The use of null values in a relational database to represent incomplete and inapplicable information

Call number: LD2668 .T4 1985 W547Master of Scienc

ON THE EXPRESSIVE POWER OF THE RELATIONAL MODEL: A DATABASE DESIGNER\u27S POINT OF VIEW

The purpose of this paper is to introduce a framework for assessing the expressive power of data models and to apply this framework to the relational model of data. From a designer\u27s point of view, a data model such as the relational model should not only be formally defined and easy to understand, but should also provide a powerful set of constructs to model real-world phenomena. The expressive power of a data model, defined as the degree to which its constructs match with constructs encountered in reality, can be judged by two complementary principles: the interpretation principle and the representation principle. It is asserted that database designers attempt to minimize the number of ad hoc database constraints, and that a data model faithful to the two principles supports this design strategy. Subsequently, this constraint minimization strategy is used to assess the expressive power of a particular data model, i.e., the relational data model. The authors take the position that the expressive power of the relational model is not optimal, due to a lack of adherence to both the interpretation principle and the representation principle. The paper amplifies this position by means of a number of examples, all based on publications by Codd and Date

An approach to modeling database activity

Results in the field of data modeling currently suffer from many of the same ills which plagued data management systems in the late 1960's. Advanced semantic modeling systems such as the Semantic Data Model and the Relational Model/Tasmania are extremely complex to understand as well as somewhat ad hoc in design. Such systems capture only static snapshots of activity in the world being modeled. On the other hand, behavioral models which do attempt to model system dynamics typically provide less overall modeling power than comprehensive semantic models. Further, the specifications of behavior which can be expressed with such models are themselves static snapshots which are not integrated with other database objects.This work describes one approach for capturing dynamic relationships by distilling the concepts found in semantic and behavioral data models into a small number of flexible constructs. The resulting Prototype Activity Modeling System (PAMS) captures the containment, feedback, operational, and state dependency roles of entities in the world being modeled. Further, these definitions of database activity are captured as database objects (rather than as a schema) so as to allow dynamic manipulation of entity roles.The key concept of the approach is the bundle - a purposefully designed extension of time-proven relational database modeling concepts which includes support for presentation ordering and complex Cartesian aggregations. By applying the basic nested bundle principle, it is possible to obtain complex hierarchies of static structural information. The static templates so constructed, when used with a non-procedural query language and the value nomination principle which reduces relations to scalar values when necessary, provide a conventional database modeling system for applications. By extending these templates with the non-procedural thunk principle which embeds query specifications within object definitions, variations caused by dependencies within the application can cause the apparent contents of the database description to change. When further extended by the activity monitoring principle which records the interaction between the application and its environment, these dynamic templates can account for changes outside the scope of the application

InfoTech Update, Volume 4, Number 2, Winter 1995

https://egrove.olemiss.edu/aicpa_news/4951/thumbnail.jp

The Role of Logical Domain Models in Decision Support Systems

Principal "content" resources of a DSS are regarded as databases, organized according to data models, and algorithms, representing decision models. The use of logical domain models is here proposed as an intermediate, integrating between data models and decision models. The function is to provide a framework of qualitative inference providing higher level interpretations on databases, and provide a qualitative context for interpreting quantitative decision models

Application of Fuzzy Logic to Distress Analysis

Computer Science

Treatment of imprecision in data repositories with the aid of KNOLAP

Traditional data repositories introduced for the needs of business processing, typically focus on the storage and querying of crisp domains of data. As a result, current commercial data repositories have no facilities for either storing or querying imprecise/ approximate data. No significant attempt has been made for a generic and applicationindependent representation of value imprecision mainly as a property of axes of analysis and also as part of dynamic environment, where potential users may wish to define their “own” axes of analysis for querying either precise or imprecise facts. In such cases, measured values and facts are characterised by descriptive values drawn from a number of dimensions, whereas values of a dimension are organised as hierarchical levels. A solution named H-IFS is presented that allows the representation of flexible hierarchies as part of the dimension structures. An extended multidimensional model named IF-Cube is put forward, which allows the representation of imprecision in facts and dimensions and answering of queries based on imprecise hierarchical preferences. Based on the H-IFS and IF-Cube concepts, a post relational OLAP environment is delivered, the implementation of which is DBMS independent and its performance solely dependent on the underlying DBMS engine