A bird's eye view on Multi-Objective Optimization techniques in Relational Databases

TL;DR

This paper reviews various multi-objective optimization techniques in relational databases, focusing on Top-k, Skyline, and introducing Flexible-skyline as an improved method with comparative analysis.

Contribution

It provides a comprehensive overview of existing methods and introduces Flexible-skyline, enhancing multi-objective query optimization in relational databases.

Findings

Flexible-skyline mitigates limitations of previous techniques

Comparative analysis of Top-k and Skyline queries

Detailed overview of algorithms for each method

Abstract

Multi-objective optimization is the problem of optimizing simultaneously multiple objective functions and several techniques exist to deal with this problem. This paper aims to present the main methods that can be used to solve this issue in the context of relational databases. In particular, this work examines Top-k query to get the k best result from a dataset and Skyline query that provides a more general overview of the best results. We also discuss Flexible-skyline, a new method designed to improve upon the previous techniques, mitigating their shortcomings. For each method, we describe the main characteristics and present an overview of the algorithms implementing such thecniques, while comparing advantages and disadvantages.