Authors :
Mamudu, Friday; Matthew Okoronkwo C.
Volume/Issue :
Volume 9 - 2024, Issue 12 - December
Google Scholar :
https://tinyurl.com/2bd32zfd
Scribd :
https://tinyurl.com/bdzeu2p8
DOI :
https://doi.org/10.5281/zenodo.14505859
Abstract :
This paper focuses on the development of Fuzzy
SQL to overcome the limitations of classical database
systems in handling imprecise and uncertain data. The
proposed comprehensive approach enriches the
capabilities of FSQL by efficiently bridging the gap
between fuzzy relational data models and their respective
practical implementations in databases. This approach
introduces new types of fuzzy comparators, fuzzy attribute
types, and fuzzy constant types in FSQL, allowing for the
specification of more accurate and expressive queries. We
introduce adaptive fulfillment thresholds along with fuzzy
set operators in order to allow complex manipulations of
fuzzy data. This paper also discusses the inclusion of
FuzzyEER principles within FSQL in order to allow a
seamless transformation from conceptual modeling to
query language execution. The significant enhancements
include the development of fuzzy functions for data
manipulation, the extension of DDL to support fuzzy data
types and constraints, and the introduction of special fuzzy
time comparators. These extensions significantly increase
the expressiveness of queries, the precision of data
representation, and the handling of uncertain temporal
information. Various performance evaluations have indeed
shown an improvement in retrieval precision and
increased user satisfaction compared to standard SQL,
especially for queries involving fuzzy conditions. The
improvements in FSQL create a solid foundation for the
management of imprecise data within relational database
systems, opening new viewpoints on applications related to
decision support systems and artificial intelligence. This
paper contributes to the developing area of fuzzy database
systems by providing practical methodologies for the
acquisition and retrieval of imprecise information in
today's data-driven environment.
Keywords :
Fuzzy Relational Databases, Fuzzy SQL, Fuzzy Queries, Fuzzy Comparators.
References :
- L. A. Zadeh, "Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems," World Scientific, 2021.
- J. Kacprzyk and S. Zadrożny, "Fuzzy querying for Microsoft Access," Journal of Intelligent Information Systems, vol. 39, no. 2, 2022.
- J. Galindo, "Fuzzy Databases: Modeling, Design and Implementation," IGI Global, 2020.
- A. Meier, et al., "Fuzzy Database Systems," in Fuzzy Methods for Customer Relationship Management and Marketing, Springer, 2023.
- O. Pivert and P. Bosc, "Fuzzy Preference Queries to Relational Databases," Imperial College Press, 2021.
- T. J. Ross, "Fuzzy Logic with Engineering Applications," Wiley, 2023.
- S. Parsons and A. Hunter, "A review of uncertainty handling in information systems," Information Fusion, vol. 67, 2021.
- J. M. Medina and M. A. Vila, "Fuzzy Tools in Databases and Information Systems," in Flexible Query Answering Systems, Springer, 2022.
- R. R. Yager and L. A. Zadeh, "An Introduction to Fuzzy Logic Applications in Intelligent Systems," Springer, 2020.
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- D. Li and Y. Du, "Artificial Intelligence with Uncertainty," CRC Press, 2021.
- R. Kumar and A. Lee, "Lossless Join Decomposition in Fuzzy Relational Databases," Journal of Database Theory and Applications, 2011.
- J. M. Medina, O. Pons, and M. A. Vila, "GEFRED: A Generalized Model of Fuzzy Relational Databases," International Journal of Intelligent Systems, 1994.
- M. A. Vila and A. Urrutia, "Extensions to the GEFRED Model: Incorporating Possibility Distributions," Fuzzy Sets and Systems, 1996.
- O. Pons and J. M. Medina, "Refinements of the GEFRED Model for Enhanced Fuzzy Data Handling," Journal of Data & Knowledge Engineering, 1998.
- S. Chen and T. Zhang, "The FuzzyEER Model: Extending EER with Fuzzy Capabilities," International Conference on Conceptual Modeling, 2005.
- T. Zhang and S. Chen, "Translating Fuzzy Conceptual Models into Relational Schemas," Journal of Database Management, 2007.
- J. Galindo, A. Urrutia, and M. Piattini, "FSQL: Extending SQL with Fuzzy Logic Capabilities," Journal of Fuzzy Systems, 1999.
- J. Perez and R. Smith, "PFSQL: Priority Fuzzy SQL for Enhanced Query Flexibility," Journal of Advanced Database Research, 2015.
- H. Nguyen and D. Tran, "Priority Queries in Fuzzy Relational Databases: A PFSQL Approach," Proceedings of the Computational Intelligence Conference, 2017.
This paper focuses on the development of Fuzzy
SQL to overcome the limitations of classical database
systems in handling imprecise and uncertain data. The
proposed comprehensive approach enriches the
capabilities of FSQL by efficiently bridging the gap
between fuzzy relational data models and their respective
practical implementations in databases. This approach
introduces new types of fuzzy comparators, fuzzy attribute
types, and fuzzy constant types in FSQL, allowing for the
specification of more accurate and expressive queries. We
introduce adaptive fulfillment thresholds along with fuzzy
set operators in order to allow complex manipulations of
fuzzy data. This paper also discusses the inclusion of
FuzzyEER principles within FSQL in order to allow a
seamless transformation from conceptual modeling to
query language execution. The significant enhancements
include the development of fuzzy functions for data
manipulation, the extension of DDL to support fuzzy data
types and constraints, and the introduction of special fuzzy
time comparators. These extensions significantly increase
the expressiveness of queries, the precision of data
representation, and the handling of uncertain temporal
information. Various performance evaluations have indeed
shown an improvement in retrieval precision and
increased user satisfaction compared to standard SQL,
especially for queries involving fuzzy conditions. The
improvements in FSQL create a solid foundation for the
management of imprecise data within relational database
systems, opening new viewpoints on applications related to
decision support systems and artificial intelligence. This
paper contributes to the developing area of fuzzy database
systems by providing practical methodologies for the
acquisition and retrieval of imprecise information in
today's data-driven environment.
Keywords :
Fuzzy Relational Databases, Fuzzy SQL, Fuzzy Queries, Fuzzy Comparators.