Scalable skyline computation using a balanced pivot selection technique

• We analyze a cost model to estimate the number of comparisons depending on a selected pivot point.
• We develop a pivot selection technique that balances on both the dominance and the incomparability of points.
• We propose an efficient method for skyline updates using a recursive point-based space partitioning scheme.
• We devise an efficient algorithm for a k representative skyline to identify meaningful k skyline points.
• We evaluate the proposed algorithms with state-of-the-art algorithms in both synthetic and real-life datasets.

Skyline queries have recently received considerable attention as an alternative decision-making operator in the database community. The conventional skyline algorithms have primarily focused on optimizing the dominance of points in order to remove non-skyline points as efficiently as possible, but have neglected to take into account the incomparability of points in order to bypass unnecessary comparisons. To design a scalable skyline algorithm, we first analyze a cost model that copes with both dominance and incomparability, and develop a novel technique to select a cost-optimal point, called a pivot point, that minimizes the number of comparisons in point-based space partitioning. We then implement the proposed pivot point selection technique in the existing sorting- and partitioning-based algorithms. For point insertions/deletions, we also discuss how to maintain the current skyline using a skytree, derived from recursive point-based space partitioning. Furthermore, we design an efficient greedy algorithm for the k representative skyline using the skytree. Experimental results demonstrate that the proposed algorithms are significantly faster than the state-of-the-art algorithms.