Abstract
This paper introduces the concept of the FLINQ library to perform linguistic fuzzy queries. This library is based on a functional programming paradigm and implemented in C# language as an extension of the LINQ library. Its main goal is to provide an easy-to-use and effective mechanism for performing linguistic queries in which fuzzy sets describe linguistic terms.
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References
Zadeh, L.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)
Chen, C., Shen, Q.: Rough-fuzzy rule interpolation for data-driven decision making. In: Jansen, T., Jensen, R., Mac Parthalain, N., Lin, C.M. (eds.) Advances in Computational Intelligence Systems: Contributions Presented at the 20th UK Workshop on Computational Intelligence, 8–10 September 2021, Aberystwyth, Wales, UK 20, pp. 27–37. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-87094-2_3
Grycuk, R.: Fast solar image retrieval and classification by fuzzy rules. In: 2022 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), pp. 1–7 (2022)
Yera, R., Alzahrani, A.A., Martínez, L.: A fuzzy content-based group recommender system with dynamic selection of the aggregation functions. Int. J. Approximate Reasoning 150, 273–296 (2022)
Chen, B.: Application of intelligent fuzzy decision tree algorithm in English translation education. In: Jan, M.A., Khan, F. (eds.) Application of Big Data, Blockchain, and Internet of Things for Education Informatization, Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 465, pp. 310–315. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-23950-2_33
Singh, A.K., Singh, R., Kumar, G., Soni, S.: Power system fault diagnosis using fuzzy decision tree. In: 2022 IEEE Students Conference on Engineering and Systems (SCES), Prayagraj, India, pp. 1–5 (2022)
Jha, S., Mehta, A.K.: An evolutionary algorithm based feature selection and fuzzy rule reduction technique for the prediction of skin cancer. Concurr. Comput. Pract. Exp. 34(5), e6694 (2022)
Wróbel, M., Starczewski, J.T., Fijałkowska, J., Siwocha, A., Napoli, Ch.: Handwritten word recognition using fuzzy matching degrees. J. Artif. Intell. Soft Comput. Res. 11(3), 229–242 (2021)
Niewiadomski, A., Kacprowicz, M.: Type-2 fuzzy logic systems in applications: managing data in selective catalytic reduction for air pollution prevention. J. Artif. Intell. Soft Comput. Res. 11(2), 85–97 (2021)
Laktionov, I., Vovna, O., Kabanets, M.: Information technology for comprehensive monitoring and control of the microclimate in industrial greenhouses based on fuzzy logic. J. Artif. Intell. Soft Comput. Res. 13(1), 19–35 (2023)
Ferranti, L., Boutellier, J.: FuzzyLogic.jl: a flexible library for efficient and productive fuzzy inference. In: 2023 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1–5 (2023)
Fosci, P., Psaila, G.: A unified view of multi-grade fuzzy-set models in J-CO-QL+. Neurocomputing 565, 126968 (2024)
Medina, J.M., Blanco, I.J., Pons, O.: A fuzzy database engine for mongoDB. Int. J. Intell. Syst. 37(9), 5691–5724 (2022)
Dyczkowski, K., et al.: Python library for interval-valued fuzzy inference. SoftwareX 26, 101730 (2024)
Yang, Q., et al.: Efficient processing of nested fuzzy SQL queries in a fuzzy database. IEEE Trans. Knowl. Data Eng. 13(6), 884–901 (2001)
Ma, Z.M., Yan, L.: Generalization of strategies for fuzzy query translation in classical relational databases. Inf. Softw. Technol. 49(2), 172–180 (2007)
Cingolani, P., Alcala-Fdez, J.: jFuzzyLogic: a robust and flexible Fuzzy-Logic inference system language implementation. In: 2012 IEEE International Conference on Fuzzy Systems, pp. 1–8 (2012)
Spolaor, S., et al.: Simpful: a user-friendly Python library for fuzzy logic. Int. J. Comput. Intell. Syst. 13(1), 1687–1698 (2020)
Julián-Iranzo, P., Moreno, G., Riaza, J.A.: The fuzzy logic programming language FASILL: design and implementation. Int. J. Approximate Reasoning 125, 139–168 (2020)
Julián-Iranzo, P., Sáenz-Pérez, F.: Bousi\(\sim \)Prolog: design and implementation of a proximity-based fuzzy logic programming language. Expert Syst. Appl. 213, 118858 (2023)
Meijer, E., Beckman, B., Bierman, G.: LINQ: reconciling object, relations and XML in the .NET framework. In: Proceedings of the 2006 ACM SIGMOD International Conference on Management of Data, p. 706 (2006)
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Bartczuk, Ł. (2025). Functional Based Fuzzy Logic Query Library. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2024. Lecture Notes in Computer Science(), vol 15166. Springer, Cham. https://doi.org/10.1007/978-3-031-81596-6_3
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