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Intelligent Computing - Proceedings of the Computing Conference

CompCom 2019: Intelligent Computing pp 659–677Cite as

Fuzzy Region Connection Calculus and Its Application in Fuzzy Spatial Skyline Queries

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 997)

Abstract

Spatial data plays a pivotal role in decision-making applications in a way that nowadays we witness its ever-growing and unprecedented use in both analyses and decision-making. In between, spatial relations constitute a significant form of human understanding of spatial formation. Regarding this, the relationships between spatial objects, particularly topological relations, have recently received considerable attention. However, real-world spatial regions such as lakes or forests have no exact boundaries and are considered fuzzy. Therefore, defining fuzzy relationships between them would yield better results. So far, several types of research have addressed this issue, and remarkable advances have been achieved. In this paper, we propose a novel method to model the “Part” relation of fuzzy region connection calculus (RCC) relations. Furthermore, a method based on fuzzy RCC relations for fuzzification of an important group of spatial queries, namely the skyline operator, is proposed in spatial databases that can be used in decision support, data visualization, and spatial databases applications. The proposed algorithms have been implemented and evaluated on real-world spatial datasets. The results of the carried out evaluation demonstrate more flexibility in comparison with other well-established existing methods, as well as the appropriateness of the speed and quality of the results.

Keywords

  • Fuzzy spatial reasoning
  • Spatial skyline query
  • Spatial data analysis
  • Fuzzy spatial skyline query

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Isfahan University of Technology, 84156-83111, Isfahan, Iran

    Somayeh Davari & Nasser Ghadiri

  2. Department of Computer Science, National Research University Higher School of Economics, Moscow, 125319, Russia

    Somayeh Davari

Authors
  1. Somayeh Davari
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  2. Nasser Ghadiri
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Corresponding author

Correspondence to Somayeh Davari .

Editor information

Editors and Affiliations

  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Prof. Dr. Kohei Arai

  2. The Science and Information SAI Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information SAI Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

Appendices

Appendix A. The Function P to Calculate the Fuzzy Relationship Part

figure a

Appendix B. The Meaning of Obrivations

The meaning of each obrivations in the paper are as follow:

TGrid::

skyline runtime by Grid method

TFuzzy::

skyline runtime by fuzzy method with or without applying Grid method

TSum: :

Total runtime

NGrid: :

the number of skyline members after applying Grid method

NFuzzy::

the number of skyline members with fuzzy method with or without applying Grid method

NSum::

Total skyline members after applying the method or methods

M::

alpha number in the fuzzy equation.

N::

beta number in the fuzzy equation.

Bet.::

the minimum acceptable value for a new tuple connection to the previous one to be added to the skyline in fuzzy method

Grid::

The number of divisions of a range of attributes, such as distance to the airport from zero to maximum in Grid method

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Davari, S., Ghadiri, N. (2019). Fuzzy Region Connection Calculus and Its Application in Fuzzy Spatial Skyline Queries. In: Arai, K., Bhatia, R., Kapoor, S. (eds) Intelligent Computing. CompCom 2019. Advances in Intelligent Systems and Computing, vol 997. Springer, Cham. https://doi.org/10.1007/978-3-030-22871-2_45

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