BSkyTree: scalable skyline computation using a balanced pivot selection

Skyline queries have gained a lot of attention for multicriteria analysis in large-scale datasets. While existing skyline algorithms have focused mostly on exploiting data dominance to achieve efficiency, we propose that data incomparability should be treated as another key factor in optimizing skyline computation. Specifically, to optimize both factors, we first identify common modules shared by existing non-index skyline algorithms, and then analyze them to develop a cost model to guide a balanced pivot point selection. Based on the cost model, we lastly implement our balanced pivot selection in two algorithms, BSkyTree-S and BSkyTree-P, treating both dominance and incomparability as key factors. Our experimental results demonstrate that proposed algorithms outperform state-of-the-art skyline algorithms up to two orders of magnitude. Categories and Subject Descriptors H.2.4 [Database Management]: Systems—Query processing General Terms Algorithms, Experimentation, Performance Key...

• Type: Conference
• Year: 2010
• Where: EDBT
• Authors: Jongwuk Lee, Seung-won Hwang
• Keywords: Balanced Pivot, Database, EDBT 2010, Non-index Skyline Algorithms, Skyline Algorithms