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.
翻译：最近的研究表明了经典顶端查询和天线查询的一些局限性。 排名查询要求用户提供特定的评分功能, 这可能导致因分配重量估计不准确而排除有趣的结果。 天线方法使得总是难以检索准确的结果, 特别是当用户必须处理一个数据集, 该数据集的图象由词义上的不同属性来界定。 因此, 为了提高最终解决方案的质量, 开发并提出了新的技术 : 我们将在这里讨论灵活天空线、 遗憾最小化和天线排名方法 。 我们比较了三个不同的操作员, 回顾它们的行为方式, 并为读者制定指南, 以便他们更容易决定这三种数据中哪一种是解决问题的最佳方法 。
相关内容
微信扫码咨询专知VIP会员