Geometric Constraint Modeling, Data Quality and Software Integration

Geometric constraints are used for a variety of applications, like the representation of consistency conditions or the determination of geometric aspects. For many cases geometric constraints form a system of non-linear equations. For geometric constraint solving there are iterative and symbolic methods. Iterative approaches are easier to be implemented and often successful, while symbolic approaches return more information which is needed during the design of a geometric constraint model by a human engineer. Data quality with respect to geometric constraint data means to avoid under-constrained models and to use over-constrained cases carefully. Symbolic solvers are able to determine whether underand/or over-constrained situations occur. Integration of the algorithmic software dealing with geometric constraints can be achieved by a modular approach, which should be driven by the algorithms rather than by data structures. Interface design and data structure hiding are good strategies to support independent development of the software components. Persistent storage of geometric constraint data shall be implemented according to the user interface meaning, where a modular data schema approach avoids time consuming integration processes.