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In this paper an approach to processing distributed queries that makes explicit use of redundant data is proposed. The basic idea is to focus on the dynamics of materialization, defined as the collection of data and partial results available for processing at any given time, as query processing proceeds. In this framework the role of data redudancy in maximizing parallelism and minimizing data movement is clarified. What results is not only the discovery of new algorithms but an improved framework for their evaluation.