A survey on flexible/restricted skyline and their applicability

TL;DR

This survey reviews recent developments in flexible and restricted skyline operators, analyzing three new techniques and comparing their features to guide practical dataset analysis.

Contribution

It provides a comprehensive analysis of three new skyline operators and compares their properties for better applicability in data analysis tasks.

Findings

F-Skyline, ORU/ORD, and ε-Skyline are analyzed and compared.

The operators differ in personalization, cardinality control, and generalization.

Guidelines are provided for selecting the appropriate operator for specific tasks.

Abstract

Skyline and Top-k are two of the most important methods to extract information from datasets, but both come with their drawbacks, that's why lately some new technics that try to mix the features of the two have been studied. In this survey three new operators are analysed, F-Skyline, ORU/ORD, and ${\epsilon}$-Skyline. After giving the main ideas behind those and their properties, they are compered on 3 fundamental features such as personalization, cardinality control, and generalization to guide the user to choose the best one for any task.