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Issue No. 03 - March (2009 vol. 21)
ISSN: 1041-4347
pp: 384-400
Lin Zhu , Fudan University, Shanghai
Yufei Tao , Chinese University of Hong Kong, Hong Kong
Shuigeng Zhou , Fudan University, Shanghai
We consider skyline computation when the underlying dataset is horizontally partitioned onto geographically distant servers \color{red} that are connected to the Internet. \color{black} The existing solutions are not suitable for our problem, because they have at least one of the following drawbacks: (i) applicable only to distributed systems adopting vertical partitioning or restricted horizontal partitioning, (ii) effective only when each server has limited computing and communication abilities, and (iii) optimized only for skyline search in subspaces but inefficient in the full space. This paper proposes an algorithm, called {\em feedback-based distributed skyline} (FDS), to support arbitrary horizontal partitioning. \color{red} FDS aims at minimizing the network bandwidth, measured in the number of tuples transmitted over the network. \color{black} The core of FDS is a novel feedback-driven mechanism, where the coordinator iteratively transmits certain feedback to each participant. Participants can leverage such information to prune a large amount of local data, which otherwise would need to be sent to the coordinator. Extensive experimentation confirms that FDS significantly outperforms alternative approaches in both effectiveness and progressiveness.
Distributed databases, Spatial databases

L. Zhu, Y. Tao and S. Zhou, "Distributed Skyline Retrieval with Low Bandwidth Consumption," in IEEE Transactions on Knowledge & Data Engineering, vol. 21, no. , pp. 384-400, 2008.
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