Updating extended possibility‐based fuzzy relational databases

Two kinds of fuzziness in attribute values of the fuzzy relational databases can be distinguished: one is that attribute values are possibility distributions and the other is that there are resemblance relations in attribute domains. The fuzzy relational databases containing these two kinds of fuzziness simultaneously are called extended possibility‐based fuzzy relational databases. In this article, we focus on such fuzzy relational databases and investigate three update operations for the fuzzy relational databases, which are Insertion, Deletion, and Modification, respectively. We develop the strategies and implementation algorithms of these operations. © 2007 Wiley Periodicals, Inc. Int J Int Syst 22: 237–258, 2007.     

Handling fuzzy information in extended possibility‐based fuzzy relational databases

Two kinds of fuzziness in attribute values of the fuzzy relational databases can be distinguished: One is that attribute values are possibility distributions, and the other is that there are resemblance relations in attribute domains. The fuzzy relational databases containing these two kinds of fuzziness simultaneously are called extended possibility‐based fuzzy relational databases. In this paper, we focus on such fuzzy relational databases. We classify two kinds of fuzzy data redundancies and define their removal. On this basis, we define fuzzy relational operations in relational algebra, which, being similar to the conventional relational databases, are complete and sound. In particular, we investigate fuzzy querying strategies and give the form of fuzzy querying with SQL. © 2002 Wiley Periodicals, Inc.     

Data dependencies in extended possibility‐based fuzzy relational databases

Based on the semantic equivalence degree the formal definitions of fuzzy functional dependencies (FFDs) and fuzzy multivalued dependencies (FMVDs) are first introduced to the fuzzy relational databases, where fuzziness of data appears in attribute values in the form of possibility attributions, as well as resemblance relations in attribute domain elements, called extended possibility‐based fuzzy relational databases. A set of inference rules for FFDs and FMVDs is then proposed. It is shown that FFDs and FMVDs are consistent and the inference rules are sound and complete, just as Armstrong's axioms for classic cases. © 2002 Wiley Periodicals, Inc.     

Semantic measure of fuzzy data in extended possibility‐based fuzzy relational databases

In this paper, we propose notions of equivalence and inclusion of fuzzy data in relational databases for measuring their semantic relationship. The fuzziness of data appears in attribute values in forms of possibility distribution as well as resemblance relations in attribute domain elements. An approach for evaluating semantic measures is presented. With the proposal, one can remove fuzzy data redundancy and define fuzzy functional dependency. © 2000 John Wiley & Sons, Inc.     

Parallel join for IBGF partitioned relational databases

This study is concerned with a parallel join operation where the subject relations are partitioned according to an interpolation based grid file (IBGF) scheme. The partitioned relations and directories are distributed over a set of independently accessible external storage units, together with the partitioning control data. The join algorithms executed by a mesh type parallel computing system allow handling of uniform as well as nonuniformly partitioned relations. Each processor locates and retrieves the data partitions it is to join at each step of the join process, in synchronisation with other processors. The approach is found to be feasible as the speedup and efficiency results found by simulation are consistent with theoretical bounds. The algorithms are tuned to join-key distributions, so that effective load balancing is achieved during the actual join. © 1997 John Wiley & Sons, Ltd.     

Fuzzy relations and fuzzy relational databases

This paper deals with the connections existing between fuzzy set theory and fuzzy relational databases. Our new result dealing with fuzzy relations is how to calculate the greatest lower bound (glb) of two similarity relations. Our main contributions in fuzzy relational databases are establishing from fuzzy set theory what a fuzzy relational database should be (the result is both surprising and elegant), and making fuzzy relational databases even more robust.Our work in fuzzy relations and in fuzzy databases had led us into other interesting problems—two of which we mention in this paper. The first is primarily mathematical, and the second provides yet another connection between fuzzy set theory and artificial intelligence. In understanding similarity relations in terms of other fuzzy relations and in making fuzzy databases more robust, we work with closure and interior operators; we present some important properties of these operators. In establishing the connection between fuzzy set theory and artificial intelligence, we show that an abstraction on a set is in fact a partition on the set; that is, an abstraction defines an equivalence relation on the underlying set.     

Reasoning of fuzzy relational databases with fuzzy ontologies

A significant interest developed regarding the problem of describing databases with expressive knowledge representation techniques in recent years, so that database reasoning may be handled intelligently. Therefore, it is possible and meaningful to investigate how to reason on fuzzy relational databases (FRDBs) with fuzzy ontologies. In this paper, we first propose a formal approach and an automated tool for constructing fuzzy ontologies from FRDBs, and then we study how to reason on FRDBs with constructed fuzzy ontologies. First, we give their respective formal definitions of FRDBs and fuzzy Web Ontology Language (OWL) ontologies. On the basis of this, we propose a formal approach that can directly transform an FRDB (including its schema and data information) into a fuzzy OWL ontology (consisting of the fuzzy ontology structure and instance). Furthermore, following the proposed approach, we implement a prototype construction tool called FRDB2FOnto. Finally, based on the constructed fuzzy OWL ontologies, we investigate how to reason on FRDBs (e.g., consistency, satisfiability, subsumption, and redundancy) through the reasoning mechanism of fuzzy OWL ontologies, so that the reasoning of FRDBs may be done automatically by means of the existing fuzzy ontology reasoner.© 2012 Wiley Periodicals, Inc.     

Querying fuzzy relational databases through fuzzy domain calculus

In this paper we present a definition of a domain relational calculus for fuzzy relational databases using the GEFRED model as a starting point. It is possible to define an equivalent fuzzy tuple relational calculus and consequently we achieve the two query language levels that Codd designed for relational databases but these are extended to fuzzy relational databases: Fuzzy relational algebra (defined in the GEFRED model) and the fuzzy relational calculus which is put forward in this paper. The expressive power of this fuzzy relational calculus is demonstrated through the use of a method to translate any algebraic expression into an equivalent expression in fuzzy domain relational calculus. Furthermore, we include a useful system so that the degree to which each value has satisfied the query condition can be measured. Some examples are also included in order to clarify the definition. ©1999 John Wiley & Sons, Inc.     

Unacceptable components in fuzzy relational databases

The existence of unacceptable components, which consist of unacceptable tuples and elements in attribute values, is shown in fuzzy relational databases. The unacceptable components are created by update operations, insertion, deletion, and modification. An unacceptable tuple in a relation is a tuple such that the degree of its not belonging to that relation is greater than that of its belonging to. The unacceptable tuple can be easily eliminated from relations. There are three kinds of unacceptable elements. One case of unacceptable elements is a redundant element created by insertion and modification. Another is an element created by a possible tuple value not at all or partially satisfying integrity constraints in insertion and modification. The other is an element created by a possible tuple value completely or partially satisfying update conditions in deletion. The unacceptable elements can be eliminated from relations without loss of information. As a result, we can obtain fuzzy relational databases without unacceptable components by a reasonable way. © 1996 John Wiley & Sons, Inc.     

A logic approach to fuzzy relational databases

A new definition of Fuzzy Relational Database is given by using logical concepts. With this definition it is possible to consider several kinds of fuzziness for the database attributes. A Domain Calculus-based query language is also established for the model. It allows us to formulate several types of queries with different lack of precision levels. © 1994 John Wiley & Sons, Inc.     

Rule‐based approach for topic maps learning from relational databases

Relational databases (RDBs) have been widely used as back end for information systems. Considering that RDBs have valuable knowledge interwoven in between stored data, how to access, represent and share this knowledge becomes an important challenge. Topic maps (TMs) emerge as a good solution for this problem. However, manual development of TMs is a difficult, time‐consuming and subjective task if there is no common guideline. The existing TMs building approaches mainly consider the meta‐information contained in a RDB, without considering the knowledge residing in the database content (its current state). Other approaches require a predefined configuration for applying a specific data transformation. This paper proposes an automatic method for TM construction based on learning rules. Our method considers the background knowledge of the RDBs during the building process and was implemented and applied on a representative set of 15 RDBs. The resulting TMs were validated syntactically using a standard tool and validated semantically through the inference of information using a formal query language. In addition, an analysis between the relational data (input) and its representation (output) was conducted. The results found in our experiments are encouraging and put in evidence the soundness of the proposed method.     

A new definition of fuzzy functional dependency in fuzzy relational databases

The need to incorporate and treat information given in fuzzy terms in Relational Databases has concentrated a great effort in the last years. This article focuses on the treatment of functional dependencies (f.d.) between attributes of a relation scheme. We review other approaches to this problem and present some of its missfunctions concerning intuitive properties a fuzzy extension of f.d. should verify. Then we introduce a fuzzy extension of this concept to overcome the previous anomalous behaviors and study its properties. of primary interest is the completeness of our fuzzy version of Armstrong axioms in order to derive all the fuzzy functional dependencies logically implied by a set of f.f.d. just using these axioms. © 1994 John Wiley & Sons, Inc.     

The integrity constraints for similarity-based fuzzy relational databases

This paper first introduces a new definition for the conformance of tuples existing in a similarity-based fuzzy database relation. Then the formal definitions of fuzzy functional and multivalued dependencies are given on the basis of the conformance values presented here. These dependencies are defined to represent relationships between domains of the same relation that exist. The definitions of the fuzzy dependencies presented in this study allow a sound and complete set of inference rules. In this paper, we include examples to demonstrate how the integrity constraints imposed by these dependencies are enforced whenever a tuple is to be inserted or to be modified in a fuzzy database relation. © 1998 John Wiley & Sons, Inc.     

Storing and querying fuzzy XML data in relational databases

Information imprecision and uncertainty exist in many real-world applications and for this reason fuzzy data management has been extensively investigated in various database management systems. Currently, introducing native support for XML data in relational database management systems (RDBMs) has attracted considerable interest with a view to leveraging the powerful and reliable data management services provided by RDBMs. Although there is a rich literature on XML-to-relational storage, none of the existing solutions satisfactorily addresses the problem of storing fuzzy XML data in RDBMs. In this paper, we study the methodology of storing and querying fuzzy XML data in relational databases. In particular, we present an edge-based approach to shred fuzzy XML data into relational data. The unique feature of our approach is that no schema information is required for our data storage. On this basis, we present a generic approach to translate path expression queries into SQL for processing XML queries.     

Handling indefinite and maybe information in logical fuzzy relational databases

In this article, fuzzy set theory uses an extension of the classical logical relational database model. A logical fuzzy relational database model was developed with the aim of manipulating imprecise information and adding deduction capabilities to the database system. The essence of this work is the detailed discussion on fuzzy definite, fuzzy indefinite, and fuzzy maybe information and the development of an information theoretical approach of query evaluation on the logical fuzzy relational database. We define redundancies among fuzzy tuples and the operator of their removal. A complete set of fuzzy relational operations in relational algebra and the calculus of linguistically quantified propositions are included also. © 2004 Wiley Periodicals, Inc.     

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。     

Measuring the quality of queries in the fuzzy relational databases

In this paper, we develop a new method to measure the quality of each tuple as an answer with respect to Select‐Project‐Join (SPJ) queries so that we can determine which answers are better answers to the given query in a fuzzy relational database. The quality of an answer is viewed as how much sure information is provided, and how much extra information is needed so that it will be a sure answer to the query. The less extra information that is required and the more sure information that is provided by an answer, the higher the quality of that answer is, and in consequence, it will be more reliable. © 2001 John Wiley & Sons, Inc.     

关系数据库中的模糊关联规则挖掘算法研究

关联是数据挖掘领域的一个重要研究课题。对模糊关联规则挖掘进行了研究,针对普通关联规则不能精确表达数据库中模糊信息关联性的问题,提出了一种新的模糊关联规则挖掘算法FARM_New,结果表明算法是有效的,提高了模糊挖掘的速度。