Stefan Berlik

Difference computation of large models

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.

Enforcing Privacy by Means of an Ontology Driven XACML Framework

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.

Directed Mutation by Means of the Skew-Normal Distribution

Directed mutation can improve the efficiency of processing many optimization problems. The directed mutation operator presented in this paper is based on the Skew-Normal Distribution. It is the first one that is not defined by case differentiation. Its expectation as well as its variance are convergent for all degrees of skewness and random number generation is simple and fast. An appropriate recombination scheme is given, and experimental results using this directed mutation are presented.

A Step Size Preserving Directed Mutation Operator

Using a directed mutation can improve the efficiency of processing many optimization problems. The first mutation operators of this kind proposed by Hildebrand [1], however, suffer from the asymmetry parameter influencing the mutation step size. Extreme asymmetry can lead to infinite step size. The operator presented here overcomes this drawback and preserves the step size.

Towards Usable Client-Centric Privacy Advisory for Mobile Collaborative Applications Based on BDDs

Considering privacy advisory for collaborative settings on mobile devices, this paper presents an innovative approach to simultaneously support dynamically reconfigurable privacy advisory and the usability of providing it. Regarded are interaction design requirements such as user-friendless and non-intrusive advisory as well as restrictions of todays mobile devices like CPU and memory consumption etc. The prototypic implementation of a client-centric privacy advisor based on binary decision diagrams shows that the proposed mechanisms integrated in an iPhone application can be effectively extended and correlated with a usable privacy-enhancing model.

Directed mutation operators – an overview

Directed mutation has shown to improve the efficiency of evolutionary algorithms significantly for a broad spectrum of optimization problems. When the first mutation operators of this kind, however, suffered from the asymmetry parameter influencing the mutation strength, in the meantime there are several new directed mutation operators available which overcome this drawback. The aim of this paper is to give an overview of all different operators in one single place. Their characteristics will be presented and their advantages and disadvantages are discussed. At the end a comparison and a summary is provided.

A design and optimization tool for screw type machines

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.

Optimizing screw machines using an evolution strategy with extended creation scheme

The optimization of screw machines with an evolution strategy led to the problem of having too few feasible offspring generated. Hence a penalty function method was implemented but turned out to be unsuitable for the given application. To overcome the drawback an extended offspring generation scheme hits then been developed.

Rule-Based Compilation of Graphical Structures

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 Generic Knowledge Integration Approach for Complex Process Control

Current manufacturing processes are characterized by their high complexity and require an increased control effort. Operating them effectively and efficiently is crucial and knowledge integration methods can make a valuable contribution to this. Presented here is a generic model predictive system that enables the integration of different sources of knowledge. In addition, the system is adaptive and allows for a self-adaptation to changing operating conditions and a self-optimization. The implementation of an inferential control mechanism finally ensures continuous process control in the absence of primary measurements.