Skyline Diagram: Finding the Voronoi Counterpart for Skyline Queries

被引:9
|
作者
Liu, Jinfei [1 ,2 ]
Yang, Juncheng [1 ]
Xiong, Li [1 ]
Pei, Jian [3 ,4 ]
Luo, Jun [5 ]
机构
[1] Emory Univ, Dept Math & Comp Sci, Atlanta, GA 30322 USA
[2] Georgia Inst Technol, Coll Comp, Atlanta, GA 30332 USA
[3] JD Com, Beijing, Peoples R China
[4] Simon Fraser Univ, Burnaby, BC, Canada
[5] Chinese Acad Sci, Machine Intelligence Ctr, Lenovo & SIAT, Beijing, Peoples R China
基金
加拿大自然科学与工程研究理事会;
关键词
D O I
10.1109/ICDE.2018.00065
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Skyline queries are important in many application domains. In this paper, we propose a novel structure Skyline Diagram, which given a set of points, partitions the plane into a set of regions, referred to as skyline polyominos. All query points in the same skyline polyomino have the same skyline query results. Similar to kth-order Voronoi diagram commonly used to facilitate k nearest neighbor (kNN) queries, skyline diagram can be used to facilitate skyline queries and many other applications. However, it may be computationally expensive to build the skyline diagram. By exploiting some interesting properties of skyline, we present several efficient algorithms for building the diagram with respect to three kinds of skyline queries, quadrant, global, and dynamic skylines. Experimental results on both real and synthetic datasets show that our algorithms are efficient and scalable.
引用
收藏
页码:653 / 664
页数:12
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