Analysis of Sequential SLG Evaluation

SLG is a table-oriented resolution method that has the ability to combine the deductive database and logic programming paradigms. As an example of its applicability to deductive databases, SLG terminates and has polynomial data complexity for programs with nite models. In terms of the logic programming (and non-monotonic) paradigms, SLG computes the well-founded model for non-stratiied programs, and has been extended to compute 3-valued stable model semantics. This paper presents performance analysis and comparisons of the SLG-WAM, an abstract machine for executing SLG. Firstly, the results indicate that the overhead of the SLG-WAM compared to a similar WAM implementation is minimal for executing SLD programs. Furthermore, results are presented to indicate tradeoos between SLD and SLG evaluation for a variety of programs. Surprisingly , SLG derivation may be expected to be competitive with SLD derivation on numerous datalog programs, even when SLD terminates and contains no redundant subcomputations. Finally, performance comparisons indicate that the SLG-WAM is about an order of magnitude faster than current deductive database systems even for datalog queries. These results call into question traditional arguments about top-down versus bottom-up evaluation eeciency, and may provide information of use in designing and implementing the next generation of logic programming and deductive database systems.