ADAPTIVE ALGORITHMS FOR JOIN PROCESSING IN DISTRIBUTED DATABASE-SYSTEMS

Distributed query processing algorithms usually perform data reduction by using a semijoin program, but the problem with these approaches is that they still require an explicit join of the reduced relations in the final phase. We introduce an efficient algorithm for join processi ng in distributed database systems that makes use of bipartite graphs in order to reduce data communication costs and local processing costs . The bipartite graphs represent the tuples that can be joined in two relations taking also into account the reduction state of the relation s. This algorithm fully reduces the relations at each site. We then pr esent an adaptive algorithm for response time optimization that takes into account the system configuration, i.e., the additional resources available and the data characteristics, in order to select the best st rategy for response time minimization. We also report on the results o f a set of experiments which show that our algorithms outperform a num ber of the recently proposed methods for total processing time and res ponse time minimization.