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Scalable skyline computation using a balanced pivot selection technique

机译：使用平衡枢轴选择技术的可扩展天际线计算

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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.
机译：最近，天际线查询作为数据库社区中的替代决策运营商受到了广泛关注。常规的天际线算法主要集中在优化点的优势上，以尽可能高效地去除非天际线点，但忽略了点的不可比性以绕过不必要的比较。为了设计可伸缩的天际线算法，我们首先分析一个同时兼顾优势和不可比性的成本模型，然后开发一种新颖的技术来选择成本最优点（称为枢轴点），以最大程度地减少基于点的空间中的比较次数分区。然后，我们在现有的基于排序和分区的算法中实现建议的枢轴点选择技术。对于点插入/删除，我们还将讨论如何使用从基于递归点的空间分区派生的天空树维护当前天际线。此外，我们使用天空树为k个代表性天际线设计了一种有效的贪婪算法。实验结果表明，所提出的算法比最新算法要快得多。

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