  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.
