Mining Thick Skylines over Large Databases

People recently are interested in a new operator, called skyline [3], which returns the objects that are not dominated by any other objects with regard to certain measures in a multi-dimensional space. Recent work on the skyline operator [3,15,8,13,2] focuses on efficient computation of skylines in large databases. However, such work gives users only thin skylines, i.e., single objects, which may not be desirable in some real applications. In this paper, we propose a novel concept, called thick skyline, which recommends not only skyline objects but also their nearby neighbors within -distance. Efficient computation methods are developed including (1) two efficient algorithms, Sampling-and-Pruning and Indexing-and-Estimating, to find such thick skyline with the help of statistics or indexes in large databases, and (2) a highly efficient Microcluster-based algorithm for mining thick skyline. The Microcluster-based method not only leads to substantial savings in computation but also provides a cocise representation of the thick skyline in the case of high cardinalities. Our experimental performance study shows that the proposed methods are both efficient and effective.