Nowadays enforcing privacy in enterprises is recognized as an issue of impact. Actually, it is a big challenge to adapt normative laws and regulations in a software system. It is a challenging task to include the formalized laws and rules in enterprises since e.g. more than one regulation may affect the terms of privacy concerning one situation. Traditional access control provides a general mechanism for assigning rights to individual users or roles. In the context of privacy this is insufficient; it offers no means to fulfil certain aspects such as limitations to the duration for which private data may be stored. To enforce privacy in enterprises we further need a fine granular access control mechanism on the data entities to ensure that every aspect of privacy can be reflected. This paper provides a novel solution for this by means of ontologies. The usage of ontologies in our approach differs from the conventional form in focusing on generating access control policies which are adapted from our software framework to provide fine granular access on the diverse data sources.

Modern software engineering practices lead to large models which exist in many versions. Version management systems should offer a service to compare, and possibly merge, these models. The computation of a difference between large models is a big challenge; current algorithms are too inefficient here. We present a new technique for computing differences between models. In practical tests, this technique has been an order of magnitude faster than currently known algorithms. The main idea is to use a high-dimensional search tree for efficiently finding similar model elements. Individual elements are mapped onto a vector of numerical values using a collection of metrics for models and a numerical representation of the names which occur in a model.

The aim of this work is to present recent advances in porting the directed covariance matrix adaptation-evolution strategy (DCMA-ES) from the test function environment to a real-world application. DCMA-ES is a CMA-ES variant enhanced by the powerful concept of directed mutation. So far, DCMA-ES has already proven to outperform the classical CMA-ES for several test functions. Hence, the target is now to benefit from the strength of this approach also in real-world scenarios, as for example in optimizing screw-type machines. The paper is organized as follows: first we outline the DCMA-ES and the functioning of the screw-type machine. Then the performance of DCMA-ES on some test functions is presented as motivation for the porting whose current status is discussed. In the final section, conclusions are provided and the future work is sketched.

Among the various types of decision support systems, decision-theoretic models and rule-based systems have gained considerable attraction. Both approaches have advantages and disadvantages. Decision-theoretic models like decision networks dispose of a sound fundamental mathematical basis and comfortable knowledge engineering tools. Rule-based systems provide an efficient execution architecture and represent knowledge in an explicit, intelligible way. In this paper, we consider fuzzy rule-based systems as a special type of condensed decision model. We outline a knowledge transformation and compilation scheme which allows one to transform a decision-theoretic model into a fuzzy rule base and, hence, to combine the advantages of both approaches. An experimental example is given as demonstration of the described techniques.

A tool for the interactive design of screw type machines are presented that also comprises an optimization module. Beside the usual features of computer aided design tools, it has unique support for screw type machine design, as for example automated calculation of the female rotor for an arbitrary given male rotor. The optimization relies on an evolution strategy with a new mutation operator, called directed mutation. It was presented in short and some results using this optimization module are given. In the final section, conclusions are provided.