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
Author affiliations


  • 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
  • Nguyen Truong Thang Institute of Information Technology, Vietnam Academy of Science and Technology, 18 Hoang Quoc Viet, Cau Giay, Ha Noi, Viet Nam



Fuzzy oriented object language, fuzzy query processing, Similarity measurement


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


Download data is not yet available.


Date C. J., and Warden A. - Relational database writings (1985–1989), Addison-Wesley Longman Publishing Co., Inc, 1990.

Ilyas I. F., Beskales G., and Soliman M. A. - A survey of top-k query processing techniques in relational database systems, ACM Computing Surveys (CSUR) 40 (4) (2008) 1-58. DOI:

De Tré G., De Caluwe R., and B der Cruyssen. - A generalised object-oriented database model, Recent issues fuzzy databases (2000) 155-182. DOI:

Bertino E. and Martino L. - Object-oriented database management systems: concepts and issues, Computer (Long. Beach. Calif) 24 (4) (1991) 33-47. 2.76261. DOI:

Deng W. - Object-Oriented Database and O/R Mapping Technology, in Big Data Analytics for Cyber-Physical System in Smart City: BDCPS 2020, 28-29 December 2020, Shanghai, China (2021) 800-806. DOI:

Simon J. P. - Scope, players, markets and geography, Digit. Policy, Regul. Gov., Artificial intelligence (2019). DOI:

Expósito Solis A. and others - Implementation of a Telegram chatbox and webplatform for hypertension, 2020.

Liu C., Li X., Li Q., Xue Y., Liu H., and Gao Y. - Robot recognizing humans intention and interacting with humans based on a multi-task model combining ST-GCN-LSTM model and YOLO model, Neurocomputing 430 (2021) 174-184. j.neucom.2020.10.016 DOI:

Gupta M. M. and Yamakawa T. - Fuzzy logic in knowledge-based systems, decision and control, Elsevier Science Inc. (1988). DOI:

Zadeh L. A. - Fuzzy probabilities, in Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems: Selected Papers by Lotfi A Zadeh, World Scientific, 1996, pp. 643-652. DOI:

Durrett R. - Probability: theory and examples, Cambridge university press 49 (2019). DOI:

Cheng C. B., Shih H. S., and Lee E. S. - Possibility Theory and Fuzzy Optimization, in Fuzzy and Multi-Level Decision Making: Soft Computing Approaches, Springer, 2019, pp. 73-88. DOI:

Umano M., Imada T., Hatono I., and Tamura H. - Fuzzy object-oriented databases and implementation of its SQL-type data manipulation language, in 1998 IEEE International Conference on Fuzzy Systems Proceedings, IEEE World Congress on Computational Intelligence (Cat. No. 98CH36228) 2 (1998) 1344-1349. https:// 10.1109/ FUZZY.1998.686314.

Bordogna G., Pasi G., and Lucarella D. - A fuzzy object-oriented data model for managing vague and uncertain information, Int. J. Intell. Syst. 14 (7) (1999) 623-651.<623::AID-INT1>3.0.CO;2-G. DOI:<623::AID-INT1>3.0.CO;2-G

Van Gyseghem N. and De Caluwe R. - Imprecision and uncertainty in the UFO database model, J. Am. Soc. Inf. Sci. 49 (3) (1998) 236–252.<236::AID-ASI5>3.0.CO;2-B. DOI:<236::AID-ASI5>3.0.CO;2-#

Wedashwara W., Mabu S., Obayashi M., and Kuremoto T. - Evolutionary rule based clustering for making fuzzy object oriented database models, in 2015 IIAI 4th International Congress on Advanced Applied Informatics (2015) 517-522. DOI:

Srivastava A., Yadav S., Srivastava N., and Khan Z. - Fuzzy Query, An Impression in Query processing, 2016.

Drissi A., Nait-Bahloul S., Benouaret K., and Benslimane D. - Horizontal fragmentation for fuzzy querying databases, Distrib, Parallel Databases 37 (3) (2019) 441-468. DOI:

Zeng Y., Zhou Y., Zhou X., and Zheng F. - Fuzzy clustering-based skyline query preprocessing algorithm for large-scale flow data analysis, J. Supercomput 76 (2) (2020) 1321-1330. DOI:

Mama R. and Machkour M. - Fuzzy Questions for Relational Systems, in The Proceedings of the Third International Conference on Smart City Applications (2019) 104-114. DOI:

Liefke K. and Werning M. - Evidence for Single-Type Semantics An Alternative To/-Based Dual-Type Semantics, J. Semant 35 (4) (2018) 639-685. jos/ffy009.

Ma Z. M., Zhang W. J., and Ma W. Y. - Assessment of data redundancy in fuzzy relational databases based on semantic inclusion degree, Inf. Process. Lett. 72 (1–2) (1999) 25-29. DOI:

Rahman K., Abdullah S., Ali A., and Amin F. - Interval-valued Pythagorean fuzzy Einstein hybrid weighted averaging aggregation operator and their application to group decision making, Complex & Intell. Syst. 5 (1) (2019) 41-52. 10.1007/s40747-018-0076-x. DOI:

Dwibedy D., Sahoo L., and Dutta S. - A New Approach to Object Based Fuzzy Database Modeling, Int. J. Soft Comput. Eng. 3 (1) (2013) 182-186. DOI:

Singpurwalla N. D. and Booker J. M. - Membership functions and probability measures of fuzzy sets, J. Am. Stat. Assoc. 99 (467) (2004) 867-877. 016214504000001196. DOI:

Nguyen T. T., Van Doan B., Truong C. N., and Tran T. T. T. - Clustering and query optimization in fuzzy object-oriented database, Int. J. Nat. Comput. Res. 8 (1) (2019) 1-17. 10.4018/IJNCR.2019010101. DOI:

Bashon Y., Neagu D., and Ridley M. J. - A new approach for comparing fuzzy objects, in International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (2010) 115-125. DOI:

Ma Z. M. - Object comparison in fuzzy object-oriented databases, In 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems 3 (2009) 672-675. DOI:




How to Cite

Nguyen Tan Thuan, Tran Thi Thuy Trinh, Doan Van Ban, Truong Ngoc Chau, Nguyen Thi Anh Phuong, and Nguyen Truong Thang, “Some new fuzzy query processing methods based on similarity measurement and fuzzy data clustering”, Vietnam J. Sci. Technol., vol. 62, no. 1, pp. 123–139, Feb. 2024.



Electronics - Telecommunication