A Dynamic Modular Method for Estimating Null Values in Relational Database Systems

Abstract

With the development of the fuzzy system, a lot of sophisticated methods based on the fuzzy system try to do the relational database estimation with a highly accuracy of the approximation by constructing a great diversity of mathematical models. In order to achieve a high-reliability performance with less complication as far as possible, this paper presents a modular method for estimating null values in the relational database system, and which is constructed based on a simple fuzzy learning algorithm. However, there exists a conflict between the degree of the interpretability and the accuracy of the approximation in a general fuzzy system. Thus, how to properly make the best compromise between the accuracy of the approximation and the degree of the interpretability in the entire system is a significant study of the subject. Due to achieve the best compromise practically, the proposed method does not only integrate advantages of fuzzy system and the method of least squares, but also introduce a new criterion, differential rate, to enhance the accuracy of the approximation with a highly accuracy of this achievement.