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Open AccessArticle

Efficient Geometric Pruning Strategies for Continuous Skyline Queries

College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 211106, China
School of Computer Science and Engineering, The University of New South Wales, Sydney, NSW 2052, Australia
Author to whom correspondence should be addressed.
Academic Editors: Norbert Bartelme and Wolfgang Kainz
ISPRS Int. J. Geo-Inf. 2017, 6(3), 91;
Received: 15 November 2016 / Revised: 17 March 2017 / Accepted: 19 March 2017 / Published: 22 March 2017
PDF [3620 KB, uploaded 22 March 2017]


The skyline query processing problem has been well studied for many years. The literature on skyline algorithms so far mainly considers static query points on static attributes. With the popular usage of mobile devices along with the increasing number of mobile applications and users, continuous skyline query processing on both static and dynamic attributes has become more pressing. Existing efforts on supporting moving query points assume that the query point moves with only one direction and constant speed. In this paper, we propose continuous skyline computation over an incremental motion model. The query point moves incrementally in discrete time steps with no restrictions and predictability. Geometric properties over incremental motion denoted by a kinetic data structure are utilized to prune the portion of data points not included in final skyline query results. Various geometric strategies are asymptotically proposed to prune the querying dataset, and event-driven mechanisms are adopted to process continuous skyline queries. Extensive experiments under different data sets and parameters demonstrate that the proposed method is robust and more efficient than multiple snapshots of I/O optimal branch-and-bound skyline (BBS) skyline queries. View Full-Text
Keywords: continuous skyline queries; incremental motion model; geometric properties; event driven mechanisms; grid file index continuous skyline queries; incremental motion model; geometric properties; event driven mechanisms; grid file index

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Zheng, J.; Chen, J.; Wang, H. Efficient Geometric Pruning Strategies for Continuous Skyline Queries. ISPRS Int. J. Geo-Inf. 2017, 6, 91.

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