率.     

一种高效的XML多分支路径查询算法

目前XML单路径查询和简单的分支路径查询已经得到了较好的解决,但如何高效地实现XML多分支路径查询还没有很好的方法。提出一种高效的XML多分支查询算法MBPQ。算法MBPQ首先对XML文档和被查询的多分支路径结点分别按照各自不同的方式进行编码,并将被查询的多分支路径拆分成单路径,最后将单路径查询匹配成多分支查询结果。在单路径查询结果匹配过程中,算法MBPQ利用栈控制匹配过程,按照查询树从左到右、自底向上的顺序匹配具有共同祖先结点的单路径查询结果,从而提高匹配效率。实验表明,与现有的XML多分支查询一般算法相比,算法MBPQ的查询效率高。     

一种基于流索引的XML数据流的Xpath查询算法

XML数据流查询过程中,核心操作是父子、祖孙节点的匹配问题.解决这一问题的有效途径是根据查询表达式构造非确定状态自动机,顺序处理解析后的节点,在这个过程中大量与查询无关的节点也参与了匹配.通过对XML数据流添加流索引,在执行查询时,直接跳过与查询不匹配的元素及其子树节点,提高了查询效率.     

一种基于路径分块索引的XML查询方法

针对XML的相对路径查询及引用路径查询问题,提出了一种面向XML数据的路径分块索引KI。探讨了KI索引构造方法、索引节点分裂算法和相关查询处理的算法,并用VC++实现,利用Shakespeare和Xorder数据集进行了XML查询测试,实验结果表明,提出的KI索引能有效地提高XML查询效率。     

一种基于XML文档聚类的XML近似查询算法

提出了一种基于XML文档聚类的XML近似查询算法。给出了基于语义的XML文档间距离的计算方法,结合该语义距离,提出了基于网格的八邻域聚类算法对XML数据库进行聚类划分,进而利用在聚类过程中得到的聚类中心对静态有序选择算法的近似查询评估阶段进行优化,使得不用对XML数据库进行完全遍历就能及时返回满足用户需要的查询结果。最后,在汽车外形智能化设计的实验中表明该算法有效地提高了静态有序选择算法的查询效率。     

一种高效的XML路径查询索引

XML文档的查询索引是当前研究的热点。提出一种高效的XML路径查询索引KDXI,首先对XML文档进行编码,然后建立结构索引并对结构索引进行编码。研究了基于KDXI索引结构的半结构连接算法和路径查询处理过程。通过KDXI索引机制,可以有效执行一般的路径查询语句,并避免冗余的结构连接操作。实验证明了KDXI索引机制的优越性。     

一种基于树状逻辑式的XML查询方法*

随着XML在各类数据表示中的广泛应用,XML文档的查询方法成为当前数据处理的一个重要问题。已有的研究如XPath和XQuery得到应用,但其查询表现存在一些缺点需要改进。从模板匹配的角度出发,基于谓词逻辑设计了树状逻辑表达式LXT。该表达式的特点是能以树状结构模板描述XML文档查询需求。在此基础上,进一步探讨了基于LXT表达式的XML文档查询方法,从数据的查询过滤、结果的组织构造与含义等方面描述了利用LXT的特点设计XML查询表现形式的基本思路。     

Intelligent query answering by knowledge discovery techniques

Knowledge discovery facilitates querying database knowledge and intelligent query answering in database systems. We investigate the application of discovered knowledge, concept hierarchies, and knowledge discovery tools for intelligent query answering in database systems. A knowledge-rich data model is constructed to incorporate discovered knowledge and knowledge discovery tools. Queries are classified into data queries and knowledge queries. Both types of queries can be answered directly by simple retrieval or intelligently by analyzing the intent of query and providing generalized, neighborhood or associated information using stored or discovered knowledge. Techniques have been developed for intelligent query answering using discovered knowledge and/or knowledge discovery tools, which includes generalization, data summarization, concept clustering, rule discovery, query rewriting, deduction, lazy evaluation, application of multiple-layered databases, etc. Our study shows that knowledge discovery substantially broadens the spectrum of intelligent query answering and may have deep implications on query answering in data- and knowledge-base systems     

基于领域本体知识库的语义查询扩展

为了在检索过程中全面表达用户查询意图,提出了基于领域本体知识库的语义查询扩展方法。该方法借助领域本体推理出的知识,使检索系统从语义层面理解用户查询语句,并通过语义相似度来控制扩展词的规模,避免了查询过度扩展,使得新构造的查询能更准确地描述用户的检索需求,提高了检索的有效性。原型系统的实验结果表明,该方法较传统的关键字匹配法和LAC方法有明显的优势,在保障查全率的基础上,可极大地提高检索准确率。     

A structured approach for cooperative query answering

This paper proposes the use of a type abstraction hierarchy as a framework for deriving cooperative query answers. The type abstraction hierarchy integrates the abstraction view with the subsumption (is-a) and composition (part-of) views of a type hierarchy. Such a framework provides multilevel object representation, which is an important aspect of cooperative query answering. The concept of pattern that specifies one or more conditions on an object is also proposed. Patterns have smaller granularity than types, and thus provide more specific semantic information. Cooperative query answering consists of query relaxation, generalization, specialization, and association on patterns. Query relaxation can be explicitly specified by the user or implicitly performed by the system. The implicit and explicit relaxations can also be combined and performed interactively by both the system and the user. CSQL, an extension of SQL for cooperative query answering, is also proposed. Preliminary experimental results reveal that the proposed type abstraction hierarchy provides an organized structure representing concepts at different knowledge levels in various domains, and provides a systematic and efficient method for cooperative query answering     

Intelligent query answering in rule based systems

We propose that in large knowledge bases which are collections of atomic facts and general rules (Horn clauses), the rules should be allowed to occur in the answer for a query. We introduce a new concept of the answer for a query which includes both atomic facts and general rules. We provide a method of transforming rules by relational algebra expressions built from projection, join, and selection and demonstrate how the answers consisting of both facts and general rules can be generated.     

A knowledge infrastructure for intelligent query answering in location-based services

Intelligent query answering in Location-based Services refers to their capability to provide mobile users with personalized and contextualized answers. Personalization is expected to lead to answers that better match user’s interests, as inferable from the user’s profile. Contextualization aims at not selecting answers that for some reason would not be appropriate at the time and place of the user query. These goals are beyond the current state of art in LBS, or are provided based on ad hoc solutions specific to the application at hand. This paper reports on the results of an investigation aiming at defining the knowledge infrastructure that should be developed within the LBS to make it capable of returning intelligent answers. We first discuss the data management features that make LBS different from other query answering systems. Next we propose a data infrastructure that builds on the idea of modular ontologies. We explain how the relevant knowledge may be incrementally set up and dynamically maintained based on an application-independent approach. Last we show how this knowledge is used to reformulate user’s queries via personalized and contextualized rewriting.     

基于空值修复的数据库一致性查询方法*

完整性约束常用来定义数据库的数据语义,违反约束的数据库实例为不一致数据库,返回含有不一致结果的查询称为不一致查询。一致性查询目的在于不修改数据库实例而从不一致数据库获取满足约束的查询结果,已有方法因其支持的约束类型有限或计算复杂度高而影响其应用范围。提出了一种基于空值修复的数据库一致性查询方法,首先将原始完整性约束转换为与查询相关的统一约束,然后根据统一约束对原SQL查询进行查询重写,重写后的查询将不一致属性值当做空值来处理以获得满足完整性约束的结果。系统实现与实验证明,该方法在多种完整性约束类型与SQL     

一种基于关键字的XML文档查询算法

对XML文档查询的常用方法有两种：一种是使用查询语言;另一种是使用关键字,而使用关键字查询XML文档比使用查询语言更为简单方便。给出了一种使用关键字查询XML文档的索引查找算法。该算法只需要扫描一次关键字对应的编码列,就可以找到需要的编码,提高了查询效率。实验表明该算法是可行的和有效的。     

一种基于编辑距离的XML查询方案

提出了一种新的XML查询方案,将内容查询置于结构查询之前,并在结构查询中提出M集合的概念,通过计算查询树的M集合和简化后的DTD的M集合之间的编辑距离,对XML文档进行排序,不仅减少了查询时间,而且在保证查全率的同时提高了查询的查准率。     

Approximating query answering on RDF databases

Database users may be frustrated by no answers returned when they pose a query on the database. In this paper, we study the problem of relaxing queries on RDF databases in order to acquire approximate answers. We address two problems in efficient query relaxation. First, to ensure the quality of answers, we compute the similarities between relaxed queries with regard to the user query and use them to score the potential relevant answers. Second, for obtaining top-k answers, we develop two algorithms. One is based on the best-first strategy and relaxed queries are executed in the ranking order. The batch based algorithm executes the relaxed queries as a batch and avoids unnecessary execution cost. At last, we implement and experimentally evaluate our approaches.