A Modular Method for Estimating Null Values in Relational Database Systems

There are many methods trying to do the relational database estimation with a highly estimated accuracy rate by constructing a great diversity of methods. This paper presents a modular method for estimating null values in relational database systems, and which is 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 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, the proposed method does not only integrate advantages of fuzzy system and simple linear regression model, but also introduce a new criterion, differential rate, to enhance the estimated accuracy of the approximation with a highly accuracy of this achievement.