Informatica

Nowadays, ontologies play a central role in many computer science problems such as data modelling, data exchange, integration of heterogeneous data and models or software reuse. Yet, if many methods of ontology based conceptual data modelling have been proposed, only few attempts have been made to ontology axioms based modelling of business rules, which make an integral part of each conceptual data model. In this paper, we present the approach how ontology axioms can be used for business rules implementation. Our proposal we apply for the transformation of PAL (Protege Axiom Language) constraints (ontology axioms), which is based on KIF (Knowledge Interchange Format) and is part of KIF ontology, into OCL (Object Constraint Language) constraints, which are part of a UML class diagram. Z language is used to formalise the proposal and describe the transformation. The Axiom2OCL plug-in is created for automation of the transformation and a case study is carried out.

We present an analysis of the separation of concerns in multi-language design and multi-language specifications. The basis for our analysis is the paradigm of the multi-dimensional separation of concerns, which claims that multiple dimensions of concerns in a design should be implemented independently. Multi-language specifications are specifications where different concerns of a design are implemented using separate languages as follows. (1) Target language(s) implement domain functionality. (2) External (or scripting, meta-) language(s) implement generalisation of the repetitive design features, introduce variations, and integrate components into a design. We present case studies and experimental results for the application of the multi-language specifications in hardware design.

Structurization of the sample covariance matrix reduces the number of the parameters to be estimated and, in a case the structurization assumptions are correct, improves small sample properties of a statistical linear classifier. Structured estimates of the sample covariance matrix are used to decorellate and scale the data, and to train a single layer perceptron classifier afterwards. In most from ten real world pattern classification problems tested, the structurization methodology applied together with the data transformations and subsequent use of the optimally stopped single layer perceptron resulted in a significant gain in comparison with the best statistical linear classifier – the regularized discriminant analysis.

An analytical equation for a generalization error of minimum empirical error classifier is derived for a case when true classes are spherically Gaussian. It is compared with the generalization error of a mean squared error classifier – a standard Fisher linear discriminant function. In a case of spherically distributed classes the generalization error depends on a distance between the classes and a number of training samples. It depends on an intrinsic dimensionality of a data only via initialization of a weight vector. If initialization is successful the dimensionality does not effect the generalization error. It is concluded advantageous conditions to use artificial neural nets are to classify patterns in a changing environment, when intrinsic dimensionality of the data is low or when the number of training sample vectors is really large.