Bottom-up evaluation and query optimization of well-founded models

Abstract

We present a bottom-up operational procedure for computing well-founded models of allowed programs with negation. This procedure provides a practical method of handling programs that involve unstratified negation in a manner that may be mixed with other evaluation approaches, such as semi-naive evaluation and various program transformations. We define classes of programs and sideways information of passing strategies (sips) for which the magic sets transformation preserves well-founded models with respect to the query. The classes of programs and sips we consider strictly subsume those already considered in the literature, and include stratified programs (with any choice of sips), left-to-right modularly stratified programs (with left-to-right sips) and arbitrary programs (with well-founded sips). For these programs and sips, our procedure for computing well-founded models is applicable to the rewritten programs, thus allowing increased efficiency of specializing a program for a query. Finally, we describe an extension of our bottom-up operational procedure that operates on the magic sets transformation of arbitrary programs with arbitrary sips, and computes query answers with respect to the well-founded models of the original program.