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Cardinality constraints are among the most popular classes of constraints in database models.
For the advancement of XML applications it is therefore a major challenge to discover natural classes of constraints that can be utilized effectively by XML data engineers.
It is not only possible to select and solve single classes of constraints, but rather any combination of them with just slight modifications.
That is, our approach is capable of detecting violations of various classes of constraints and suggesting repair solutions for any violated structural and/or conformity constraint.
The literature of multidimensional modeling provides several types and classes of constraints (cf. [2, 9, 11, 12, 19, 20, 27, 28, 36]).
Thus, the analysis of the network at the mesoscopic level provides an appropriate framework for identifying the roles that different classes of constraints (developmental, structural and functional) play in determining the organization of a nervous system.
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We compare tractable classes of constraint satisfaction problems (CSPs).
Many recent studies have identified phase transitions from under- to overconstrained instances in classes of constraint satisfaction search problems, and their relation to search cost.
Well suited to these methods are classes of constraint systems that can be used to efficiently model, design and analyze quasi-uniform (aperiodic) and self-similar, layered material structures.
Our class of constraints is chosen near-optimal as already minor extensions of its expressiveness cause potential intractability.
In the paper we consider bounds on differences (which are an important class of constraints based on linear inequalities) and we analyze the computational complexity of query answering.
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