Using Intelligent Methods to Solve Null Values Problem in Databases

Abstract

Null is a special marker used to indicate that a data value does not exist in the database. Null has been a source of debate because of its special requirements for its use in Structured Query Language (SQL), and the special handling required by aggregate functions and SQL grouping operators. This paper presents a hybrid approach for solving null values problem, it hybridize rough set theory with ID3 (Iterative Dichotomiser 3) decision tree induction algorithm. The proposed approach is a supervised learning model. Large set of complete data called learning data is used to find the decision rule sets that then have been used in solving the incomplete data. In case of one and two null values occurrence with a row, the system had the ability of estimating approximately 99% of the null values and absolutely 83% of them in case of one null within a record and 63% of the null values in case of two null values. While when three null values were occurred within a row the approximately estimated values were 97% and the absolutely estimated null values were 60%.