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

TL;DR

This paper surveys advanced Skyline and Ranking query techniques, focusing on their extensions to handle output size, query formulation, and personalization, enhancing user preference management in data retrieval.

Contribution

It provides a comprehensive comparison of state-of-the-art preference-based query frameworks, highlighting their capabilities and limitations in managing user preferences.

Findings

Extended tools address output explosion and query difficulty

Comparison of personalization capabilities of different techniques

Analysis of flexibility in user preference expression

Abstract

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.