关系数据库中面向属性的泛化和约简

文中讨论了关系数据库中的知识发现,以RS理论为数学工具,将面向属性的归纳方法与实例学习相结合,用来关系数据库中知识发现的属性泛化和约简。引入了属性泛化的算法。用信息论的观点来解释属性约简的含义,提出了用信息增益作为属性的重要度量,并给出了基于这种重要度量的属性约简算法。     

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.     

A new method for computing fuzzy functional dependencies in relational database systems

In this paper, we present a new method for computing fuzzy functional dependencies between attributes in fuzzy relational database systems. The method is based on the use of fuzzy implications. A literature analysis has shown that there is no algorithm that would enable the identification of attribute relationships in fuzzy relational schemas. This fact was the motive for development a new methodology in the analysis of fuzzy functional dependencies over a given set of attributes. Solving this, not so new problem, is not only research challenge having theoretical importance, but it also has practical significance. Possible applications of the proposed methodology include GIS, data mining, information retrieval, reducing data redundancy in fuzzy relations through implementation of logical database model, estimation of missing values etc.     

Building systems on relational database

Relational databases are becoming increasingly popular. They create an environment within which computer system developers can use high level tools to build application systems quickly. This article looks at one computer manufacturer, Microdata, which has built a whole product line around the use of relational database products.     

Embedding deductive capabilities in relational database systems

The basic idea of our work is to include deductive capabilities in a relational database system by fully using the operands of relational algebra and without using any theorem prover. We present a deductive database system where the user can formulate deduction laws as logical formulas, but which works on an underlying relational database system by fully using the data manipulation routines of such a system. Generally, it is not possible to express the deduction laws by means of expressions of relational algebra (see Ref. 1). In our system, a relational algebra expression is associated with every database deduction law. The result of its evaluation (with respect to an underlying database) is the set of all database entries that can be deduced from the database by applying the respective law once (Theorem 1). By successively applying all these relational expressions for all the database deduction laws it is possible to generate the whole extensional database (i.e. all tuples deducible from the database).     

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.     

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.     

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     

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.     

Multilevel fuzzy relational systems: structure and identification

 Existing fuzzy relational equations (FRE) typically possess an evident single-level structure, where no consequence part of the rule being modeled, is used as a fact to another rule. Corresponding to multistage fuzzy reasoning, a natural extension of traditional fuzzy relational systems (FRS) is to introduce some intermediate levels of processing governed by enhanced FRE's so that the structure resulted becomes multilevel or multistage. Three basic multilevel FRS structures, namely, incremental, aggregated, and cascaded, are considered in this paper and they correspond to different reasoning mechanisms being frequently used by human beings in daily life. While the research works on multilevel FRS are sparse and our ability to solve a system of multilevel FRE's in a purely analytical manner is very limited, we address the identification problem from an optimization approach and introduce three fuzzy neural models. The proposed models consist of single-level FRS modules that are arranged in different hierarchical manners. Each module can be realized by Lin and Lee's fuzzy neural model for implementing the Mamdani fuzzy inference. We have particularly addressed the problem of how to distribute the input variables to different (levels of) relational modules for the incremental and aggregated models. In addition, the new models can learn a complete multistage fuzzy rule set from stipulated data pairs using structural and parameter learning. The effectiveness of the multilevel models has been demonstrated through various benchmarking problems. It can be generally concluded that the new models are distinctive in learning, generalization, and robustness.     

Integrating relational and object-oriented database systems using a metaclass concept

This paper presents a specific approach of integrating a relational database system into a federated database system. The underlying database integration process consist of three steps: first, the external database systems have to be connected to the integrated database system environment and the external data models have to be mapped into a canonical data model. This step is often called syntactic transformation including structural enrichment and leads to component schemas for each external DBMS. Second, the resulting schemas from the first step are used to construct export schemas which are then integrated into global, individual schemas or views in the third step. In this paper we focus on the first step for relational databases, i.e., the connection of a relational database system and the mapping of the relational model into a canonical data model. We take POSTGRES as the relational database system and the object-oriented federated database system VODAK as the integration platform which provides the open, object-oriented data model as the canonical data model for the integration. We show different variations of mapping the relational model. By exploiting the metaclass concept provided by VML, the modelling language of VODAK, we show how to tailor VML such that the canonical data model meets the requirements of integrating POSTGRES into the global database system VODAK in an efficient way.     

Management of XML documents without schema in relational database systems

Many applications deal with highly flexible XML documents from different sources, which makes it difficult to define their structure by a fixed schema or a DTD. Therefore, it is necessary to explore ways to cope with such XML documents. The paper analyzes different storage and retrieval methods for schemaless XML documents using the capabilities of relational systems. We compare our experiences with the implementation of an XML-to-relational mapping with an LOB implementation in a commercial RDBMS. The paper concludes with a vision of how different storage methods could converge towards a common high-level XML-API for databases.     

A Tree-structured Database Machine for large relational database systems

This paper proposes a Tree-structured Database Machine (TDM), which is designed forsupporting large relational database systems. The TDM aims at totally improving three levelperformance bottlenecks in relational database systems, i.e.(?) data transfer bottleneck, dataprocessing bottleneck, and multiquery optimization bottleneck. For reducing these bottlenecks,the TDM provides the approaches of data partition and data distribution upon data storages forconcurrent data accesses, the tree-structured multiprocessor architecture for parallel dataprocessing, the technique of tightly-coupled-shared-memory for decreasing data movement,the mechanisms of indices and dictionaries for reducing data transfer from the data storages tothe memory, and so on. These features show that the TDM not only improves the systemperformance, increases the parallelism and the data processing power, but also eliminates theperformance bottlenecks in relational database systems.     

Magnetic bubble memory structures for relational database management systems

Recently the concept of using magnetic bubble technology to store data and to perform logical operations has received considerable attention. By exploiting this hardware approach for relational database management systems, we introduce an efficient support for records permutation, sorting and searching for data. Actually these are substantial operations in relational data models.The organization of a relational data model in a magnetic bubbie memory is straightforward. In such systems the memory consists of loops, where each loop is capable of holding one record. Under the control of a switch, a loop can circulate the records or can hold them in position. However, as the number of switches in the memory system increases, the number of control lines becomes large and the model structure may lose its practical significance.In this investigation three different models of bubble memories are applied to a simple relational database example. The two basic operations of permuting records and searching for data are emphasized. For these models, some theoretical features, the essential characteristics and their applicability are pointed out.     

Efficient recursive XML query processing using relational database systems

Recursive queries are quite important in the context of XML databases. In addition, several recent papers have investigated a relational approach to store XML data and there is growing evidence that schema-conscious approaches are a better option than schema-oblivious techniques as far as query performance is concerned. However, the issue of recursive XML queries for such approaches has not been dealt with satisfactorily. In this paper we argue that it is possible to design a schema-oblivious approach that outperforms schema-conscious approaches for certain types of recursive queries. To that end, we propose a novel schema-oblivious approach, called Sucxent++ (Schema Unconcious XML Enabled System), that outperforms existing schema-oblivious approaches such as XParent by up to 15 times and schema-conscious approaches (Shared-Inlining) by up to eight times for recursive query execution. Our approach has up to two times smaller storage requirements compared to existing schema-oblivious approaches and 10% less than schema-conscious techniques. In addition Sucxent++ performs marginally better than Shared-Inlining and is 5.7–47 times faster than XParent as far as insertion time is concerned.     

Purpose based access control for privacy protection in relational database systems

In this article, we present a comprehensive approach for privacy preserving access control based on the notion of purpose. In our model, purpose information associated with a given data element specifies the intended use of the data element. A key feature of our model is that it allows multiple purposes to be associated with each data element and also supports explicit prohibitions, thus allowing privacy officers to specify that some data should not be used for certain purposes. An important issue addressed in this article is the granularity of data labeling, i.e., the units of data with which purposes can be associated. We address this issue in the context of relational databases and propose four different labeling schemes, each providing a different granularity. We also propose an approach to represent purpose information, which results in low storage overhead, and we exploit query modification techniques to support access control based on purpose information. Another contribution of our work is that we address the problem of how to determine the purpose for which certain data are accessed by a given user. Our proposed solution relies on role-based access control (RBAC) models as well as the notion of conditional role which is based on the notions of role attribute and system attribute.     

Security checking in relational database management systems augmented with inference engines

In this paper we will discuss the notion of multilevel security and the difficulties encountered in designing an implementation scheme for a security policy for a multilevel secure database management system (MLS/DBMS). We will then describe how these difficulties may be overcome in augmenting a database with an inference engine so that it functions like a knowledge based system.