SkyDiver: a framework for skyline diversification

Skyline queries have attracted considerable attention by the database community during the last decade, due to their applicability in a series of domains. However, most existing works tackle the problem from an efficiency standpoint, i.e., returning the skyline as quickly as possible. The user is then presented with the entire skyline set, which may be in several cases overwhelming, therefore requiring manual inspection to come up with the most informative data points. To overcome this shortcoming, we propose a novel approach in selecting the k most diverse skyline points, i.e., the ones that best capture the different aspects of both the skyline and the dataset they belong to. We present a novel formulation of diversification which, in contrast to previous proposals, is intuitive, because it is based solely on the domination relationships among points. Consequently, additional artificial distance measures (e.g., Lp norms) among skyline points are not required. We present efficient approaches in solving this problem and demonstrate the efficiency and effectiveness of our approach through an extensive experimental evaluation with both real-life and synthetic data sets.