An outline of multi objective optimization in databases with focus on flexible skyline queries

TL;DR

This paper reviews multi-objective optimization techniques in databases, emphasizing the flexible skyline paradigm as an effective approach to handle conflicting criteria in large data sets.

Contribution

It provides a comprehensive overview of multi-criteria optimization methods in databases, highlighting the advantages of the flexible skyline approach over traditional methods.

Findings

Flexible skyline overcomes critical issues of other methods

Thorough comparison of ranking, skyline, and advanced techniques

Clarifies the role of multi-objective optimization in large-scale database queries

Abstract

The problem of optimizing across different, conceivably conflicting, criteria is called multi-objective optimization and it is widely spread across many fields. This is a recurring problem in database queries when there is the need of obtaining the best objects from a very large data set. In this article, I included a complete review of the main approaches typically used to achieve multi-criteria optimization. Starting from ranking queries and skylines and then proceeding to more advanced methods, this paper aims to define a clear outline of multi-objective optimization in databases. In particular, the flexible skyline paradigm is considered and thoroughly discussed as it overcomes many of the critical issues that arise with other methods.