  Industrial and scientific applications handle large volumes of data that
render manual validation by humans infeasible. Therefore, we require automated
data validation approaches that are able to consider the prior knowledge of
domain experts to produce dependable, trustworthy assessments of data quality.
Prior knowledge is often available as rules that describe interactions of
inputs with regard to the target e.g. the target must be monotonically
decreasing and convex over increasing input values. Domain experts are able to
validate multiple such interactions at a glance. However, existing rule-based
data validation approaches are unable to consider these constraints. In this
work, we compare different shape-constrained regression algorithms for the
purpose of data validation based on their classification accuracy and runtime
performance.
