A Survey of Skyline Query Processing

TL;DR

This survey reviews the latest techniques and variations in skyline query processing, a method for identifying optimal options in large datasets, highlighting their applications and algorithmic developments.

Contribution

It provides a comprehensive taxonomy and comparison of state-of-the-art skyline query algorithms and their application-specific adaptations.

Findings

Various skyline query algorithms and their variations are analyzed.

Application-specific approaches improve efficiency in different scenarios.

A taxonomy categorizes key aspects of skyline query processing methods.

Abstract

Living in the Information Age allows almost everyone have access to a large amount of information and options to choose from in order to fulfill their needs. In many cases, the amount of information available and the rate of change may hide the optimal and truly desired solution. This reveals the need of a mechanism that will highlight the best options to choose among every possible scenario. Based on this the skyline query was proposed which is a decision support mechanism, that retrieves the valuefor- money options of a dataset by identifying the objects that present the optimal combination of the characteristics of the dataset. This paper surveys the state-of-the-art techniques for skyline query processing, the numerous variations of the initial algorithm that were proposed to solve similar problems and the application-specific approaches that were developed to provide a solution efficiently in each case. Aditionally in each section a taxonomy is outlined along with the key aspects of each algorithm and its relation to previous studies.