Scalable skyline computation using object-based space partitioning

Abstract

The skyline operator returns from a set of multi-dimensional objects a subset of superior objects that are not dominated by others. This operation is considered very important in multi-objective analysis of large datasets. Although a large number of skyline methods have been proposed, the majority of them focuses on minimizing the I/O cost. However, in high dimensional spaces, the problem can easily become CPU-bound due to the large number of computations required for comparing objects with current skyline points while scanning the database. Based on this observation, we propose a dynamic indexing technique for skyline points that can be integrated into state-of-the-art sort-based skyline algorithms to boost their computational performance. The new indexing and dominance checking approach is supported by a theoretical analysis, while our experiments show that it scales well with the input size and dimensionality not only because unnecessary dominance checks are avoided but also because it allows efficient dominance checking with the help of bitwise operations.