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In this thesis, we explore transitive combination of binary transformations and we study what the resulting problems are. We develop a catalog of six failure potentials that can manifest in failures with regards to consistency between the models. The knowledge about these failure potentials can inform a transformation developer about possible problems arising from the combination of transformations. One failure potential is a consequence of the transformation network topology and the used domain models. It can only be avoided through topology adaptations. Another failure potential emerges, when two transformations try to enforce conflicting consistency constraints. This can only be repaired through adaptation of the original consistency constraints. Both failure potentials are case-specific and cannot be solved without knowing which transformations will be combined. Furthermore, we develop two transformation implementation patterns to mitigate two other failure potentials. These patterns can be applied by the transformation developer to an individual transformation definition, independent of the combination scenario. For the remaining two failure potentials, no general solution was found yet and further research is necessary.

We evaluate the findings with a case study that involves two independently developed transformations between a component-based software architecture model, a UML class diagram and its Java implementation. All failures revealed by the evaluation could be classified with the identified failure potentials, which gives an initial indicator for the completeness of our failure potential catalog. The proposed patterns prevented all failures of their targeted failure potential, which made up 70% of all observed failures, and shows that the developed implementation patterns are applicable and help to mitigate issues occurring from transitively combining binary transformations.

In this thesis the effectiveness of density-based outlier detection algorithms (such as KNN, LOF and related methods) on entirely synthetically generated data are compared, using its underlying density as ground truth.

Die Sprache Modular Simulation Language modelliert die Kopplung zwischen verschiedenen Simulationen. Die Kopplung von Simulationen dient dem besseren Verständnis und der Austauschbarkeit. In dieser Arbeit wird geprüft, ob die Sprache alle nötigen Modelle erhält um universell Kommunikation, Struktur und Kopplung von beliebigen Simulationen darstellen zu können. Dazu wurde die Cloud Simulation Cloud Sim Plus ausgewählt und die Kommunikation von zwei Features dieser Simulation modelliert. Während der Modellierung wurden fehlende Elemente der Sprache identifiziert und Lösungsvorschläge integriert oder diskutiert. Das Ergebnis zeigt, dass sie Modular Simulation Language zum aktuellen Zeitpunkt nicht vollständig ist, das Thema aber weiter untersucht werden muss.

number of attributes, which are referred to as dimensions, hinder the search of objects that normally not occur. For instance, consider a money transaction that has been not legally carried out. Such objects are called outlier. A common method to detect outliers in high dimensional datasets are based on the search in subspaces of the dataset. These subspaces have the characteristics to reveal possible outliers. The most common evaluation of algorithms searching for subspaces is based on benchmark datasets. However, the benchmark datasets are often not suitable for the evaluation of these subspace search algorithms. In this context, we present a method that evaluates subspace search algorithms without relying on benchmark datasets by hiding outliers in the result set of a subspace search algorithm.

Mit Hilfe eines Systematic Literature Reviews soll in dieser Arbeit untersucht werden, welche Ansätze es in der Literatur zur Ausfallmodellierung der Hardware-Komponenten von sicherheitskritischen Systemen gibt. Die Recherche lieferte zwei Ansätze: Einer zeigt, wie die Modellierung von Hardware-Ausfällen um eine Weibull-verteile Ausfallrate erweitert werden kann. Ein anderer zeigt, wie die Zustandsmodellierung um einen weiteren Fehlerzustand ergänzt werden kann. Für diese Erweiterungen wurde eine Konzeption skizziert und anschließend deren Integration in die Modellauswertung von Palladio diskutiert.

Our contribution is two-fold: (1) we introduce a general streaming framework for training arbitrary generative models on data streams. Here, generative models are useful to capture the history of the stream. (2) We instantiate this framework with a Variational Autoencoder, which adapts its network architecture to the dimensionality of incoming data.

Our experiments against several benchmark outlier data sets show that AVA outperforms the state of the art and successfully adapts to streams with concept drift.