2018 IEEE 34th International Conference on Data Engineering (ICDE) (2018)
Apr 16, 2018 to Apr 19, 2018
The skyline of a data point set consists of the best points in the set, and is very important for multi-criteria decision making. One recent and important variant of the traditional skyline is group-based skyline, which aims to find the best groups of points in a given set. This paper brings forward an efficient approach, called minimum dominance search (MDS), to solve the g-skyline problem, a latest group-based skyline problem. MDS consists of two steps: In the first step, a novel g-skyline support structure, i.e., minimum dominance graph (MDG), is constructed to store all the points which may occur in g-skyline groups. In the second step, two searching algorithms are proposed to find g-skyline groups based on the MDG through two searching algorithms, and a skyline-combination based optimization strategy is employed to improve these two algorithms. The support for dynamic group sizes, i.e., a practical extension of the origin g-skyline problem, is provided through slightly modifying MDS. Comprehensive experiments are conducted on both synthetic and real-world data sets, and the results show that our algorithms are orders of magnitude faster than the state-of-the-art.
decision making, graph theory, optimisation, search problems
C. Wang, C. Wang, G. Guo, X. Ye and P. S. Yu, "Efficient Computation of G-Skyline Groups (Extended Abstract)," 2018 IEEE 34th International Conference on Data Engineering (ICDE), Paris, France, 2018, pp. 1769-1770.