Michael Borth

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Within a research project at DaimlerChrysler we use vehicles as mobile data sources for distributed knowledge discovery. We realized that current approaches are not suitable for our purposes. They aim to infer a global model and try to approximate the results one would get from a single joined data source. Thus, they treat distribution as a technical issue(More)
Many expert systems for diagnosis, prediction, and analysis in complex dynamic scenarios use Bayesian networks for reasoning under uncertainty. These networks often benefit from adaptations to their specific conditions by machine learning on operational data. The knowledge encoded in these adapted networks yields insights as to typical modes of operations,(More)
Private and public clouds are getting more and more common. With them comes the need to analyze data stored by different applications in different clouds. Different clouds and applications tend to enforce the use of different data stores, which makes it even harder to aggregate information. The main outcome is that integrating different data sources(More)
Smart system of systems adapt to their context, current situation, and configuration. To engineer such systems’ behavior, we need to design and evaluate system-level control strategies and the intelligent management of key scenarios. We propose a model-based approach called probabilistic system summaries to explore related design choices, e.g., where to put(More)
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