A formal-concept-lattice driven approach for skyline refinement

Abstract

Skyline queries constitute an appropriate tool that can help users to make intelligent decisions in the presence of multidimensional data when different, and often contradictory criteria are to be taken into account. Based on the concept of Pareto dominance, the skyline process extracts the most interesting (not dominated in sense of Pareto) objects from a set of data. However, this process often leads to a huge skyline, which is less informative for the end-users. In this paper, we propose an efficient approach to refine the skyline and reduce its size, using the principle of the formal concepts analysis. The basic idea is to build a formal concept lattice for skyline objects based on the minimal distance between each concept and the target concept. We show that the refined skyline is given by the concept that contains k objects (where k is a user-defined parameter) and has the minimal distance to the target concept. A set of experiments are conducted to demonstrate the effectiveness and efficiency of our approach.