灵活的天际线，遗憾最小化和天际线排名：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
摘要：
最近的研究指出了经典的top-k查询和skyline查询的一些局限性。排序查询迫使用户提供特定的评分函数，这可能导致由于分配的权重的不准确估计而排除感兴趣的结果。skyline方法使得总是检索准确的结果变得困难，特别是当用户必须处理其元组由语义上不同的属性定义的数据集时。因此，为了提高最终解决方案的质量，已经开发并提出了新的技术：在这里，我们将讨论灵活的天际线，遗憾最小化和天际线排名方法。我们提出了三个不同的运营商之间的比较，回顾他们的行为方式，并为读者定义了一个指导方针，以便他们更容易决定哪一个，在这三个，是最好的技术，适用于解决他们的问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
