Aggregate measures summarizing subsets of data are valuable in exploratory analysis and decision support, especially when dependent aggregations can be easily specified and computed. A novel class of queries, called composite subset measures, was previously introduced to allow correlated aggregate queries to be easily expressed. This paper considers how to evaluate composite subset measure queries using a large distributed system. We describe a cross-node data redistribution strategy that takes into account the nested structure of a given query. The main idea is to group data into blocks in "cube space", such that aggregations can be generated locally within each block, leveraging previously proposed optimizations per-block. The partitioning scheme allows overlap among blocks so that sliding window aggregation can be handled. Furthermore, it also guarantees that the final answer is the union of local results with no duplication and there is no need for the expensive data combination step. We identify the most important partitioning parameters and propose an optimization algorithm. We also demonstrate effectiveness of the optimizer to minimize the query response time.