A Heuristic for 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. It has been shown 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 efficient heuristic algorithm for rule allocation and study its performance experimentally. We represent a knowledge base as a hierarchy of rules. These rules are then allocated in a bottom-up fashion w.r.t. the hierarchy. The experimental results of the heuristic algorithm on random hierarchies as well as on hierarchies with varying heights are seen to be close to the optimal solution.
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