Computing compressed multidimensional skyline cubes efficiently

Recently, the skyline computation and analysis have been extended from one single full space to multidimensional subspaces, which can lead to valuable insights in some applications. Particularly, compressed skyline cubes in the form of skyline groups and their decisive subspaces provide a succinct summarization and compression of multidimensional subspace skylines. However, computing skyline cubes remains a challenging task since the existing methods have to search an exponential number of nonempty subspaces for subspace skylines. In this paper, we propose a novel and efficient method, Stellar, which exploits an interesting skyline group lattice on a small subset of objects which are in the skyline of the full space. We show that this skyline group lattice is easy to compute and can be extended to the skyline group lattice on all objects. After computing the skyline in the full space, Stellar only needs to enumerate skyline groups and their decisive subspaces using the full space skyline objects. Avoiding searching for skylines in an exponential number of subspaces improves the efficiency and the scalability of subspace skyline computation substantially in practice. An extensive performance study verifies the merits of our new method.