  The problem of selecting the most representative tuples from a dataset has
led to the development of powerful tools, among which Skyline and Ranking (or
Top-k) queries stand out for their ability to support the optimization of
multiple criteria in the query process. This paper surveys the remarkable
efforts made towards the extension of the aforementioned tools to overcome
their limitations, respectively the explosion of the output result and the
difficulty of query formulation. Moreover, we explore the application of these
state-of-the-art techniques as preference-based query frameworks, proposing a
comparison of their query personalization capabilities, the ability to control
the output size and their flexibility with respect to the user input
preferences.
