SKYPEER: Efficient Subspace Skyline Computation over Distributed Data

Abstract

Skyline query processing has received considerable attention in the recent past. Mainly, the skyline query is used to find a set of non dominated data points in a multidimensional dataset. While most previous work has assumed a centralized setting, in this paper we address the efficient computation of subspace skyline queries in large-scale peer-to-peer (P2P) networks, where the dataset is horizontally distributed across the peers. Relying on a super-peer architecture we propose a threshold based algorithm, called SKYPEER, which forwards the skyline query requests among peers, in such a way that the amount of transferred data is significantly reduced. For efficient subspace skyline processing, we extend the notion of domination by defining the extended skyline set, which contains all data elements that are necessary to answer a skyline query in any arbitrary subspace. We prove that our algorithm provides the exact answers and we present optimization techniques to reduce communication cost and execution time. Finally, we provide an extensive experimental evaluation showing that SKYPEER performs efficiently and provides a viable solution when a large degree of distribution is required.