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Making the Skyline Larger: A Fuzzy-Neighborhood-Based Approach

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Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 611))

Abstract

Skyline queries have gained much attention in the last decade and are proved to be valuable for multi-criteria ranking. They are based on the concept of Pareto dominance. In many real-life applications, the skyline returns only a small number of non-dominated objects which could be insufficient for the user. In this paper, we discuss an approach to enriching the small skyline with particular points that could serve the decision makers’ needs. The idea consists in identifying the most interesting non-skyline points belonging to the fuzzy neighborhood of a skyline point and then adding them to the classical skyline. To do so, a particular fuzzy closeness relation is introduced. The relaxed skyline obtained which include the classical skyline, is a discriminated set. Furthermore, an efficient algorithm to compute the relaxed skyline is proposed. Extensive experiments are conducted to demonstrate the effectiveness of our approach and the performance of the proposed algorithm.

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Notes

  1. 1.

    Even the relation \(S_{FE} \subseteq S_{relax} \) holds in this context.

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Author information

Authors and Affiliations

  1. DIF-FS/UMBB, Boumerdes, Algeria

    Djamal Belkasmi

  2. LIAS/ENSMA, Poitiers, France

    Djamal Belkasmi & Allel Hadjali

  3. LRIA/USTHB, Algiers, Algeria

    Hamid Azzoune

Authors
  1. Djamal Belkasmi
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  2. Allel Hadjali
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  3. Hamid Azzoune
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Corresponding author

Correspondence to Allel Hadjali .

Editor information

Editors and Affiliations

  1. INESC-ID,Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Joao Paulo Carvalho

  2. LIP 6, Université Pierre et Marie Curie, Paris, France

    Marie-Jeanne Lesot

  3. School of Industrial Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands

    Uzay Kaymak

  4. IDMEC,Instituto Superior Técnico, Universidade de Lisboa, Lisboa, Portugal

    Susana Vieira

  5. LIP6, Université Pierre et Marie Curie, CNRS, Paris, France

    Bernadette Bouchon-Meunier

  6. Iona College, Machine Intelligence Institute, New Rochelle, New York, USA

    Ronald R. Yager

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Belkasmi, D., Hadjali, A., Azzoune, H. (2016). Making the Skyline Larger: A Fuzzy-Neighborhood-Based Approach. In: Carvalho, J., Lesot, MJ., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-40581-0_28

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  • DOI: https://doi.org/10.1007/978-3-319-40581-0_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-40580-3

  • Online ISBN: 978-3-319-40581-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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