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The problem of selecting the most representative tuples from a dataset hasled to the development of powerful tools, among which Skyline and Ranking (orTop-k) queries stand out for their ability to support the optimization ofmultiple criteria in the query process. This paper surveys the remarkableefforts made towards the extension of the aforementioned tools to overcometheir limitations, respectively the explosion of the output result and thedifficulty of query formulation. Moreover, we explore the application of thesestate-of-the-art techniques as preference-based query frameworks, proposing acomparison of their query personalization capabilities, the ability to controlthe output size and their flexibility with respect to the user inputpreferences.

Skyline Operators and Regret Minimization Techniques for Managing User Preferences in the Query Process