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objects for a scenario in the energy market domain. For this purpose we use a scientific workflow management system, namely the Apache Taverna. We will then insure the correctness of the data flow of the process model. The theoretical starting point for this thesis is a verification approach of the supervisors of this thesis. It formalizes BPMN process models by mapping them to Petri Nets and unfolding the execution semantics regarding data. We develop an algorithm for transforming Taverna workflows to BPMN 2.0. We then ensure the correctness of the data-flow of the process model. For this purpose we analyse which compliance rules are relevant for the data objects and how to specify them using anti-patterns.

In our work, we propose measures to analyse the impact of aggregation on clustering and evaluate them experimentally. In particular, we aggregate with standard summary statistics and assess the impact using clustering structure measures, internal validity indices, external validity indices and instance-based forecasting. We adapt these evaluation measures and other data mining techniques to our use case. Furthermore, we propose a decision framework which allows to choose an aggregation level and other experimental settings, considering the trade-off between clustering quality and computational cost.

Our extensive experiments comprise the cross-product of 6 physical attributes, 7 clustering algorithms, 7 aggregation techniques, 9 aggregation levels and 13 time series dissimilarities. We use real-world data from different machines and sensors of a production site at the KIT Campus North, extracting time series of fixed and variable length. Overall, we find that clustering results become less similar the more the data is aggregated. However, the exact effect and value of evaluation measures depends on the type of aggregate, clusteringalgorithm, dataset and dissimilarity measure.

The work’s first part focuses on the binary anomaly detection classification problem and aims to predict an engine’s deficiency status. Here, we manage to recognize more than 90% of the faulty engines. In the second part, we extend our analysis to the multi-class classification problem with high-unbalanced classes. Here, our objective is to forecast the exact type of failure. To some extent, this situation shows similarities with the microarray analysis – we observe high-dimensional data with few instances available. This thesis develops a relevance-driven feature engineering meta-algorithm framework. We study the integration of feature relevance evaluation in the construction process of new features. We also use ensemble feature selection algorithms and define our own criteria to determine the relevance of feature subsets. These criteria are integrated in the feature engineering process in order to accelerate it by ignoring parts of the search space without significantly degrading the data quality.

Wir versuchen die Vorteile beider Ansätze zu verschmelzen, indem im Voraus optimierte Parameterkonfigurationen auf der Zielhardware, sowie unter Umständen mit einer anderen Anwendung, verwendet werden. Wir evaluieren sowohl die Hardware- als auch die Anwendungsportabilität der Konfigurationen anhand von fünf Beispielanwendungen.

Diese Arbeit beschäftigt sich damit unterschiedlich sprachige Korpora vergleichbar zu machen. Darüber hinaus werden erste Ansätze zum Vergleich zeitlicher Entwicklungen zwischen verschiedenen Sprachkorpora gegeben.