**1. Introduction**

Expert systems, or more precise decision support systems, are valuable tools for structuring the results of scientific research and to translate this to knowledge. The decision support sys‐ tem Determinator is now used for several years as a platform for models to identify subjects [1, 2]. The system is based on the two main different procedures for identification [3, 4]; a sin‐ gle access key (tree) and a free access key (matrix). The latter option provides the possibility to calculate the match between the subject as chosen by the user and the objects as included in the data model, based on a range of characteristics. In addition, a matrix allows to make selections, to filter the set of available objects and to compare two objects for their variability.

Datamodels for Determinator can be constructed using a Developer, which is part of the en‐ tire Determinator platform. Besides defining the objects (descriptions, illustrations and la‐ bels), the characteristics, and the connection between them (the matrix), the Developer also allows to evaluate the structure of the data model. Several parameters and metadata for the evaluation of a data model are part of the Developer.

This chapter provides the logic basis for the Determinator platform and introduces the back‐ ground and calculation of four different parameters for the evaluation of data models: the coverage of variability space of the total data model or of a single object, the redundancy in a data model, and the capability to distinguish between different objects. The way in which these parameters are developed and applied will be demonstrated using a real case concern‐ ing the diagnosis of illegal hormone treatment of veal calves [5, 6]. The applicability of the parameters will be discussed and the development of a specific case (histological diagnosis) in a general platform (Determinator) will be evaluated.

© 2012 van Raamsdonk et al.; licensee InTech. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2012 van Raamsdonk et al.; licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
