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 processing 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 into account also the reduction state of the relations. This algorithm fully reduces the relations at each site. We then present 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 strategy for response time minimization. We also report on the results of a set of experiments that show that our algorithms outperform a number of the recently proposed methods for total processing time and response time minimization.; In multidatabase systems, we address the problem of data integration and query processing in the presence of conflicting values for the attributes that correspond to different instances of the same real-world object appearing in multiple roles. We present a new method for schema and data integration in multidatabase systems that presents the answer to the query as a set of sets representing the distinct intersections between the relations representing the various roles of interest. We present extensions to MSQL that allow us to express role-sets and quantifiers applied to them. We further develop a strategy for query processing based on our role-based model. Our method allows for local selection for all queries with/without aggregation and thus reduces the amount of data transmitted. In addition, it makes efficient use of merge-sort techniques to generate the role-sets and compute joins at the global query site.
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