Multi-Objective Optimization, different approach to query a database

TL;DR

This survey compares two approaches to querying complex datasets, analyzing algorithms for Top-K and skyline queries, and evaluates their advantages and disadvantages based on various criteria.

Contribution

It provides a comprehensive analysis of different query algorithms and approaches for complex datasets, including Top-K and skyline queries, with insights into their core ideas and trade-offs.

Findings

TA and NRA algorithms effectively handle Top-K queries.

Basic Block Nested Loops is suitable for skyline queries.

Prioritized and Flexible skyline approaches offer customizable querying options.

Abstract

The datasets available nowadays are very rich and complex, but how do we reach the information we are looking for? In this survey, two different approaches to query a dataset are analyzed and algorithms for each type are explained. Specifically, the TA and NRA have been analyzed for the Top-K query and the Basic Block Nested Loops has been examined for the skyline query. Moreover, it's explained the core idea behind the Prioritized and Flexible skyline. In the end, the pros and cons of each type of analyzed query have been evaluated based on different criteria.