A NEW METHOD FOR FUZZY QUERY PROCESSING IN RELATIONAL DATABASE SYSTEMS

In this paper, we present a new method for fuzzy query processing in relational database systems based on automatic clustering techniques and weighting concepts. The proposed method allows the query conditions and the weights of query items of users' fuzzy SQL queries to be described by linguistic terms represented by fuzzy numbers. Because the proposed fuzzy query processing method allows the users to construct their fuzzy queries more conveniently, the existing relational database systems will be more intelligent and more flexible to the users.     

Fuzzy query processing for document retrieval based on extendedfuzzy concept networks

In this paper, we present a new method for fuzzy query processing for document retrieval based on extended fuzzy concept networks. In an extended fuzzy concept network, there are four kinds of fuzzy relationships between concepts, i.e., fuzzy positive association, fuzzy negative association, fuzzy generalization, and fuzzy specialization. An extended fuzzy concept network can be modeled by a relation matrix and a relevance matrix, where the elements in a relation matrix represent the fuzzy relationships between concepts, and the elements in a relevance matrix indicate the degrees of relevance between concepts. The implicit fuzzy relationships between concepts can be inferred by the transitive closure of the relation matrix. The implicit degrees of relevance between concepts also can be inferred by the transitive closure of the relevance matrix. The proposed method allows the users to perform positive queries, negative queries, generalization queries, and specialization queries. The proposed method allows the users to perform fuzzy queries in a more flexible and more intelligent manner.     

Generalization of strategies for fuzzy query translation in classical relational databases

Users of information systems would like to express flexible queries over the data possibly retrieving imperfect items when the perfect ones, which exactly match the selection conditions, are not available. Most commercial DBMSs are still based on the SQL for querying. Therefore, providing some flexibility to SQL can help users to improve their interaction with the systems without requiring them to learn a completely novel language. Based on the fuzzy set theory and the α-cut operation of fuzzy number, this paper presents the generic fuzzy queries against classical relational databases and develops the translation of the fuzzy queries. The generic fuzzy queries mean that the query condition consists of complex fuzzy terms as the operands and complex fuzzy relations as the operators in a fuzzy query. With different thresholds that the user chooses for the fuzzy query, the user’s fuzzy queries can be translated into precise queries for classical relational databases.     

Two-dimensional specification of universal quantification in agraphical database query language

A technique is proposed for specifying universal quantification and existential quantification (combined with negation) in a two-dimensional (graphical) database query language. Unlike other approaches that provide set operators to simulate universal quantification, this technique allows a direct representation of universal quantification. Syntactic constructs for specifying universal and existential quantifications, two-dimensional translation of universal quantification to existential quantification (with negation), and translation of existentially quantified two-dimensional queries to relational queries are presented. The resulting relational queries can be processed directly by many existing database systems. The authors claim that this technique renders universal quantifications easy to understand. To substantiate this claim, they provide a simple, easy-to-follow guideline for constructing universally quantified queries     

Rewriting rules to permeate complex similarity and fuzzy queries within a relational database system

In recent years, the availability of complex data repositories (e.g., multimedia, genomic, semistructured databases) has paved the way to new potentials as to data querying. In this scenario, similarity and fuzzy techniques have proven to be successful principles for effective data retrieval. However, most proposals are domain specific and lack of a general and integrated approach to deal with generalized complex queries, i.e., queries where multiple conditions are expressed, possibly on complex as well as on traditional data. To overcome such limitations, much work has been devoted to the development of middleware systems to support query processing on multiple repositories. On a similar line, We present a formal framework to permeate complex similarity and fuzzy queries within a relational database system. As an example, we focus on multimedia data, which is represented in an integrated view with common database data. We have designed an application layer that relies on an algebraic query language, extended with MM-tailored operators, and that maps complex similarity and fuzzy queries to standard SQL statements that can be processed by a relational database system, exploiting standard facilities of modern extensible RDBMS. To show the applicability of our proposal, we implemented a prototype that provides the user with rich query capabilities, ranging from traditional database queries to complex queries gathering a mixture of Boolean, similarity, and fuzzy predicates on the data.     

基于模糊语言方法的信息检索系统的研究

该文提出了一个基于模糊语言方法的信息检索系统模型。该系统分为查询界面子系统、数据库子系统和检索子系统三大部分。在查询界面子系统,用布尔表达式表示用户的查询请求,并对每个查询关键词赋予了两种不同语义的语言值权重,该权重表达了用户的模糊检索要求;在数据库子系统,用索引词一文档模糊矩阵表示待检索的文档,对每个索引词。根据其在文档中的出现频率大小。引入了数值权重;在检索子系统,运用模糊语言方法,对用户输入的布尔查询表达式与索引词一文档模糊矩阵进行自底向上的模糊匹配,最后返回满足用户要求的检索结果。相对于传统的基于查询关键词精确匹配的检索系统而言,该系统能较好地满足用户查询要求中的灵活性。     

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。     

基于关系数据库的模糊查询技术

在关系数据库中,SQL只能处理精确查询,而现实中存在许多模糊查询的问题。在模糊逻辑理论的基础上,提出了关系数据库的模糊查询思路,并详细地讨论了在数值和字符属性上的实现方法。实现方法不仅对数据库的查询进行了扩展,而且对实际系统的开发也有一定的借鉴作用。     

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     

PICASSO: A graphical query language

PICASSO (PICture Aided Sophisticated Sketch Of database queries) is a graphics-based database query language designed for use with a universal relation database system. The primary objective of PICASSO is ease of use. Graphics are used to provide a simple method of expressing queries and to provide visual feedback to the user about the system's interpretation of the query. Inexperienced users can use the graphical feedback to aid them in formulating queries whereas experienced users can ignore the feedback. Inexperienced users can pose queries without knowing the details of underlying database schema and without learning the formal syntax of SQL-like query language. This paper presents the syntax of PICASSO queries and compares PICASSO queries with similar queries in standard relational query languages. Comparisons are also made with System/U, a non-graphical universal relation system on which PICASSO is based. The hypergraph semantics of the universal relation are used as the foundation for PICASSO and their integration with a graphical workstation enhances the usability of database systems.     

Seamlessly integrating similarity queries in SQL

Modern database applications are increasingly employing database management systems (DBMS) to store multimedia and other complex data. To adequately support the queries required to retrieve these kinds of data, the DBMS need to answer similarity queries. However, the standard structured query language (SQL) does not provide effective support for such queries. This paper proposes an extension to SQL that seamlessly integrates syntactical constructions to express similarity predicates to the existing SQL syntax and describes the implementation of a similarity retrieval engine that allows posing similarity queries using the language extension in a relational DBMS. The engine allows the evaluation of every aspect of the proposed extension, including the data definition language and data manipulation language statements, and employs metric access methods to accelerate the queries. Copyright © 2008 John Wiley & Sons, Ltd.     

The MIEL system: Uniform interrogation of structured and weakly-structured imprecise data

We present an information system developed to help assessing the microbiological risk in food. That information system contains experimental results in microbiology, mainly extracted from scientific publications. The increasing amount of the experimental results available and the difficulty to integrate them into a classic relational database schema led us to design a system composed of two distinct subsystems queried through a common interface. The first subsystem is a classic relational database. The second subsystem is a database containing weakly-structured pieces of information expressed in terms of conceptual graphs. The data stored in both bases can be fuzzy ones in order to take into account the specificities of the biological information. The uniform query language used on both relational database and conceptual graph database allows the users to express preferences by using fuzzy sets in their queries. The MIEL system is now operational and used by the microbiologists involved in the Sym’Previus French project.     

Flexible and efficient IR using array databases

The Matrix Framework is a recent proposal by Information Retrieval (IR) researchers to flexibly represent information retrieval models and concepts in a single multi-dimensional array framework. We provide computational support for exactly this framework with the array database system SRAM (Sparse Relational Array Mapping), that works on top of a DBMS. Information retrieval models can be specified in its comprehension-based array query language, in a way that directly corresponds to the underlying mathematical formulas. SRAM efficiently stores sparse arrays in (compressed) relational tables and translates and optimizes array queries into relational queries. In this work, we describe a number of array query optimization rules. To demonstrate their effect on text retrieval, we apply them in the TREC TeraByte track (TREC-TB) efficiency task, using the Okapi BM25 model as our example. It turns out that these optimization rules enable SRAM to automatically translate the BM25 array queries into the relational equivalent of inverted list processing including compression, score materialization and quantization, such as employed by custom-built IR systems. The use of the high-performance MonetDB/X100 relational backend, that provides transparent database compression, allows the system to achieve very fast response times with good precision and low resource usage.     

Content-based image retrieval based on a fuzzy approach

A typical content-based image retrieval (CBIR) system would need to handle the vagueness in the user queries as well as the inherent uncertainty in image representation, similarity measure, and relevance feedback. We discuss how fuzzy set theory can be effectively used for this purpose and describe an image retrieval system called FIRST (fuzzy image retrieval system) which incorporates many of these ideas. FIRST can handle exemplar-based, graphical-sketch-based, as well as linguistic queries involving region labels, attributes, and spatial relations. FIRST uses fuzzy attributed relational graphs (FARGs) to represent images, where each node in the graph represents an image region and each edge represents a relation between two regions. The given query is converted to a FARG, and a low-complexity fuzzy graph matching algorithm is used to compare the query graph with the FARGs in the database. The use of an indexing scheme based on a leader clustering algorithm avoids an exhaustive search of the FARG database. We quantify the retrieval performance of the system in terms of several standard measures.     

ESTIMATING NULL VALUES IN THE DISTRIBUTED RELATIONAL DATABASES ENVIRONMENT

To estimate nullvalues in relational database systems is an important research topic. In Chen and Yeh (1997) a method for estimating null values in relational database systems was presented. In Chen and Chen (1997) a method for fuzzy query translation for information in the distributed relational databases environment was presented. In this article, the works of Chen and Chen (1997) and Chen and Yeh (1997) are extended to propose a method for estimating null values in the distributed relational databases environment. The proposed method provides a useful way to estimate incomplete data when the relations stored in a failed server cannot be accessed in the distributed relational databases environment.     

QQL: A DB&;IR Query Language

Traditional database query languages are based on set theory and crisp first order logic. However, many applications require retrieval-like queries which return result objects associated with a degree of being relevant to the query. Historically, retrieval systems estimate relevance by exploiting hidden object semantics whereas query processing in database systems relies on matching select-conditions with attribute values. Thus, different mechanisms were developed for database and information retrieval systems. In consequence, there is a lack of support for queries involving both retrieval and database search terms. In this work, we introduce the quantum query language (QQL). Its underlying unifying theory is based on the mathematical formalism of quantum mechanics and quantum logic. Van Rijsbergen already discussed the strong relation between the formalism of quantum mechanics and information retrieval. In this work, we interrelate concepts from database query processing to concepts from quantum mechanics and logic. As result, we obtain a common theory which allows us to incorporate seamlessly retrieval search into traditional database query processing.     

The GMAP: a versatile tool for physical data independence

Physical data independence is touted as a central feature of modern database systems. It allows users to frame queries in terms of the logical structure of the data, letting a query processor automatically translate them into optimal plans that access physical storage structures. Both relational and object-oriented systems, however, force users to frame their queries in terms of a logical schema that is directly tied to physical structures. We present an approach that eliminates this dependence. All storage structures are defined in a declarative language based on relational algebra as functions of a logical schema. We present an algorithm, integrated with a conventional query optimizer, that translates queries over this logical schema into plans that access the storage structures. We also show how to compile update requests into plans that update all relevant storage structures consistently and optimally. Finally, we report on experiments with a prototype implementation of our approach that demonstrate how it allows storage structures to be tuned to the expected or observed workload to achieve significantly better performance than is possible with conventional techniques. Edited by Matthias Jarke, Jorge Bocca, Carlo Zaniolo. Received September 15, 1994 / Accepted September 1, 1995     

A theory of translation from relational queries to hierarchicalqueries

In a heterogeneous database system, a query for one type of database system (i.e., a source query) may have to be translated to an equivalent query (or queries) for execution in a different type of database system (i.e., a target query). Usually, for a given source query, there is more than one possible target query translation. Some of them can be executed more efficiently than others by the receiving database system. Developing a translation procedure for each type of database system is time-consuming and expensive. We abstract a generic hierarchical database system (GHDBS) which has properties common to database systems whose schema contains hierarchical structures (e.g., System 2000, IMS, and some object-oriented database systems). We develop principles of query translation with GHDBS as the receiving database system. Translation into any specific system can be accomplished by a translation into the general system with refinements to reflect the characteristics of the specific system. We develop rules that guarantee correctness of the target queries, where correctness means that the target query is equivalent to the source query. We also provide rules that can guarantee a minimum number of target queries in cases when one source query needs to be translated to multiple target queries. Since the minimum number of target queries implies the minimum number of times the underlying system is invoked, efficiency is taken into consideration     

Metaform: updatable form screens and their application to the use of office metaphors in query language instruction

Abstract

A database interface language and system, called Metaform, which automatically generates multi-relational form screen interfaces for use by non-computer professionals has been developed. A form screen is a subset of the relational database, with a particular relation or combination of relations being represented. Through form screens, users can simultaneously query and update several relations in the database without having to know about its underlying structure. An overview of the Metaform system is presented and several examples of the use of the Metaform query language and update operators are described.

A series of ‘usability’ studies were conducted on a prototype of the Metaform system to examine the claims that the form concept aids computer-naive users in building complex database queries. These studies adopted the form screen concept to present six office paper work analogies to users to help them to understand the database retrieval concepts. The analogies of a file cabinet, a file folder, a stack of forms, a single form, a table of information on a form and a field of information were used in a two-staged training module.

At the end of each training sequence, users answered questions with the prototype and with paper and pencil which tapped their understanding of the database retrievals they were learning to perform. The results from these questionnaires were mixed. Users performed successful relational queries for simple retrievals and for those using existential quantifiers. They had difficulty with queries involving multiple steps and intermediate stages. Although users understood and used the analogies, they ran into difficulties with the ambiguities in the English statements of the queries, thus suggesting a need for another level of metaphors and/or problem representation tools not associated with the machine but with the user's comprehension of database retrieval problems.