  To retrieve the best results in a database we use Top-K queries and Skyline
queries but some problems arise. The formers rely too much on user preferences,
which are difficult to quantify and may skew the fetching of the data, while
the latters tend to output too much data. In this paper, we explore three
different branches of research that seek to overcome such limitations:
Flexible/Restricted Skylines, Skyline Ordering/Ranking, and Regret
Minimization. We analyze how they work and we make comparisons among them to
guide the reader to choose the approach that best fits their use cases.
