Efficient Skycube Computation using Bitmaps derived from Indexes

Abstract

Skyline queries have been increasingly used in multi-criteria decision making and data mining applications. They retrieve a set of interesting points from a potentially large set of data points. A point is said to be interesting if it is as good or better in all dimensions and better in at least one dimension. Skyline Cube (Skycube) consists of skylines of all possible non-empty subsets of a given set of dimensions. In this paper, we propose two algorithms for computing skycube using bitmaps that are derivable from indexes. Point-based skycube algorithm is an improvement over the existing Bitmap algorithm, extended to compute skycube. Point-based algorithm processes one point at a time to check for skylines in all subspaces. Value-based skycube algorithm views points as value combinations and probes entire search space for potential skyline points. It significantly reduces bitmap access for low cardinality dimensions. Our experimental study shows that the two algorithms strictly dominate, or at least comparable to, the current skycube algorithm in most of the cases, suggesting that such an approach could be an useful addition to the set of skyline query processing techniques.