A great deal of work on automating systems design and development has been carried out, especially in the database area. Systems that semi-automate the database design process have been developed. These systems are interactive in that they may need to ask the user (usually, a database designer) for clarification. The result is that the system asks questions to the user that make the system look less intelligent than it should. This general type of problem has long been recognized with a proposed approach to overcoming it being the incorporation of common sense knowledge into a design system. The View Creation System is an expert system that plays the role of a database designer. With it, a user knowing little about database technology can express his or her database design requirements, which are represented by an entity-relationship model and then translated into a normalized relational model. The system contains a great deal of knowledge about database design, but little, if any, about the user’s application. This forces the user to specify many trivial facts that would be known by any human designer. To overcome this limitation, a Common Sense Business Reasoner is being developed that has a knowledge base containing general knowledge about the world and a reasoning tool to apply this knowledge to a database design task. An empirical study is carried out to simulate and assess the effectiveness of adding the Common Sense Business Reasoner to the View Creation System.

Research devoted to developing knowledge-based tools for database design has demonstrated that it is possible to encode a great deal of process knowledge about database design in a (knowledge-based) computer program. However, experience with these tools shows that the contribution of an expert human designer extends beyond his or her knowledge of database design techniques. The paper discusses the application of an approach, called Naive Semantics (NS), to simulate the contributions made to a design based on the designer's general knowledge. Naive semantics involves the use of an extensible store of generally understood knowledge about the world. The paper describes the types of information that could be included in a Naive Semantics knowledge base and how that knowledge might be applied to increase the effectiveness of automated database design systems. Results of various implementations of ontologies based on Naive Semantics are discussed.

An approach to organizing storage in database systems is presented that, under a wide range of conditions, saves both storage space and processing time. Text values in a database are replaced by short, fixed-length, rank-preserving numeric tokens. The actual values are stored in separate, non-redundant storage. Database operations that depend only on the relative magnitude of data values can be performed directly on the tokens. Tokenization is shown to improve database performance most in situations where there are a lot of ad hoc queries and a low volume of database insertions relative to other operations.

Automated database design systems embody knowledge about the database design process. However, their lack of knowledge about the domains for which databases are being developed significantly limits their usefulness. A methodology for acquiring and using general world knowledge about business for database design has been developed and implemented in a system called the Common Sense Business Reasoner, which acquires facts about application domains and organizes them into a a hierarchical, context-dependent knowledge base. This knowledge is used to make intelligent suggestions to a user about the entities, attributes, and relationships to include in a database design. A distance function approach is employed for integrating specific facts, obtained from individual design sessions, into the knowledge base (learning) and for applying the knowledge to subsequent design problems (reasoning).

Semantic integrity constraints are used to help ensure that a database accurately reflects the real world in structure and content. Existing database design methodologies, however, do not often include a comprehensive procedure for identifying such constraints and incorporating them into a design. A number of knowledge-based systems for database design have been developed with varying degrees of emphasis on, and approaches to, this task. A representative sample of these systems is reviewed for their ability to identify, represent and validate integrity constraints. In order to examine how constraints are represented, a six-dimensional model of semantic integrity constraints based on that proposed by Fernandez et al. [32] is presented. Mechanisms used by the prototype systems to implement each of the dimensions are examined. This analysis is used to identify apparently successful approaches as well as areas where further research on incorporating and representing semantic integrity constraints in knowledge-based database design systems is needed.

A new data abstraction, called Materialization, is introduced to model a situation that occurs frequently in the real world and has important implications for database design. Materialization is the relationship between two entity types, one that represents a conceptual object, for example, a TV Model, and one that represents its corresponding concrete objects, in this case, actual TV Sets. The Materialization construct is formally defined and contrasted with other well-known data abstractions. Its design implications are presented in terms of the entity-relationship model and its translation into a relational model. Guidelines are offered for the proper employment of this relationship in database design methodologies, and a discussion is provided of why this constitutes an important data modeling construct.>

The View Creation System (VCS) is an expert system that engages a user in a dialogue about the information requirements for some application, develops an Entity-Relationship model for the user's database view, and then converts the E-R model to a set of Fourth Normal Form relations. This paper describes the knowledge base of VCS. That is, it presents a formal methodology, capable of mechanization as a computer program, for accepting requirements from a user, identifying and resolving inconsistencies, redundancies, and ambiguities, and ultimately producing a normalized relational representation. Key aspects of the methodology are illustrated by applying VCS's knowledge base to an actual database design task.