论文标题
灵活的天际线，遗憾的最小化和天际线排名：一个比较，以了解如何选择正确的方法
Flexible skylines, regret minimization and skyline ranking: a comparison to know how to select the right approach
论文作者
论文摘要
最近的研究指出了有关经典Top-K查询和天际线查询的一些局限性。排名查询强制用户提供特定的评分函数，这可能导致不准确的估计分配权重，因此可能会导致不准确的结果。天际线方法使得很难始终取回准确的结果，尤其是当用户必须处理该数据集的元素由语义上不同的属性定义时。因此，为了提高最终解决方案的质量，已经开发和提出了新技术：在这里，我们将讨论灵活的天际线，遗憾的最小化和天际线排名方法。我们介绍了三个不同的操作员之间的比较，回忆起他们的行为方式并为读者定义指南，以便他们更容易地确定这三个是哪一个是解决问题的最佳技术。
Recent studies pointed out some limitations about classic top-k queries and skyline queries. Ranking queries impose the user to provide a specific scoring function, which can lead to the exclusion of interesting results because of the inaccurate estimation of the assigned weights. The skyline approach makes it difficult to always retrieve an accurate result, in particular when the user has to deal with a dataset whose tuples are defined by semantically different attributes. Therefore, to improve the quality of the final solutions, new techniques have been developed and proposed: here we will discuss about the flexible skyline, regret minimization and skyline ranking approaches. We present a comparison between the three different operators, recalling their way of behaving and defining a guideline for the readers so that it is easier for them to decide which one, among these three, is the best technique to apply to solve their problem.
