An outranking-based approach for skyline refinement

Haddache, Mohamed; Belkasmi, Djamal; Hadjali, Allel; Asayesh, Hamid

doi:10.1109/is.2016.7737442

Cited by 6 publications

(8 citation statements)

References 22 publications

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“…Approaches based on ranking functions (See Papadias et al (2003), Chan et al (2006b), Koltun and Papadimitriou (2005), Chan et al (2006a), Lee et al (2007), Balke et al (2007), Sarma et al (2011), Haddache et al (2016), Loyer et al (2013)): the idea of these approaches is to combine the skyline operator with the top−k retrieval. For each tuple in the skyline (computed in the whole universe), one joins a related score, which is computed by the means of a ranking function f such as entropy function or sum of coordinates of a point on all dimensions.…”

Section: Related Workmentioning

confidence: 99%

“…For the comparison purpose, we recall first the principle of two approaches to skyline refinement: the naive Papadias et al (2003) and outranking Haddache et al (2016) approaches. Then, we discuss the computation aspect and the complexity issue.…”

Section: Refining Skyline Computation Frameworkmentioning

confidence: 99%

“…It is worth noticing that when computing the skyline, two scenarios often occur: either (1) a huge number of skyline objects are returned which are less informative for the end users or (2) a small number of skyline objects are retrieved which could be insufficient to serve the user needs. In order to solve these two problems, several approaches have been developed, on the one hand, to relax the skyline and thus increasing its size Goncalves and Tineo (2007), Belkasmi et al (2019) in the case of the problem (2) and, on the other hand, to refine the skyline and thus reducing its size Hadjali et al (2011), Papadias et al (2003), Chan et al (2006b), Koltun and Papadimitriou (2005), Chan et al (2006a), Balke et al (2007, 2006), Lee et al (2007), Sarma et al (2011), Zhang and Alhajj (2010), Benouaret et al (2011), Haddache et al (2016) in the context of the problem (1). In this paper, we address the problem of huge skyline.…”

Section: Introductionmentioning

confidence: 99%

“…The methods proposed in Haddache et al (2019a, b) are of an iterative nature and allow returning a flat subset containing k skyline objects, while the solution discussed here establishes a rank-order on the skyline objects and retrieves the top−k (i.e., a discriminated subset).We develop and implement an optimized algorithm to efficiently compute the refined skyline Sref. Furthermore, we provide a complete analysis of the theoretical complexity of all the algorithms studied for the comparison purpose.We conduct a set of thorough experiments to study, analyze and compare the relevance and effectiveness of the proposed method with the approaches proposed in Haddache et al (2019a, b), the naive Papadias et al (2003) and outranking Haddache et al (2016) methods.…”

Section: Introductionmentioning

confidence: 99%

“…We conduct a set of thorough experiments to study, analyze and compare the relevance and effectiveness of the proposed method with the approaches proposed in Haddache et al (2019a, b), the naive Papadias et al (2003) and outranking Haddache et al (2016) methods.…”

Section: Introductionmentioning

confidence: 99%

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Skyline refinement exploiting fuzzy formal concept analysis

Haddache

Hadjali

Asayesh

2021

IJICC

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PurposeThe study of the skyline queries has received considerable attention from several database researchers since the end of 2000's. Skyline queries are an appropriate tool that can help users to make intelligent decisions in the presence of multidimensional data when different, and often contradictory criteria are to be taken into account. Based on the concept of Pareto dominance, the skyline process extracts the most interesting (not dominated in the sense of Pareto) objects from a set of data. Skyline computation methods often lead to a set with a large size which is less informative for the end users and not easy to be exploited. The purpose of this paper is to tackle this problem, known as the large size skyline problem, and propose a solution to deal with it by applying an appropriate refining process.Design/methodology/approachThe problem of the skyline refinement is formalized in the fuzzy formal concept analysis setting. Then, an ideal fuzzy formal concept is computed in the sense of some particular defined criteria. By leveraging the elements of this ideal concept, one can reduce the size of the computed Skyline.FindingsAn appropriate and rational solution is discussed for the problem of interest. Then, a tool, named SkyRef, is developed. Rich experiments are done using this tool on both synthetic and real datasets.Research limitations/implicationsThe authors have conducted experiments on synthetic and some real datasets to show the effectiveness of the proposed approaches. However, thorough experiments on large-scale real datasets are highly desirable to show the behavior of the tool with respect to the performance and time execution criteria.Practical implicationsThe tool developed SkyRef can have many domains applications that require decision-making, personalized recommendation and where the size of skyline has to be reduced. In particular, SkyRef can be used in several real-world applications such as economic, security, medicine and services.Social implicationsThis work can be expected in all domains that require decision-making like hotel finder, restaurant recommender, recruitment of candidates, etc.Originality/valueThis study mixes two research fields artificial intelligence (i.e. formal concept analysis) and databases (i.e. skyline queries). The key elements of the solution proposed for the skyline refinement problem are borrowed from the fuzzy formal concept analysis which makes it clearer and rational, semantically speaking. On the other hand, this study opens the door for using the formal concept analysis and its extensions in solving other issues related to skyline queries, such as relaxation.

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