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Optimization of Bound Disjunctive Queries with Constraints
"To Appear in Theory and Practice of Logic Programming (TPLP)" This paper
presents a technique for the optimization of bound queries over disjunctive
deductive databases with constraints. The proposed approach is an extension of
the well-known Magic-Set technique and is well-suited for being integrated in
current bottom-up (stable) model inference engines. More specifically, it is
based on the exploitation of binding propagation techniques which reduce the
size of the data relevant to answer the query and, consequently, reduces both
the complexity of computing a single model and the number of models to be
considered. The motivation of this work stems from the observation that
traditional binding propagation optimization techniques for bottom-up model
generator systems, simulating the goal driven evaluation of top-down engines,
are only suitable for positive (disjunctive) queries, while hard problems are
expressed using unstratified negation. The main contribution of the paper
consists in the extension of a previous technique, defined for positive
disjunctive queries, to queries containing both disjunctive heads and
constraints (a simple and expressive form of unstratified negation). As the
usual way of expressing declaratively hard problems is based on the
guess-and-check technique, where the guess part is expressed by means of
disjunctive rules and the check part is expressed by means of constraints, the
technique proposed here is highly relevant for the optimization of queries
expressing hard problems. The value of the technique has been proved by several
experiments.Comment: 35 page