Automated quality assurance of continuous data

Most real-world databases contain some amount of inaccurate data. Reliability of critical attributes can be evaluated from the values of other attributes in the same data table. This paper presents a new fuzzy-based measure of data reliability in continuous attributes. We partition the relational schema of a database into a subset of input (predicting) and a subset of target (dependent) attributes. A data mining model, called information-theoretic connectionist network, is constructed for predicting the values of a continuous target attribute. The network calculates the degree of reliability of the actual target values in each record by using their distance from the predicted values. The approach is demonstrated on the voting data from the 2000 Presidential Elections in the us.