关系数据库不可用空值的查询与处理

现有的基于关系数据模型的商业数据库采用空值对缺失信息进行建模与处理,然而,单一的空值解释无法体现空值本身的丰富语义。事实上,在相关研究中空值通常被解释为‘值未知’,‘值不可用’以及‘值不存在’等。文中主要研究不可用空值的查询与处理。通过仔细地观察和深刻地理解,分别在传统关系数据库查询和模糊数据库查询中讨论不同语义背景和查询条件下不可用空值的处理和分类。此外,还针对涉及不可用空值的传统关系数据库查询提出选择运算和差运算算法,这些算法使文中的研究更具实用性。     

含有空值关系数据库的查询处理

本文在深刻理解空值语义的基础上，给出一种处理占位型空值的方法，空值环境下关系数据库的查询策略，定义了含三种查询操作的关系代数最小完备集中的关系代数运算，并对查询计算的有效性和完备性进行了分析。     

空值环境下关系数据库查询处理方法

本文给出了一种处理占位型空值的简化方法，解决了空值环境下关系数据库的查询问题，针对不同的空值语义语义特点和查询中的作用，定义了三种不同的操作。ＤＥＦＩＮＩＴＥ，ＥＸＩＳＴ，ＭＡＹＢＥ。同时，给出了含有这三种操作的选择运算、集合差、一般连接运算的新定义。     

SQL中的空值探讨

文章介绍了空值的语义和三值逻辑,并在此基础上分析了SQL语言处理空值时存在的问题并对其进行修改,构造了针对不完全信息数据库的查询语言。     

关系模型中空值的引入与处理

随着数据库应用的不断扩展,对不完全信息的处理的需求日益突出出来,本文讨论了在数据库中引入空值的必要性和有关空值的若干语义问题,给出了一个增加了处理空值功能的扩充的关系模型,和若干现有的讨论文章相此,本模型力图最充分地表达空值本身的语义内涵,从这一基点出发,文章讨论了空值环境对若干关系操作的影响以及引入空值以后函数据关性的满足性问题,并且给出了在本文所提出的扩充关系模型下的某些新的定义。     

SQL语言中的空值问题

结构化查询语言(SQL)是关系数据库中重要的查询语言,同时由于关系数据库中空值存在的必要性,因此在对不完全信息数据库进行SQL操作时,如何正确地处理空值是一个很重要的研究方向.文中简述了SQL语言标准中对空值的描述,从理论角度分析了引入空值后SQL语言的语义变化,同时从应用角度阐述了引入空值后所引发的一系列问题,最后介绍了下一代数据库语言标准SQL3对NULL处理方法的扩充.由于现实数据库信息的不完全性,文中对于如何正确地认识SQL语言中的空值问题具有很大的借鉴意义.     

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     

SQL语言中的空值问题

结构化查询语言(SQL)是关系数据库中重要的查询语言，同时由于关系数据库中空值存在的必要性，因此在对不完全信息数据库进行SQL操作时，如何正确地处理空值是一个很重要的研究方向。文中简述了SQL语言标准中对空值的描述，从理论角度分析了引入空值后SQL语言的语义变化，同时从应用角度阐述了引入空值后所引发的一系列问题，最后介绍了下一代数据库语言标准SQL3对NULL处理方法的扩充。由于现实数据库信息的不完全性，文中对于如何正确地认识SOL语言中的空值问题具有很大的借鉴意义。     

模糊空值环境下关系模型的冗余处理

本文将模糊空值环境下的关系模型扩展为扩展关系模型,并借用模糊语义距离给出基本关系模型和扩展模型上冗余信息清除的基本方法。     

模糊空值环境下扩展关系模型的冗余处理

本文将模糊空值环境下的关系模型扩展为扩展关系模型,并借用模糊语义距离给出清除扩展模型上冗余信息的基本方法.     

Null values in fuzzy databases

Since in the real world, it often occurs that information is missing, database systems clearly need some facilities to deal with missing data. With respect to traditional database systems, the most commonly adopted approach to this problem is based on null values and three valued logic. This paper deals with the semantics and the use of null values in fuzzy databases. In dealing with missing information a distinction is made between incompleteness due to unavailability and incompleteness due to inapplicability. Both the database modelling and database querying aspects are described. With respect to attribute values, incompleteness due to unavailability is modelled by possibility distributions, which is a commonly used technique in the fuzzy databases. Domain specific null values, represented by a bottom symbol, are used to model incompleteness due to inapplicability. Extended possibilistic truth values are used to formalize the impact of data manipulation and (flexible) querying operations in the presence of these null values. The different cases of appearances of null values in the handling of selection conditions of flexible database queries are described in detail.     

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。     

On Similarity Measures for Multimedia Database Applications

A multimedia database query consists of a set of fuzzy and boolean (or crisp) predicates, constants, variables, and conjunction, disjunction, and negation operators. The fuzzy predicates are evaluated based on different media criteria, such as color, shape, layout, keyword. Since media-based evaluation yields similarity values, results to such a query is defined as an ordered set. Since many multimedia applications require partial matches, query results also include tuples which do not satisfy all predicates. Hence, any fuzzy semantics which extends the boolean semantics of conjunction in a straight forward manner may not be desirable for multimedia databases. In this paper, we focus on the problem of ‘given a multimedia query which consists of multiple fuzzy and crisp predicates, how to provide the user with a meaningful overall ranking.’ More specifically, we study the problem of merging similarity values in queries with multiple fuzzy predicates. We describe the essential multimedia retrieval semantics, compare these with the known approaches, and propose a semantics which captures the retrieval requirements in multimedia databases. Received 13 August 1999 / Revised 13 May 2000 / Accepted in revised form 26 July 2000     

Similarity-based ranking and query processing in multimedia databases

Since media-based evaluation yields similarity values, results to a multimedia database query, Q(Y₁,…,Y_n), is defined as an ordered list S_Q of n-tuples of the form X₁,…,X_n. The query Q itself is composed of a set of fuzzy and crisp predicates, constants, variables, and conjunction, disjunction, and negation operators. Since many multimedia applications require partial matches, S_Q includes results which do not satisfy all predicates. Due to the ranking and partial match requirements, traditional query processing techniques do not apply to multimedia databases. In this paper, we first focus on the problem of “given a multimedia query which consists of multiple fuzzy and crisp predicates, providing the user with a meaningful final ranking”. More specifically, we study the problem of merging similarity values in queries with multiple fuzzy predicates. We describe the essential multimedia retrieval semantics, compare these with the known approaches, and propose a semantics which captures the requirements of multimedia retrieval problem. We then build on these results in answering the related problem of “given a multimedia query which consists of multiple fuzzy and crisp predicates, finding an efficient way to process the query.” We develop an algorithm to efficiently process queries with unordered fuzzy predicates (sub-queries). Although this algorithm can work with different fuzzy semantics, it benefits from the statistical properties of the semantics proposed in this paper. We also present experimental results for evaluating the proposed algorithm in terms of quality of results and search space reduction.     

Efficient processing of nested Fuzzy SQL queries in a fuzzydatabase

In a fuzzy relational database where a relation is a fuzzy set of tuples and ill-known data are represented by possibility distributions, nested fuzzy queries can be expressed in the Fuzzy SQL language. Although it provides a very convenient way for users to express complex queries, a nested fuzzy query may be very inefficient to process with the naive evaluation method based on its semantics. In conventional databases, nested queries are unnested to improve the efficiency of their evaluation. In this paper, we extend the unnesting techniques to process several types of nested fuzzy queries. An extended merge-join is used to evaluate the unnested fuzzy queries. As shown by both theoretical analysis and experimental results, the unnesting techniques with the extended merge-join significantly improve the performance of evaluating nested fuzzy queries     

Full predicate coverage for testing SQL database queries

In the field of database applications a considerable part of the business logic is implemented using a semi‐declarative language: the Structured Query Language (SQL). Because of the different semantics of SQL compared with other procedural languages, the conventional coverage criteria for testing are not directly applicable. This paper presents a criterion specifically tailored for SQL queries (SQLFpc). It is based on Masking Modified Condition Decision Coverage (MCDC) or Full Predicate Coverage and takes into account a wide range of the syntax and semantics of SQL, including selection, joining, grouping, aggregations, subqueries, case expressions and null values. The criterion assesses the coverage of the test data in relation to the query that is executed and it is expressed as a set of rules that are automatically generated and efficiently evaluated against a test database. The use of the criterion is illustrated in a case study, which includes complex queries. Copyright © 2010 John Wiley & Sons, Ltd.     

Efficient fuzzy ranking queries in uncertain databases

Recently, uncertain data have received dramatic attention along with technical advances on geographical tracking, sensor network and RFID etc. Also, ranking queries over uncertain data has become a research focus of uncertain data management. With dramatically growing applications of fuzzy set theory, lots of queries involving fuzzy conditions appear nowadays. These fuzzy conditions are widely applied for querying over uncertain data. For instance, in the weather monitoring system, weather data are inherent uncertainty due to some measurement errors. Weather data depicting heavy rain are desired, where ??heavy?? is ambiguous in the fuzzy query. However, fuzzy queries cannot ensure returning expected results from uncertain databases. In this paper, we study a novel kind of ranking queries, Fuzzy Ranking queries (FRanking queries) which extend the traditional notion of ranking queries. FRanking queries are able to handle fuzzy queries submitted by users and return k results which are the most likely to satisfy fuzzy queries in uncertain databases. Due to fuzzy query conditions, the ranks of tuples cannot be evaluated by existing ranking functions. We propose Fuzzy Ranking Function to calculate tuples?? ranks in uncertain databases for both attribute-level and tuple-level uncertainty models. Our ranking function take both the uncertainty and fuzzy semantics into account. FRanking queries are formally defined based on Fuzzy Ranking Function. In the processing of answering FRanking queries, we present a pruning method which safely prunes unnecessary tuples to reduce the search space. To further improve the efficiency, we design an efficient algorithm, namely Incremental Membership Algorithm (IMA) which efficiently answers FRanking queries by evaluating the ranks of incremental tuples under each threshold for the fuzzy set. We demonstrate the effectiveness and efficiency of our methods through the theoretical analysis and experiments with synthetic and real datasets.     

基于语言变量的关系数据库模糊查询

在模糊理论的基础上,将权重概念引入关系数据库模糊查询中,以体现用户对查询中各个属性的相对重视程度。记录按匹配度的降序输出,方便用户选择。权重和匹配度都是语言变量,取值为语言值,更加贴近自然。采用模糊集合的alpha截集去模糊的思想,将带语言值权重的模糊查询条件转化为精确的SQL语句,利用RDBMS的机制进行记录的筛选,避免对整个数据库表的扫描,在一定程度上保证查询的效率。     

关系数据库中带语言值权重的模糊查询

在模糊理论的基础上,提出了将权重概念引入数据库模糊查询中,使用户对查询中各个属性的相对重视程度得以体现。为每条记录提供了一个匹配度,按匹配度的降序输出结果,方便用户选择。权重和匹配度都是语言变量［7］,其取值为语言值形式,更加贴近自然。     

Fuzzy query translation for relational database systems

The paper presents a new method for fuzzy query translation based on the alpha-cuts operations of fuzzy numbers. This proposed method allows the retrieval conditions of SQL queries to be described by fuzzy terms represented by fuzzy numbers. It emphasizes friendliness and flexibility for inexperienced users. The authors have implemented a fuzzy query translator to translate user's fuzzy queries into precise queries for relational database systems. Because the proposed method allows the user to construct his fuzzy queries intuitively and to choose different retrieval threshold values for fuzzy query translation, the existing relational database systems will be more friendly and more flexible to the users.