Some new fuzzy query processing methods based on similarity measurement and fuzzy data clustering

Nguyen Tan Thuan; Tran Thi Thuy Trinh; Doan Van Ban; Truong Ngoc Chau; Nguyen Thi Anh Phuong; Nguyen Truong Thang

doi:10.15625/2525-2518/18222

Author affiliations

Authors

Nguyen Tan Thuan The University of Danang - University of Technology and Education, 48 Cao Thang, Hai Chau, Da Nang, Viet Nam
Tran Thi Thuy Trinh Duy Tan University, 254 Nguyen Van Linh, Thanh Khe, Da Nang, Viet Nam
Doan Van Ban Institute of Information Technology, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Cau Giay, Ha Noi, Viet Nam
Truong Ngoc Chau The University of Danang - University of Science and Technology, 54 Nguyen Luong Bang, Lien Chieu, Da Nang, Viet Nam
Nguyen Thi Anh Phuong Institute of Information Technology, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Cau Giay, Ha Noi, Viet Nam https://orcid.org/0009-0001-9990-2164
Nguyen Truong Thang Institute of Information Technology, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Cau Giay, Ha Noi, Viet Nam

DOI:

https://doi.org/10.15625/2525-2518/18222

Keywords:

Fuzzy oriented object language, fuzzy query processing, Similarity measurement

Abstract

In relational and object-oriented database systems there is always data that is naturally fuzzy or uncertain. However, to deal with complex data types with fuzzy nature, these systems have many limitations. Therefore, in order to represent and manage fuzzy data, it is necessary to have a fuzzy interrogation system to facilitate non-expert users. To solve this challenge, the paper proposes two different approaches to increase the flexibility of the fuzzy interrogation system. Firstly, based on similarity measures and fuzzy logic, we develop three fuzzy query processing algorithms for single-condition and multi-condition cases such as FQSIMSC (Fuzzy Query Sim Single Condition), FQSIMMC (Fuzzy Query Sim Multi-Condition) and FQSEM (Fuzzy Query SEM). Secondly, we combine the fuzzy clustering algorithm EMC (Expectation maximization Coefficient) and the query processing algorithm that is based on fuzzy partitions FQINTERVAL (Fuzzy Query Interval). With this approach, we not only improve query processing cost but also support applications and devices equipped with intelligent interactive function that easily interacts with the fuzzy query system. The results of our theoretical and experimental analysis, it can be seen that both the proposed methods significantly reduce the processing time and memory space for a data set (extracted from UCI) that has a fuzzy and incomplete natural element with the resulting data size being optimal

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