Fuzzy query translation for relational database systems

Chen, Shyi‐Ming; Jong, Woei-Tzy

doi:10.1109/3477.604117

Cited by 88 publications

(4 citation statements)

References 11 publications

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“…In recent years, more and more research on database query is devoted to generalized processing of the fuzzy conditions. In [6], an approach was presented to translate the fuzzy condition into the precise condition by using the α-cut operation of fuzzy number in relational database. In [7], a new knowledge-based approach to fuzzy query translation for a database was proposed to overcome the shortcoming of conventional DBMS (database management system) that didn't support the fuzzy queries over any of databases.…”

Section: Related Workmentioning

confidence: 99%

Conceptual Queries Based on Knowledge for E-Commerce

Sun

Song

et al. 2011

AMR

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In some e-commerce system, users do not know performance of commodities described by numeric values, but they can provide conceptual descriptions, such as “high”, “medium” and “low” on its price. The query based on concepts as inputs is named conceptual query. The basic method of conceptual query is to convert user's fuzzy concept into standard SQL statement based on the domain knowledge. However, if a condition is composed of the high and multilevel concepts, then its conversion of the concepts is very complex. Its efficiency will be poor. A new knowledge-based query method on multilevel generalized data is proposed in this paper. The knowledge of fuzzy concepts needs to be established, and multilevel generalized data is generated, a knowledge-based multilevel query is achieved on generalized data. Experiments on the synthetic data sets show that on a higher level and multiple levels the query efficiency of the method proposed in the paper is better than the basic method. The knowledge-based conceptual query on multilevel generalized data will provide efficient methods for the query about housing products in rural town and can meet the needs of customers with different knowledge, and has higher practical value.

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Section: Related Workmentioning

confidence: 99%

Conceptual Queries Based on Knowledge for E-Commerce

Sun

Song

et al. 2011

AMR

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“…In 1965, Zadeh [34] presented the fuzzy set (FS) theory. Following that, FSs and their applications have drawn the attention of other authors, including Chen and Jong (1997) [8], Manoj et al (1998) [21], and Singh et al (2019 [28] [29]. The non-membership degree is automatically regarded as one-minus membership degree in the FS theory, which only takes an element's membership degree into account.…”

Section: Introductionmentioning

confidence: 99%

Picture Fuzzy Set with Disaster Management & Decision Making by Measures

2024

Communications on Applied Nonlinear Analysis

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Picture fuzzy sets are extension of intuitionistic fuzzy sets. One method to ascertain the relationship between two picture fuzzy sets is to use the correlation coefficient of those sets. In this research, we introduce the refusal membership grade for picture fuzzy sets in the implementation of similarity measure, correlation coefficient and Distance based Similarity Measure in the application of disaster management. We will get the accurate value while considering refusal value in the utilization of picture fuzzy set. It has universal applications in numerous disciplines such pattern recognition, clustering analysis, and disaster management, among others. A picture fuzzy set can be used to convey the character of a decision-making problem, both positively and negatively and comparative study also made with the help of Distance measure, Similarity measure and Correlation coefficient.

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“…If database is not properly organized then whole software will be paralyzed and consumes lot of time to set right the whole system. [1][2] [3]Distributed database is one of the prime technology for the software industry to use their application all over the world. [6] [7] It increase the speed of the application by employing multiple site server accessibility to the user.…”

Section: Introductionmentioning

confidence: 99%

“…The research employed fuzzy logic to remove the impreciseness in the query submitted by the inexperienced users. The proposed method provide flexible and friendly environment [3].…”

mentioning

confidence: 99%

Structured Query Language (SQL) Answering Model for User Queries based on Intuitionistic Fuzzy Logic

Dutta¹

2015

IJCA

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Instuitionistic fuzzy logic is widely accepted method to analyse the imprecise and vague data. There are number of database management systems (DBMS) are available to facilitate the users to store and organize the data for the future purpose. DBMS lacks to understand the user queries in distributed environment. Sql is a popular querying language to fetch data depend upon the user queries. The structure of the sql query is designed for precise queries from the user but it will return error for vague queries. As the business extends from one part of the world to the other language should be a barrier for the non-English speakers. The research is to find the solution for the problems exist in the sql to translate the user queries. The proposed model will answer all kind of user queries and tries solve the vagueness problem using fuzzy logic.

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Fuzzy query translation for relational database systems

Cited by 88 publications

References 11 publications

Conceptual Queries Based on Knowledge for E-Commerce

Conceptual Queries Based on Knowledge for E-Commerce

Picture Fuzzy Set with Disaster Management & Decision Making by Measures

Structured Query Language (SQL) Answering Model for User Queries based on Intuitionistic Fuzzy Logic

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