On efficient top-k query processing in highly distributed environments

Lately the advances in centralized database management systems show a trend towards supporting rank-aware query operators, like top-k, that enable users to retrieve only the most interesting data objects. A challenging problem is to support rank-aware queries in highly distributed environments. In this paper, we present a novel approach, called SPEERTO, for top-k query processing in large-scale peer-to-peer networks, where the dataset is horizontally distributed over the peers. Towards this goal, we explore the applicability of the skyline operator for efficiently routing top-k queries in a large super-peer network. Relying on a thresholding scheme, SPEERTO returns the exact results progressively to the user, while the number of queried super-peers and transferred data is minimized. Finally, we propose different variations of SPEERTO that allow balancing between transferred data volume and response time. Through simulations we demonstrate the feasibility of our approach.