On one hand, the examination of outliers also
creates costs. On the other hand, the costs of non-detected data
errors can be much higher than costs of suspected problems that
turn out to be false alarms. Therefore, we extend the approach
by adding cost considerations. Third, we present result visualizations
that can support decision makers in choosing parameters