摘要: Given a set of multi-dimensional points, the skyline contains best points according to any preference function that is monotone on all axes. In practice, applications require analysis usually provide numerous candidate attributes, and various users depending their interests may issue queries regarding different (small) subsets dimensions. Formally, given relation with large number (e.g.,ge 10) query aims at finding in an arbitrary subspace low dimensionality (e.g., 2). The existing algorithms do not support retrieval efficiently because they (i) scanning entire database least once, or (ii) are optimized for one particular but incur significant overhead other subspaces. this paper, we propose technique SUBSKY which settles problem using single B-tree, can be implemented relational database. core transformation converts data 1D values, enables several effective pruning heuristics. Extensive experiments real confirm outperforms alternative approaches significantly both efficiency scalability.