Abstract

Neutrosophic database is an extension of classical database model with the framework of neutrosophic data and similarity measure formula to process uncertain data. Designing good neutrosophic database, normalization plays an important role in which a relation is decomposed into smaller relations with respect to neutrosophic functional dependencies to obtain desired neutrosophic normal forms. The objective of this paper is to investigate lossless join and dependency preservation properties of normalization in neutrosophic database. Two algorithms are designed to maintain lossless join and dependency preservation of decomposition of a relation in neutrosophic database. The algorithms have been tested and validated with examples.

References

• Codd E. A Relational Model for Large Shared Data Banks. Comm. of ACM 13. 1970; 377–387.
• Zadeh A. L. Fuzzy Sets. Information and Controls 8. 1965; 338–353.
• Gau W. L. and Buehrer D. J., 1993, “Vague Sets”, IEEE Trans. Syst. Man, Cybernetics, Vol. 23, No. 2, pp. 610-614.
• Mishra J, Ghosh S. A Multivalued Integrity Constraint in Fuzzy Relational Database. International Journal of Computational Cognition 9. 2011; 72–78.
• Chen G. , Kerre E. E., Vandenbulcke, J. Normalization Based on ffd in a Fuzzy Relational Data Model. Information Systems 21.1996; 299–310.
• Raju N.V.S.V.K., Majumdar K.A. Fuzzy functional dependencies and lossless join decomposition of fuzzy relational database system. ACM Transactions on Database Systems 13. 1988: 129–166.
• Yazici A, Sozat, I. M. The Integrity Constraints for Similarity-Based Fuzzy Relational Databases. International Journal of Intelligent Systems 13. 1998; 641–659.
• Zhao A, Ma L, Yan M. Z. A Vague Relational Model and Algebra. Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD2007). 2007; 81-85.
• Zhao F, Ma M. Z. Vague Query Based on Vague Relational Model. Springer- Verlag Berlin Heidelberg, AISC 61. 2009; 229-238.
• Mishra J, Ghosh S. A Vague Multivalued Data Dependency. International Journal of Fuzzy Information and Engineering, Springer Vol. 5. 2013; 459-473.
• Smarandache F. First International Conference on Neutrosophy, Neutrosophic Probability, Set, and Logic. University of New Mexico. 2001; 1(3).
• De S, Mishra J. A New Approach of Functional Dependency in a Neutrosophic Relational Database Model. Asian Journal of Computer Science and Technology.2019; 8(2):44-48.
• De S, Mishra J. Query Processing of Inconsistent Data using Neutrosophic Set. IEEE International Conference on Computing Communication and Automation.2016; 10(6):120-124.
• De S, Mishra J. Compare Different Similarity Measure Formula Based Imprecise Query on Neutrosophic Data. International Conference on Advanced Computing and Intelligent Engineering, 2016; 5(12): 330-334.
• De S, Mishra J. Inconsistent Data Processing Using Vague Set and Neutrosophic Set for Justifying Better Outcome. IEEE International Conference on Inventive Communication and Computational Technologies,2017;.7(4):26-30.
• De S, Mishra J. Processing of Inconsistent Neutrosophic Data and Selecting Primary Key from the Relation. International Conference on Inventive Computing and Informatics. 2017;.6(7): 245-250.