Flexible skyline: overview and applicability

TL;DR

This paper reviews the flexible skyline approach, a recent technique that combines the advantages of ranking and skyline queries to better extract interesting data from large datasets.

Contribution

It provides an overview of flexible skyline methods and discusses their applicability as a versatile solution for data retrieval tasks.

Findings

Flexible skyline offers a controllable output size.

It balances ranking speed with skyline's comprehensiveness.

The approach adapts to various data analysis needs.

Abstract

Ranking (or top-k) and skyline queries are the most popular approaches used to extract interesting data from large datasets. The first one is based on a scoring function to evaluate and rank tuples. Its computation is fast, but it is sensitive to the choice of the evaluating function. Skyline queries are based on the idea of dominance and the result is the set of all non-dominated tuples. This is a very interesting approach, but it can't allow to control the cardinality of the output. Recent researches discovered more techniques to compensate for these drawbacks. In particular, this paper will focus on the flexible skyline approach.