Translation of fuzzy queries for relational database system

The concept of a database skeleton which reflects both the user's conception of the real world and the system's understanding of the interrelationships among database entities is described. It consists of a conceptual schema (conceptual graphs) and a relational schema (information graph). With the aid of the database skeleton, fuzzy queries can be translated and disambiguated by analyzing the queries using the conceptual graphs of a database skeleton. The query language XQL is introduced, and the XQL translator is described in some detail.     

关系数据库中分组查询的模糊扩展及去模糊机制

当前关系数据库模糊查询的研究中,涉及到分组查询having子句中的模糊条件或相对语言量词的较少。在模糊理论的基础上对having子句进行了模糊扩展,并利用模糊集合隶属函数的α截集将模糊的having子句转化为标准的SQL语句,因此可以利用RDBMS对记录进行筛选,保证了查询的效率。利用模糊集合基数的非模糊表示法来计算带量词的having语句,计算简单,结果简洁。     

Answers to queries concerning uncertain and imprecise information in fuzzy relational databases

A model of an extended fuzzy relational database was proposed to accommodate uncertain and imprecise information. We use two supplementary measurements, satisfactory degree and extra degree, for determining the quality of answers to Select‐Project‐Join (SPJ) queries. The method of measurement determines how much satisfactory information is provided and how much truth information is required for a query. The answers to the query thus contain sure answers and maybe answers. The core of this study is the detailed discussion on the quality of answers in an extended fuzzy relation to query processing. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 647–668, 2005.     

Generating weighted fuzzy rules from relational database systems for estimating values using genetic algorithms

In recent years, some methods have been proposed to estimate values in relational database systems. However, the estimated accuracy of the existing methods are not good enough. In this paper, we present a new method to generate weighted fuzzy rules from relational database systems for estimating values using genetic algorithms (GAs), where the attributes appearing in the antecedent part of generated fuzzy rules have different weights. After a predefined number of evolutions of the GA, the best chromosome contains the optimal weights of the attributes, and they can be translated into a set of rules to be used for estimating values. The proposed method can get a higher average estimated accuracy rate than the methods we presented in two previous papers.     

Integrating fuzzy queries into an existing database management system: An example

In traditional database management systems, queries are intended to retrieve data which satisfy crisp criteria. In some cases, this lack of flexibility leads to empty answers. That is one of the reasons why we have been investigating the extension of these systems so that they become able to support imprecise querying capabilities. In this article, the introduction of imprecise queries in a particular nonrelational system (Information Warehouse) is presented. One of the main interesting aspects of this work resides in the specific data model for which the semantics of fuzzy queries firstly has to be defined. © 1994 John Wiley & Sons, Inc.     

SQLf: a relational database language for fuzzy querying

An important issue in extending database management systems functionalities is to allow the expression of imprecise queries to enable these systems to satisfy the user needs more closely. This paper deals with imprecise querying of regular relational databases. The basic idea is to extend an existing query language, namely SQL. In this context, two important points must be considered: one concerns the integration in the extended language of many propositions that have been made elsewhere, in particular those concerning fuzzy aggregation operators; and the second point is to know whether the equivalences which are valid in SQL still hold in the extended language. Both these topics are investigated in this paper     

Mining fuzzy association rules in a bank-account database

This paper describes how we applied a fuzzy technique to a data-mining task involving a large database that was provided by an international bank with offices in Hong Kong. The database contains the demographic data of over 320,000 customers and their banking transactions, which were collected over a six-month period. By mining the database, the bank would like to be able to discover interesting patterns in the data. The bank expected that the hidden patterns would reveal different characteristics about different customers so that they could better serve and retain them. To help the bank achieve its goal, we developed a fuzzy technique, called fuzzy association rule mining II (FARM II). FARM II is able to handle both relational and transactional data. It can also handle fuzzy data. The former type of data allows FARM II to discover multidimensional association rules, whereas the latter data allows some of the patterns to be more easily revealed and expressed. To effectively uncover the hidden associations in the bank-account database, FARM II performs several steps which are described in detail in this paper. With FARM II, the bank discovered that they had identified some interesting characteristics about the customers who had once used the bank's loan services but then decided later to cease using them. The bank translated what they discovered into actionable items by offering some incentives to retain their existing customers.     

Using a relational database to support explanation in aknowledge-based system

In knowledge-based system (KBS) applications where the number of facts in the knowledge base is very large, the amount of information that is needed for effective explanation support can become too voluminous to be stored in main memory. The authors present an approach to modeling and managing the information needed for explanation rising a relational database. It is shown how different types of explanation can then be produced by appropriate queries on this database. The authors formulate representative queries for some major types of explanation using ESQL, an extension of structured query language (SQL)     

模糊规则相似性计算与性能分析研究

针对模糊规则相似性分析和计算问题,本文对模糊规则相似性计算方法进行了研究。首先,将模糊规则相似性等价地转化为多变量模糊集相似性,并对模糊规则相似性计算方法提出3种应用性能评价指标——可区分性、维数依赖性和计算复杂性。其次,在现有两种模糊规则相似性计算方法的基础上,提出4种新的计算方法,对各种方法进行系统地性能分析和比较。最后,对模糊规则相似性计算进行仿真研究,结果表明了所提应用性能指标的有效性、计算方法的可行性及分析结果的正确性。本文研究结果为模糊规则相似性分析和计算提供了依据,尤其为基于模糊规则相似性辨识和合并的模糊系统与模糊神经网络结构简化奠定了基础,提供了一种新的设计思路。     

Attribute-oriented fuzzy generalization in proximity- and similarity-based relational database systems

In this article we investigate an attribute-oriented induction approach for acquisition of abstract knowledge from data stored in a fuzzy database environment. We utilize a proximity-based fuzzy database schema as the medium carrying the original information, where lack of precise information about an entity can be reflected via multiple attribute values, and the classical equivalence relation is replaced with the broader fuzzy proximity relation. We analyze in detail the process of attribute-oriented induction by concept hierarchies, utilizing the original properties of fuzzy databases to support this established data mining technique. In our approach we take full advantage of the implicit knowledge about the similarity of original attribute values, included by default in the investigated fuzzy database schemas. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 763–779, 2007.     

Adding a relational interface to a nonrelational database

Today, thanks to more powerful machines and more robust data models, relational databases have gained broad industry acceptance. As relational databases grow in power and popularity, developers face pressure to convert legacy databases to this newer model. The author proposes a technique for doing so efficiently. The approach involves a bridge program that adds a relational software layer atop the nonrelational database management system. The interface translates the relational database management language into the nonrelational database management language to access the existing nonrelational database     

A knowledge-based system for generating informative responses to indirect database queries

The objective of this study is to develop a knowledge-base framework for generatingcooperative answers to indirect queries. Anindirect query can be considered as a nonstandard database query in which a user did not specify explicitly the information request. In a cooperative query answering system, a user's indirect query should be answered with an informative response, either anaffirmative response or anegative response, which is generated on the basis of the inference of the user's information request and the reformulation of the users' indirect query.This paper presents methods for inferring users' intended actions, determining users' information requirements, and for automatically reformulating indirect queries into direct queries. The inference process is carried out on the basis of a user model, calluser action model, as well as the query context. Two kinds ofinformative responses, i.e.affirmative responses andnegative responses can be generated by arule-based approach.     

Generalizing database relational algebra for the treatment of incomplete or uncertain information and vague queries

This paper deals with relational databases which are extended in the sense that fuzzily known values are allowed for attributes. Precise as well as partial (imprecise, uncertain) knowledge concerning the value of the attributes are represented by means of [0,1]-valued possibility distributions in Zadeh's sense. Thus, we have to manipulate ordinary relations on Cartesian products of sets of fuzzy subsets rather than fuzzy relations. Besides, vague queries whose contents are also represented by possibility distributions can be taken into account. The basic operations of relational algebra, union, intersection, Cartesian product, projection, and selection are extended in order to deal with partial information and vague queries. Approximate equalities and inequalities modeled by fuzzy relations can also be taken into account in the selection operation. Then, the main features of a query language based on the extended relational algebra are presented. An illustrative example is provided. This approach, which enables a very general treatment of relational databases with fuzzy attribute values, makes an extensive use of dual possibility and necessity measures.     

Safety, domain independence and translation of complex value database queries

This paper considers the theory of database queries on the complex value data model with external functions. Motivated by concerns regarding query evaluation, we first identify recursive sets of formulas, called embedded allowed, which is a class with desirable properties of “reasonable” queries.We then show that all embedded allowed calculus (or fix-point) queries are domain independent and continuous. An algorithm for translating embedded allowed queries into equivalent algebraic expressions as a basis for evaluating safe queries in all calculus-based query classes has been developed.Finally we discuss the topic of “domain independent query programs”, compare the expressive power of the various complex value query languages and their embedded allowed versions, and discuss the relationship between safety, embedded allowed, and domain independence in the various calculus-based queries.     

A user-oriented database system and its mapping to the relational model

An architecture of a database system for non-expert users and capable of supporting manual document structures is described. The development of a user-oriented data model (UODM) with suitable constructs is presented. Various design strategies for generating an appropriate relational schema automatically from the UODM are presented and discussed. Finally it is pointed out that the use of a typifying attribute and a set of mutually exclusive attributes may be of use as a general data analysis tool.     

Atlas: a nested relational database system for text applications

Advanced database applications require facilities such as text indexing, image storage, and the ability to store data with a complex structure. However, these facilities are not usually included in traditional database systems. In this paper we describe Atlas, a nested relational database system that has been designed for text-based applications. The Atlas query language is TQL, an SQL-like query language with text operators. The query language is supported by signature file text indexing techniques, and by a parser that can be configured for different text formats and even some foreign languages. Atlas can also be used to store images and audio     

On a similarity measure between LR‐type fuzzy numbers and its application to database acquisition

This article presents a new similarity measure for LR‐type fuzzy numbers. The proposed similarity measure is based on a defined metric between LR‐type fuzzy numbers. It is known that an exponential operation is highly useful in dealing with the classical Shannon entropy and cluster analysis. We adopted, therefore, the exponential operation on this metric. Furthermore, we analyze its properties and make numerical comparisons to several similarity measures. The results show that the proposed similarity measure can overcome the drawbacks of the existing similarity measures. We then apply it to compound attributes for handling null queries to database systems. These applications can also be widely used in fuzzy queries to databases. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1001–1016, 2005.     

A collaborative filtering framework based on fuzzy association rules and multiple-level similarity

The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches. Cane Wing-ki Leung is a PhD student in the Department of Computing, The Hong Kong Polytechnic University, where she received her BA degree in Computing in 2003. Her research interests include collaborative filtering, data mining and computer-supported collaborative work. Stephen Chi-fai Chan is an Associate Professor and Associate Head of the Department of Computing, The Hong Kong Polytechnic University. Dr. Chan received his PhD from the University of Rochester, USA, worked on computer-aided design at Neo-Visuals, Inc. in Toronto, Canada, and researched in computer-integrated manufacturing at the National Research Council of Canada before joining the Hong Kong Polytechnic University in 1993. He is currently working on the development of collaborative Web-based information systems, with applications in education, electronic commerce, and manufacturing. Fu-lai Chung received his BSc degree from the University of Manitoba, Canada, in 1987, and his MPhil and PhD degrees from the Chinese University of Hong Kong in 1991 and 1995, respectively. He joined the Department of Computing, Hong Kong Polytechnic University in 1994, where he is currently an Associate Professor. He has published widely in the areas of computational intelligence, pattern recognition and recently data mining and multimedia in international journals and conferences and his current research interests include time series data mining, Web data mining, bioinformatics data mining, multimedia content analysis,and new computational intelligence techniques.     

Optimizing the evaluation of calculus expressions in a relational database system

There is an interesting search strategy (due to James B. Rothnie) for efficient implementation of a limited kind of selection criterion for a relational database. This strategy is here generalized to arbitrary relational calculus expressions, and an analysis of the resulting improvement of performance is given. The strategy is used in a relational database system TGR and an overview of the architecture of this system is presented. TGR uses microprogrammed database primitives for searching the database. This approach is very similar to the use of a database processor although it also allows flexible change of processor design. The behaviour of TGR in evaluating typical queries is analyzed and the results are used for pointing out the bottlenecks in a relational database system with a particular type of structure. As a conclusion the construction of a database processor with the database primitives from TGR as instruction set is recommended. This would be a step towards getting acceptable performance in a relational database system.