Upside-down Deduction

Over the recent years, several proposals were made to enhance database systems with automated reasoning. In this article we analyze two such enhancements based on meta-interpretation. We consider on the one hand the theorem prover Satchmo, on the other hand the Alexander and Magic Set methods. Although they achieve different goals and are based on distinct reasoning paradigms, Satchmo and the Alexander or Magic Set methods can be similarly described by upside-down meta-interpreters, i.e., meta-interpreters implementing one reasoning principle in terms of the other. Upside-down meta-interpretation gives rise to simple and efficient implementations, but has not been investigated in the past. This article is devoted to studying this technique. We show that it permits one to inherit a search strategy from an inference engine, instead of implementing it, and to combine bottom-up and top-down reasoning. These properties yield an explanation for the efficiency of Satchmo and a justification for the unconventional approach to top-down reasoning of the Alexander and Magic Set methods

A FRAMEWORK FOR DEDUCTIVE DATABASE DESIGN IM DECISION SUPPORT SYSTEMS

A three-level framework for design and implementation of deductive database management systems is described. The three levels consist of the abstraction, for abstracting the real world semantics, the language, for man-machine communication, and the environment, for specifying the hardware/software environment. This framework is applied to some representative systems. Based on the results, an architecture for a deductive database management system is proposed

A logic programming framework for modeling temporal objects

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Using Expert Knowledge in Database-Oriented Problem Solving

Database-oriented problem solving often involves the processing of deduction rules which may be recursive in relational database systems. In this kind of problem solving, expert knowledge plays an important role in the guidance of correct and efficient processing. This paper presents a modularized relational planner RELPLAN, which develops a knowledge directed inference and planning mechanism for efficient processing of deduction rules in relational DB systems