Estimation of network proximity among nodes is an important building block in several applications like service selection and composition, multicast tree formation, and overlay construction. Recently, scalable techniques have been proposed to estimate inter-node latencies, including network coordinate systems like GNP and Vivaldi. However, existing mechanisms for querying such information do not scale well to a very large number of nodes, when one wants to accurately find a set of nodes globally closest to a given node. In this paper we are concerned with distributing the position data among a set of infrastructure nodes, and propose ways of partitioning and querying this data. The trade-offs between accuracy and overhead in this distributed infrastructure are explored. We evaluate our solution through simulations with real and synthetic network measurement data.