Efficient Skyline and Top-k Retrieval in Subspaces

摘要: Skyline and top-k queries are two popular operations for preference retrieval. In practice, applications that require these usually provide numerous candidate attributes, whereas, depending on their interests, users may issue regarding different subsets of the dimensions. The existing algorithms inadequate subspace skyline/top-k search because they have at least one following defects: 1) scanning entire database once, 2) optimized but incur significant overhead other subspaces, or 3) demand expensive maintenance cost space consumption. this paper, we propose a technique SUBSKY, which settles both types by using purely relational technologies. core SUBSKY is transformation converts multidimensional data to one-dimensional (1D) values. These values indexed simple B-tree, allows us answer accessing fraction database. entails low overhead, equals updating traditional B-tree. Extensive experiments with real confirm our outperforms alternative solutions significantly in efficiency scalability.