The major strength of relational databases lies in their ability to process data in a set-at-a-time manner. An important drawback, however, is their inability to handle rules. Aninformation system must allow users to define, represent and manipulate rules just as easily as data and treat them in a common framework. Such a system is called a knowledge-based system. The objective of this research is to investigate the feasibility of developing such a framework for integrating rules and data within a relational database system. This research develops and evaluates techniques for integration in this manner, and highlights the strengths and weaknesses of such an approach. Consequently, it focuses on the following issues: extending a relational language like SQL to express rules, storing and manipulating rules in an efficient manner, developing query processing algorithms (this will also involve designing new data structures), implementing the algorithms and evaluating their performance. This work draws to a large extent on existing literature and theory in the area of relational database systems, deductive databases, and also on existing work in the areas of artificial intelligence and expert systems. The findings will be useful for the designers of next-generation database systems.
