Given a set of multi-dimensional points, the skyline contains the best points according to any preference function that is monotone on all axes. In practice, applications that require skyline analysis usually provide numerous candidate attributes, and various users depending on their interests may issue queries regarding different (small) subsets of the dimensions. Formally, given a relation with a large number (e.g.,ge 10) of attributes, a query aims at finding the skyline in an arbitrary subspace with a low dimensionality (e.g., 2).

The existing algorithms do not support subspace skyline retrieval efficiently because they (i) require scanning the entire database at least once, or (ii) are optimized for one particular subspace but incur significant overhead for other subspaces. In this paper, we propose a technique SUBSKY which settles the problem using a single B-tree, and can be implemented in any relational database. The core of SUBSKY is a transformation that converts multi-dimensional data to 1D values, and enables several effective pruning heuristics. Extensive experiments with real data confirm that SUBSKY outperforms alternative approaches significantly in both efficiency and scalability.