论文标题
通过受限的天际线从天际线操作员那里获得更多，遗憾的
Getting more from the skyline operator through restricted skylines, regret minimizing sets and skyline ordering: a survey on their properties and comparison
论文作者
论文摘要
给定一组多维点，Skyline运算符从此类数据集返回一组潜在的有趣点。这个受欢迎的操作员滤除了一组不受其他元素主导的元组，从而减小了可能大的初始数据集的大小。但是，随着数据集尺寸的增长，天际线操作员的查询结果可能具有很高的基数，这可能会使最终用户查看结果，从而产生相反的目标。此外，它不允许任何用户表达他们对属性的偏好，以控制数据集的基数并返回更令人满意的结果。在这项调查中，我们讨论并比较了旨在扩展天际线操作员功能的最新方法，并讨论了它们在现实世界中的适用性。我们展示了这些框架如何在数据集上运行，以及它们如何声称可以减轻来自天际线操作员的缺点。
Given a set of multidimensional points, the skyline operator returns a set of potentially interesting points from such a dataset. This popular operator filters out a set of tuples that are not dominated by other ones, reducing the size of a possibly large initial dataset. However, with the dataset dimensions growing, the query result of the skyline operator can have a high cardinality that could overwhelm the final user looking at the result, yielding the very opposite goal. Moreover, it doesn't allow any user to express their preferences over the attributes, in order to control the dataset cardinality and to return a more satisfying result. In this survey, we discuss and compare the more recent approaches developed to extend the skyline operator's functionalities, and we discuss their applicability in real-world scenarios. We show how these frameworks operate on the dataset and how they claim to mitigate the drawbacks coming from the skyline operator.
