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We describe novel efficient techniques for the distributed evaluation of hierarchical aggregate selection queries over LDAP directory data, distributed across multiple autonomous directory servers. Such queries are useful for emerging applications like the directory enabled networks initiative. Our techniques follow the LDAP approach of distributed query evaluation by referrals, where each relevant server computes answers locally, and the LDAP client coordinates between directory servers. We make a conceptual separation between the identification of relevant servers and the distributed computation of answers. We focus on the challenging task of generating an efficient plan for evaluating hierarchical aggregate selection queries, which involves correlating directory entries across multiple servers. The key features of our plan are: 1) the network traffic consists of query answers, and auxiliary messages that depend only on the number of servers and the size of the query (not on the data size), 2) the coordination effort at the client is independent of the data size, and 3) potentially expensive server-to-server communication and coordination is avoided. We complement our analysis with experiments that show the robustness and scalability of our techniques for highly distributed directory query processing.