  To make intelligent decisions over complex data by discovering a set of
interesting options is something that has become very important for users of
modern applications. Consequently, researchers are studying new techniques to
overcome limitations of traditional ways of querying data from databases as
top-k queries and skyline queries. Over the past few years new methods have
been developed as Flexible Skylines, Regret Minimization and Skyline
ordering/ranking. The aim of this survey is to describe these techniques and
some their possible variants comparing them and explaining how they improve
traditional methods.
