Rule simplification methods for statistical data editing

What is it about?

Data editing is the process to detect and correct errors in statistical data. Quite often, data editing can be automated. The basis of automated data editing are edit rules; relations between variables which should be fulfilled (e.g. profits is turnover minus cost). In many automated data editing applications a large number of edit rules needs to be dealt with. My paper shows that the permance of automatic data editing can be conderably improved by a constraint simplifcation step.

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Why is it important?

Rule simplification may drastically improve automatic data editing's performace. It can also be helpful for detecting flaws in the rule formulation.

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