Uncertainty in a nested relational database model

Some database models have already been developed to deal with complex values but they have constrains that data stored is precise and queries are crisp. However, as many researchers have pointed out, there is a need to present, manipulate, and query complex and uncertain data of various non-traditional database applications such as oceanography, multimedia, meteorology, office automation systems, engineering designs, expert database systems and geographic information systems. In this paper, we present a logical database model, which is an extension of a nested relational data model (also known as an NF² data model), for representing and manipulating complex and uncertain data in databases. We also introduce a possible physical representation of such complex and uncertain values in databases and describe the query processing of the model that we discuss here.     

An AGENDA for testing relational database applications

Database systems play an important role in nearly every modern organization, yet relatively little research effort has focused on how to test them. This paper discusses issues arising in testing database systems, presents an approach to testing database applications, and describes AGENDA, a set of tools to facilitate the use of this approach. In testing such applications, the state of the database before and after the user's operation plays an important role, along with the user's input and the system output. A framework for testing database applications is introduced. A complete tool set, based on this framework, has been prototyped. The components of this system are a parsing tool that gathers relevant information from the database schema and application, a tool that populates the database with meaningful data that satisfy database constraints, a tool that generates test cases for the application, a tool that checks the resulting database state after operations are performed by a database application, and a tool that assists the tester in checking the database application's output. The design and implementation of each component of the system are discussed. The prototype described here is limited to applications consisting of a single SQL query. Copyright © 2004 John Wiley & Sons, Ltd.     

A relational database system for student use

An APL implementation of a relational database system suitable for small interactive databases in a teaching environment is described Both user and database administrator functions are implemented. The system has a command-driven user-friendly interface.     

GRDB: A general purpose relational database system

This article discusses the design and development of GRDB (General Purpose Relational Data Base System) which has been implemented on a DEC-1090 system in Pascal. GRDB is a general purpose database system designed to be completely independent of the nature of data to be handled, since it is not tailored to the specific requirements of any particular enterprise. It can handle different types of data such as variable length records and textual data. Apart from the usual database facilities such as data definition and data manipulation, GRDB supports User Definition Language (UDL) and Security definition language. These facilities are provided through a SEQUEL-like General Purpose Query Language (GQL). GRDB provides adequate protection facilities up to the relation level. The concept of “security matrix” has been made use of to provide database protection. The concept of Unique IDentification number (UID) and Password is made use of to ensure user identification and authentication. The concept of static integrity constraints has been used to ensure data integrity. Considerable efforts have been made to improve the response time through indexing on the data files and query optimisation. GRDB is designed for an interactive use but alternate provision has been made for its use through batch mode also. A typical Air Force application (consisting of data about personnel, inventory control, and maintenance planning) has been used to test GRDB and it has been found to perform satisfactorily.     

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.     

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     

A relational database management system for atmospheric compensation research

Atmospheric compensation is a key step in the processing of most remotely sensed Earth images, but is particularly important in Hyperspectral Imagery (HSI) analysis. The state of atmospheric sensible variables at the time of the image is a key driver of atmospheric path radiance and transmissivity effects. A thorough assessment of the state of the atmosphere is rarely conducted in HSI analysis, however, even in rigorous scientific literature. This is due in part to the historical lack of meteorological data associated with imagery collects, and the difficulty in accessing and analyzing the data that is available. With the expanding availability of online climatological and observational atmospheric data and the advent of web enabled search and retrieval tools, easy access to worldwide atmospheric data relevant to atmospheric compensation is possible. In this project, a Relational Database Management System (RDBMS) is developed that automatically retrieves relevant atmospheric data from several online data stores based on input image metadata. The RBDMS provides researchers with a single, easily accessible repository of meteorological data to aid in researching atmospheric effects. System design considerations and available data sources are reviewed, and example research applications are presented.     

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)     

An optimizer for a relational database command language

This paper describes the optimizer included in the ICL personal data system (PDS), an interactive relational database system designed for users with little or no computer experience. The optimizer transforms the selection criteria in a command into an equivalent form which can be used to obey the command more quickly. The optimization method and the factorization procedure it uses are described in detail.     

Using a relational database for scalable XML search

XML is a flexible and powerful tool that enables information and security sharing in heterogeneous environments. Scalable technologies are needed to effectively manage the growing volumes of XML data. A wide variety of methods exist for storing and searching XML data; the two most common techniques are conventional tree-based and relational approaches. Tree-based approaches represent XML as a tree and use indexes and path join algorithms to process queries. In contrast, the relational approach utilizes the power of a mature relational database to store and search XML. This method relationally maps XML queries to SQL and reconstructs the XML from the database results. To date, the limited acceptance of the relational approach to XML processing is due to the need to redesign the relational schema each time a new XML hierarchy is defined. We, in contrast, describe a relational approach that is fixed schema eliminating the need for schema redesign at the expense of potentially longer runtimes. We show, however, that these potentially longer runtimes are still significantly shorter than those of the tree approach. We use a popular XML benchmark to compare the scalability of both approaches. We generated large collections of heterogeneous XML documents ranging in size from 500 MB to 8 GB using the XBench benchmark. The scalability of each method was measured by running XML queries that cover a wide range of XML search features on each collection. We measure the scalability of each method over different query features as the collection size increases. In addition, we examine the performance of each method as the result size and the number of predicates increase. Our results show that our relational approach provides a scalable approach to XML retrieval by leveraging existing relational database optimizations. Furthermore, we show that the relational approach typically outperforms the tree-based approach while scaling consistently over all collections studied.

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.     

Numerical operations on a relational database

The problem is discussed of defining numerical operations on a relational database to accommodate the statistical analyses associated with a bivariate frequency table. An attempt has been made to extend Codd's relational algebra to include simple bivariate statistical operations preserving the closure of Codd's algebra. This extended algebra is applied to bivariate relational data structures needed for real-time automatic statistical quality control of a manufacturing process. Also discussed are some new category-numeric operations on relational tables     

Chabot: retrieval from a relational database of images

Selecting from a large, expanding collection of images requires carefully chosen search criteria. We present an approach that integrates a relational database retrieval system with a color analysis technique. The Chabot project was initiated at our university to study storage and retrieval of a vast collection of digitized images. These images are from the State of California Department of Water Resources. The goal was to integrate a relational database retrieval system with content analysis techniques that would give our querying system a better method for handling images. Our simple color analysis method, if used in conjunction with other search criteria, improves our ability to retrieve images efficiently. The best result is obtained when text-based search criteria are combined with content-based criteria and when a coarse granularity is used for content analysis     

AESOP: an architectural relational database

This paper describes the principles and implementation of AESOP, an architectural relational database which aims to integrate building design procedures and to provide good graphic manipulation facilities. It is also intended to be highly flexible in that it allows inexact as well as exact data, can support a variety of user views of data and modes of use, and can accommodate itself to new techniques as these become available. AESOP uses ‘fuzzy’ logic. It holds information in the form of components which are linked lists of records, or relations, which are linked lists of tuples containing pointers to records, or, more generally, as directed graphs with records at the boundaries. New relations can be defined interactively from old using set theoretic operations thus enabling the logical operations of the first order predicate calculus to be performed on relations. The database also holds internal procedures called maps. Maps may be used to transform data as required by the user or to test the properties of a design proposal. New maps can be defined interactively from existing maps by applying composition, recursion or the logical operations of the first order calculus. An example of the use of the principles of fuzzy logic for guiding the selection of building fabri? is given. Some tests of AESOP in practice on live problems are noted. It is concluded that AESOP could provide a useful framework for a CAAD system and that further work is required in two areas, the user interface, and operations involving geometrical relations.     

基于关系型数据库的公文工作流系统

根据公文流转的特点,对工作流模型进行了简化,并将这个简化模型建立在关系型数据库中。简化后的公文工作流模型呈树型结构,公文从树的枝节点向根节点流转、汇合,整个流转过程没有分支。讨论了公文工作流在关系型数据库中的数据存储结构,并以一个部级单位为例,给出了建立在这套模型之上的公文系统的实现方式。     

A medical information relational database system (MIRDS)

A medical information relational database system (MIRDS) which is resident on a relational database machine and is accessed via microcomputers has been created for a pediatric pulmonary division of a research hospital. The power and flexibility of MIRDS has permitted the integration of clinical tasks, research interests, and laboratory functions. Procedures have been devised to assure data integrity, allow flexibility in data retrievals, produce standardized report formats, and permit data access for users with a wide range of query expertise. There are few impediments to the integration of additional clinical, research, and laboratory functions as the system evolves.     

Micro: A normalization tool for relational database designers

Normalization is a major task in relational database design. Although normalization algorithms have been developed, very few commercial design tools are available to assist the normalization satisfactorily. In this paper, we present a prototype system Micro for automatic normalization. We have developed a simple algorithm for 2NF normalization, and used the abstract algorithms reported in the literature for 3NF and BCNF normalization. We employ efficient data structures on functional dependencies and relation schemes to improve the performance of these algorithms. Micro enforces a certain fixed order among functional dependencies to deal with the non-deterministic feature that is associated with the original algorithms. Micro provides a windowing user interface through which the database designer can specify functional dependencies easily and generate real normalized tables. Through Micro, we wish to demonstrate that the automation of normalization is practical.     

Clustering and aggregation of relational data with applications to image database categorization

In this paper, we introduce a new algorithm for clustering and aggregating relational data (CARD). We assume that data is available in a relational form, where we only have information about the degrees to which pairs of objects in the data set are related. Moreover, we assume that the relational information is represented by multiple dissimilarity matrices. These matrices could have been generated using different sensors, features, or mappings. CARD is designed to aggregate pairwise distances from multiple relational matrices, partition the data into clusters, and learn a relevance weight for each matrix in each cluster simultaneously. The cluster dependent relevance weights offer two advantages. First, they guide the clustering process to partition the data set into more meaningful clusters. Second, they can be used in subsequent steps of a learning system to improve its learning behavior. The performance of the proposed algorithm is illustrated by using it to categorize a collection of 500 color images. We represent the pairwise image dissimilarities by six different relational matrices that encode color, texture, and structure information.