Comparing modern techniques for querying data starting from top-k and skyline queries

TL;DR

This paper surveys recent advancements in data querying techniques like Flexible Skylines, Regret Minimization, and Skyline ranking, highlighting their improvements over traditional top-k and skyline queries.

Contribution

It provides a comprehensive comparison and explanation of new methods that enhance traditional data querying techniques.

Findings

New methods offer better flexibility and relevance in data retrieval.

These techniques improve upon traditional top-k and skyline queries.

The survey clarifies the advantages and variants of modern querying approaches.

Abstract

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.