Rewriting and Efficient Computation of Bound Disjunctive Datalog Queries Rewriting and Efficient Computation of Bound Disjunctive Datalog Queries

A strong interest in enhancing Datalog programs by the capability of disjunction emerged in several areas, such as databases, artificial intelligence, logic programming, and so forth (Lobo, Minker & Rajasekar, 1992). Disjunctive rules have been profitably used in several contexts including knowledge representation, databases querying, and representation of incomplete information (Eiter, Gottlob & Mannila, 1997a; Gelfond & Lifschitz, 1991). Most of the works on disjunction are concerned with the definition of intuitive and expressive semantics, which are commonly based on the paradigm of minimal models (Abiteboul, Hull & Vianu, 1995; Lobo et al., 1992; Ullman, 1989). Disjunction in the head of rules increases the expressive power of the language but makes the computation of queries very difficult, as it allows the presence of multiple models whose number is generally exponential with respect to the size of the input (Abiteboul et al., 1995; Eiter et al., 1997a). Therefore, a great interest has been devoted to the definition of efficient algorithms for both computing the semantics of programs and answering queries. Most of the research has concentrated on the definition of efficient fixpoint algorithms computing the semantics of programs, and the more effective proposals are based on the evaluation of the (intelligent) ground instantiation of programs and on the use of heuristics (Leone, Rullo & Scarcello, 1997). However, different from standard datalog, for disjunctive queries, there are few effective methodologies which systematically utilize the query to propagate bindings into the body of the rules to avoid computing all the models of the program. The following example taken from Greco (2003), shows a program in which only a strict subset of the minimal models needs to be considered to answer the query.