LAUSR

Skyline, aiming at finding a Pareto optimal subset of points in a multi-dimensional dataset, has gained great interest due to its extensive use for multi-criteria analysis and decision making. The skyline consists of all points that are not dominated by any other points. It is a candidate set of the optimal solution, which depends on a specific evaluation criterion for optimum. However, conventional skyline queries, which return individual points, are inadequate in group querying case since optimal combinations are required. To address this gap, we study the skyline computation in the group level and propose efficient methods to find the Group-based skyline (G-skyline). For computing the front $l$l skyline layers, we lay out an efficient approach that does the search concurrently on each dimension and investigates each point in the subspace. After that, we present a novel structure to construct the G-skyline with a queue of combinations of the first-layer points. We further demonstrate that the G-skyline is a complete candidate set of top-$l$l solutions, which is the main superiority over previous group-based skyline definitions. However, as G-skyline is complete, it contains a large number of groups which can make it impractical. To represent the “contour” of the G-skyline, we define the Representative G-skyline (RG-skyline). Then, we propose a Group-based clustering (G-clustering) algorithm to find out RG-skyline groups. Experimental results show that our algorithms are several orders of magnitude faster than the previous work.