Data partition and parallel evaluation of Datalog programs

Parallel bottom-up evaluation provides an alternative for the efficient evaluation of logic programs. Existing parallel evaluation strategies are neither effective nor efficient in determining the data to be transmitted among processors. In this paper, we propose re different strategy, for general Datalog programs, that is based on the partitioning of data rather than that of rule instantiations. The partition and processing schemes defined in this paper are more general than those in existing strategies. A parallel evaluation algorithm is given based on the semi-naive bottom-up evaluation. A notion of potential usefulness is recognized as a data transmission criterion to reduce, both effectively and efficiently, the amount of data transmitted. Heuristics and algorithms are proposed for designing the partition and processing schemes for a given program. Results from an experiment show that the strategy proposed in this paper has many promising features