Bandwidth-constrained distributed skyline computation
Proceedings of the Eighth ACM International Workshop on Data Engineering for Wireless and Mobile Access
AGiDS: A Grid-Based Strategy for Distributed Skyline Query Processing
Globe '09 Proceedings of the 2nd International Conference on Data Management in Grid and Peer-to-Peer Systems
Efficient execution plans for distributed skyline query processing
Proceedings of the 14th International Conference on Extending Database Technology
Distributed cache indexing for efficient subspace skyline computation in p2p networks
DASFAA'10 Proceedings of the 15th international conference on Database Systems for Advanced Applications - Volume Part I
Distributed skyline processing: a trend in database research still going strong
Proceedings of the 15th International Conference on Extending Database Technology
A survey of skyline processing in highly distributed environments
The VLDB Journal — The International Journal on Very Large Data Bases
Energy-efficient skyline query optimization in wireless sensor networks
Wireless Networks
Daisy: the center for data-intensive systems at Aalborg University
ACM SIGMOD Record
Energy-efficient filtering for skyline queries in cluster-based sensor networks
Computers and Electrical Engineering
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An interesting problem in peer-based data management is efficient support for skyline queries within a multiattribute space. A skyline query retrieves from a set of multidimensional data points a subset of interesting points, compared to which no other points are better. Skyline queries play an important role in multi-criteria decision making and user preference applications. In this paper, we address the skyline computing problem in a structured P2P network. We exploit the iMinMax(θ) transformation to map high-dimensional data points to 1-dimensional values. All transformed data points are then distributed on a structured P2P network called BATON, where all peers are virtually organized as a balanced binary search tree. Subsequently, a progressive algorithm is proposed to compute skyline in the distributed P2P network. Further, we propose an adaptive skyline filtering technique to reduce both processing cost and communication cost during distributed skyline computing. Our performance study, with both synthetic and real datasets, shows that the proposed approach can dramatically reduce transferred data volume and gain quick response time.