| Title of paper:
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Big data, intuitionistic fuzzy sets and MapReduce operators
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| Author(s):
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| Panagiotis Chountas
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| University of Westminster Faculty of Science and Technology, Dept. of Computer Science, London W1W 6UW, UK
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| p.i.chountas@westminster.ac.uk
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| Krassimir Atanassov
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| Bulgarian Academy of Sciences, Institute of Biophysics and Biomedical Engineering, Dept. of Bioinformatics and Mathematical Modelling, Sofia, Bulgaria
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| krat@bas.bg
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| Vassia Atanassova
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| Bulgarian Academy of Sciences, Institute of Biophysics and Biomedical Engineering, Dept. of Bioinformatics and Mathematical Modelling, Sofia, Bulgaria
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| vassia.atanassova@gmail.com
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| Evdokia Sotirova
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| Prof. Dr. Asen Zlatarov University, Intelligent Systems Laboratory, Burgas, Bulgaria
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| esotirova@btu.bg
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| Sotir Sotirov
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| Prof. Dr. Asen Zlatarov University, Intelligent Systems Laboratory, Burgas, Bulgaria
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| ssotirov@btu.bg
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| Olympia Roeva
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| Bulgarian Academy of Sciences, Institute of Biophysics and Biomedical Engineering, Dept. of Bioinformatics and Mathematical Modelling, Sofia, Bulgaria
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| olympia@biomed.bas.bg
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| Published in:
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Notes on Intuitionistic Fuzzy Sets, Volume 24 (2018), Number 2, pages 129-135
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| DOI:
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https://doi.org/10.7546/nifs.2018.24.2.129-135
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| Download:
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 PDF (174 Kb Kb, File info)
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| Abstract:
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One of the main restrictions of the relational data model is the lack of support for flexible, imprecise and vague information in data encoding and retrieval. Fuzzy set theory and more specifically intuitionistic fuzzy sets provides an effective solution to model the data imprecision in relational databases. Several works in the last 30 years have used fuzzy set theory to extend relational data model to permit representation and retrieval of imprecise data. However, to the best of our knowledge, such approaches have not been designed to scale-up to very large datasets. In this paper, we develop MapReduce algorithms to enhance the standard relational operations with IFS predicates.
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| Keywords:
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Intuitionistic fuzzy sets, Big data, MapReduce
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| AMS Classification:
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03E72
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| References:
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