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However, most approaches assume that the data's dimensionality, i.e., the number of attributes, stays constant over time. This assumption is unjustified in many real-world use cases, such as sensor networks or computer cluster monitoring. Feature-evolving data streams do not impose this restriction and thereby pose additional challenges.

In this thesis, we extend the well-known Local Outlier Factor (LOF) algorithm for outlier detection from the static case to the feature-evolving setting. Our algorithm combines subspace projection techniques with an appropriate index structure using only bounded computational resources. By discarding old observations our approach also deals with concept drift. We evaluate our approach against the respective state-of-the-art methods in the static case, the streaming case, and the feature-evolving case.

Although state-of-the-art approaches deliver good results, they are hard to train due to the high number of variables, lacking the ability to generalize, and need to make many simplifying assumptions. In contrast to theory-based models, purely data-driven approaches require large datasets and are often unable to produce physically consistent results. Theory-Guided Data Science (TGDS) aims at using scientific knowledge to improve the effectiveness of Data Science models in scientific discovery. This concept has been very successful in several domains including climate science and material research.

Our work is the first one to apply TGDS to battery systems by working together closely with domain experts. We compare the performance of different TGDS approaches against each other as well as against the two baselines using only theory-based EC-Models and black-box Machine Learning models.

participating entities often change and a lot of data exchange happens with external organizations such as suppliers or producers which brings concern about unauthorized data access. This creates the need for access control systems to be able to handle such a combination of a highly dynamic system and the arising concern about the security of data. In many situations the decision for access control depends on the context information of the requester. Another problem of dynamic system is that the manual development of access policies can be time consuming and expensive. Approaches using automated policy generation have shown to reduce this effort. In this master thesis we introduce a concept which combines context based model-driven security with automated policy generation and evaluate if it is a suitable option for the creation of access control systems and if it can reduce the effort in policy generation. The approach makes use of usage and misusage diagrams which are on a high architectural abstraction level to derive and combine access policies for data elements which are located on a lower abstraction level.

LogMeIn Inc. is a global company offering Software-as-a-Service solutions for remote collaboration and IT management. An example product is GoToMeeting which allows to create and join virtual meeting rooms.

This Master’s Thesis presents the JoinTracer approach which enables the telemetry-data-based traceability of GoToMeeting join-flows of the GoToMeeting architecture. The approach combines new mechanics and already existing traceability techniques from different traceability communities to leverage synergies and to enable the traceability of individual join-flows. In this work, the JoinTracer approach is designed and implemented as well as evaluated regarding the functionality, performance and acceptance. The results are discussed to analyze the future development and the applicability of this approach to other contexts as well.

Current research includes Data Science approaches that need large amounts of data, which are costly when performing scientific experiments. Theory-Guided Data Science aims to combine Data Science approaches with domain knowledge to reduce the amount of data needed while predicting the output precisely.

However, even state-of-the-art Theory-Guided Data Science approaches lack the possibility to model the slopes occuring in the transition phases. In this thesis we aim to close this gap by proposing a new loss constraint that represents both transition and stationary phases. Our method is compared with theoretical and Data Science approaches on synthetic and real world data.