Rule Allocation in Distributed Deductive Database Systems

Allocation of rules to sites in a distributed deductive database system is an important and challenging task especially for a large knowledge base. We identify communication cost in rule execution to be the primary basis for decomposing a global knowledge base into clusters for their allocation to sites. We show that the problem of optimal allocation is a 0-1 quadratic programming problem, which has prohibitive execution times for large knowledge bases. We propose an eecient heuristic algorithm for rule allocation and study its performance experimentally. We represent a knowledge base as a hierarchy and characterize it in terms of height and inherent clusters with overlaps. The experimental results of the heuristic algorithm on random hierarchies as well as on hierarchies with varying heights and overlaps are seen to be close to the optimal solution.