Scalable skyline computation using object-based space partitioning

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 becaus...

• Type: Conference
• Year: 2009
• Where: SIGMOD
• Authors: Shiming Zhang, Nikos Mamoulis, David W. Cheung
• Keywords: Current Skyline Points, Database, SIGMOD 2009, Skyline Operator Returns, Sort-based Skyline Algorithms