A flexible solution to embrace Ranking and Skyline queries approaches

TL;DR

This paper proposes a flexible approach that integrates Ranking and Skyline queries to overcome their individual limitations in multi-objective optimization tasks.

Contribution

It introduces a novel method that combines Ranking and Skyline queries, enhancing their applicability across various fields of interest.

Findings

Improved ability to handle multi-objective optimization problems.

Enhanced dataset exploration with combined query approaches.

Greater flexibility in user preference modeling.

Abstract

The multi-objective optimization problem has always been the main objective of the principal traditional approaches, such as Ranking queries and Skyline queries. The conventional idea was to either use one or the other, trying to exploit both ranking queries advantages when it comes to taking into account user preferences, and skyline queries points of strength when the main objective was to obtain interesting results from a dataset in a simple, yet effective fashion, both of them showing limitations when entering specific fields of interest.