When faced with a database containing millions of tuples, a user may be only interested in a (typically much) smaller representative subset. Recently, a query called the regret minimization query was proposed toward this purpose to create such a subset for users. Specifically, this query finds a set of tuples that minimizes the user regret (measured by how far the user’s favorite tuple in the selected set is from his/her favorite tuple in the whole database). The regret minimization query was shown to be very useful in bridging the best worlds between two existing well-known queries, top-k queries and skyline queries: Like top-k queries, the total number of tuples returned in this new query is controllable, and like skyline queries, this new query does not require a user to specify any preference function. Thus, it has attracted a lot of attention from researchers in the database community. Various methods were proposed for regret minimization. However, despite the abundant research effort, there is no systematic comparison among the existing methods. This paper surveys this interesting and evolving research topic by broadly reviewing and comparing the state-of-the-art methods for regret minimization. Moreover, we study different variants of the regret minimization query that has garnered considerable attention in recent years and present some interesting problems that have not yet been addressed in the literature. We implemented 12 state-of-the-art methods published in top-tier venues such as SIGMOD and VLDB from 2010 to 2018 for obtaining regret minimization sets and give an experimental comparison under various parameter settings on both synthetic and real datasets. Our evaluation shows that the optimal choice of methods for regret minimization depends on the application demands. This paper provides an empirical guideline for making such a decision.

中文翻译：

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后悔最小化查询和变体的实验调查：在top-k查询和天际线查询之间架起最佳世界

当面对包含数百万个元组的数据库时，用户可能只对（通常很多）较小的代表性子集感兴趣。最近，针对该目的提出了一种称为后悔最小化查询的查询，以为用户创建这种子集。具体地说，此查询找到了一个元组集，该元组集最大程度地减少了用户的后悔（通过选择的集合中用户最喜欢的元组与他/她在整个数据库中最喜欢的元组之间的距离来衡量）。遗憾最小化查询被证明是在缩小两者之间的世界上最好现有的知名查询，顶非常有用ķ查询和天际线查询：像顶ķ查询，此新查询中返回的元组总数是可控制的，并且与天际线查询一样，此新查询不需要用户指定任何首选项功能。因此，它引起了数据库社区研究人员的广泛关注。为了使后悔最小化，提出了各种方法。然而，尽管进行了大量的研究，但是现有方法之间没有系统的比较。本文通过广泛地回顾和比较减少后悔的最新方法，对这个有趣且不断发展的研究主题进行了调查。此外，我们研究了后悔最小化查询的不同变体，这些变体近年来引起了相当大的关注，并提出了一些有趣的问题，这些问题尚未在文献中得到解决。我们从2010年到2018年在SIGMOD和VLDB等顶级场所实施了12种最新方法，以获取遗憾最小化集，并在合成和真实数据集的各种参数设置下进行实验比较。我们的评估表明，最小化后悔方法的最佳选择取决于应用需求。本文为做出这样的决定提供了经验指导。