Giving the Right Answer: a Brief Overview on How to Extend Ranking and Skyline Queries

TL;DR

This paper reviews and compares three advanced methods—Flexible Skylines, Skyline Ranking, and Regret Minimization—that aim to improve result retrieval in databases by addressing limitations of traditional Top-K and Skyline queries.

Contribution

It provides a comprehensive analysis and comparison of three research approaches to extend ranking and skyline queries, guiding users in selecting suitable methods for their needs.

Findings

Flexible/Restricted Skylines mitigate user preference issues.

Skyline Ordering/Ranking enhances result prioritization.

Regret Minimization balances result quantity and relevance.

Abstract

To retrieve the best results in a database we use Top-K queries and Skyline queries but some problems arise. The formers rely too much on user preferences, which are difficult to quantify and may skew the fetching of the data, while the latters tend to output too much data. In this paper, we explore three different branches of research that seek to overcome such limitations: Flexible/Restricted Skylines, Skyline Ordering/Ranking, and Regret Minimization. We analyze how they work and we make comparisons among them to guide the reader to choose the approach that best fits their use cases.