On derived dependencies and connected databases

AbstractThis paper introduces a new class of deductive databases (connected databases) for which SLDNF-resolution never flounders and always computes ground answers. The class of connected databases properly includes that of allowed databases. Moreover the definition of connected databases enables evaluable predicates to be included in a uniform way. An algorithm is described which, for each predicate defined in a normal database, derives a propositional formula (groundness formula) describing dependencies between the arguments of that predicate. Groundness formulae are used to determine whether a database is connected. They are also used to identify goals for which SLDNF-resolution will never flounder and will always compute ground answers on a connected database

Depth-bounded bottom-up evaluation of logic programs

AbstractWe present here a depth-bounded bottom-up evaluation algorithm for logic programs. We show that it is sound, complete, and terminating for finite-answer queries if the programs are syntactically restricted to DatalognS, a class of logic programs with limited function symbols. DatalognS is an extension of Datalog capable of representing infinite phenomena. Predicates in DatalognS can have arbitrary unary and limited n-ary function symbols in one distinguished argument. We precisely characterize the computational complexity of depth-bounded evaluation for DatalognS and compare depth-bounded evaluation with other evaluation methods, top-down and Magic Sets among others. We also show that universal safety (finiteness of query answers for any database) is decidable for DatalognS