Efficient skycube computation using point and domain-based filtering

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 not dominated by any other point. 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. The Point-based skycube algorithm is an improvement over the existing Bitmap algorithm, extended to compute skycube. The Point-based algorithm processes one point at a time to check for skylines in all subspaces. The Domain-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 a useful addition to the set of skyline query processing techniques.