Paper Info
Title
On fuzzy approaches for enlarging skyline query results.
Abstract
In the last decade, skyline queries have gained much attention and are proved to be valuable for multi-criteria decisions. Based on the concept of Pareto dominance, they return the non-dominated points, called the skyline points. In practice, it may happen that the skyline only contains a small number of points which could be insufficient for the user needs. In this paper, we discuss two fuzzy-set-based approaches to enriching the small skyline with particular points that could serve the decision makers’ needs. The basic idea consists in identifying the most interesting points among the non-skyline ones. On the one hand, we introduce a novel fuzzy dominance relationship which makes more demanding the dominance between the points of interest. So, much points would be considered as incomparable and then as elements of the new relaxed skyline. On the other hand, we leverage an appropriate fuzzy closeness relation to retrieve non skyline points that are fuzzily close to some skyline points. Furthermore, we develop efficient algorithms to compute the relaxed variants of skyline. Extensive experiments are conducted to demonstrate the effectiveness of our approaches and analyze the performance of the proposed algorithms. A comparative study between the approaches presented is made as well.
Year
DOI
Venue
2019
10.1016/j.asoc.2018.10.013
Applied Soft Computing
Keywords
Field
DocType
Databases,Skyline queries,Pareto dominance,Skyline relaxation,Fuzzy relations
Skyline,Small number,Closeness,Fuzzy logic,Theoretical computer science,Artificial intelligence,Point of interest,Machine learning,Pareto principle,Mathematics
Journal
Volume
ISSN
Citations 
74
1568-4946
0
PageRank 
References 
Authors
0.34
7
3
Name
Order
Citations
PageRank
Djamal Belkasmi101.01
Allel Hadjali239149.62
Hamid Azzoune313.07