  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 $l$ 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-$l$
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.
