DeepAI
Log In Sign Up

Comparing modern techniques for querying data starting from top-k and skyline queries

06/06/2022
by   Fabio Patella, et al.
0

To make intelligent decisions over complex data by discovering a set of interesting options is something that has become very important for users of modern applications. Consequently, researchers are studying new techniques to overcome limitations of traditional ways of querying data from databases as top-k queries and skyline queries. Over the past few years new methods have been developed as Flexible Skylines, Regret Minimization and Skyline ordering/ranking. The aim of this survey is to describe these techniques and some their possible variants comparing them and explaining how they improve traditional methods.

READ FULL TEXT

page 1

page 2

page 3

page 4

01/22/2022

Comparison of 6 different approaches to outclass Top-k queries and Skyline queries

Topk queries and skyline queries have well explored limitations which re...
03/01/2022

Counting stars: a survey on flexible Skyline Query approaches

Nowadays, as the quantity of data to process began to rise, so did the n...
04/12/2022

Understanding the compromise between skyline and ranking queries

Skyline and Ranking queries have gained great popularity in the recent y...
02/21/2022

Flexible Skylines: Customizing Skyline Queries Catching Desired Preferences

The techniques most extensively used to retrieve interesting data from d...
02/13/2022

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

To retrieve the best results in a database we use Top-K queries and Skyl...
02/13/2022

Comparing Flexible Skylines And Top-k Queries: Which Is the Best Alternative?

The question of how to get the best results out of the data we have is a...