论文标题
关于使天际线更灵活的调查
A survey on making skylines more flexible
论文作者
论文摘要
顶部 - $ K $查询和天际线是在均匀的多维数据集中找到最有趣条目的两种最常见方法。但是，这两种策略都有一些缺点。 $ k $ $查询的精确指定非常具有挑战性，除了具有无法预测的输出红衣外，Skylines无法对特定方案进行定制。我们描述了一些旨在解决顶级$ K $查询和天际线的缺点的替代方法，并比较所有方法，以说明它们每个人所具有的所需属性中的哪种。
Top-$k$ queries and skylines are the two most common approaches to finding the most interesting entries in a homogeneous multi-dimensional dataset. However, both of these strategies have some shortcomings. Top-$k$ queries are very challenging to specify precisely and skylines are not customizable to specific scenarios, on top of having unpredictable output cardinalities. We describe some alternative methods aimed at addressing the shortcomings of top-$k$ queries and skylines and compare all approaches to illustrate which of the desired properties each of them possesses.
