Maintaining data-driven rules in databases

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Abstract
A new model with invariant-based language effectively handles data-driven rules in databases and uses the rules' inherent semantic properties and supporting mechanisms to meet high-level language requirements. It is an extension of the basic PARDES model developed by Opher Etzion in 1990 to support derivations and integrity constraints in databases. The model's invariant-based language, unlike other programming languages, can follow data- driven rules' semantic properties. Such rules are activated by modifications of data items in a database, and they play an important role in many applications that maintain complex relationships between data items or interdependencies between parts of the database. Applications include expert systems, real- time databases, simulations, and decision-support systems. The authors present requirements for choosing an adequate programming style that uses data-driven rules. These requirements are based on software-engineering criteria such as compatibility with a high-level language and verifiability of the rule language. The authors show that contemporary database programming styles fail to meet these requirements, and they present the invariant- based language as a viable solution.

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