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In this paper we present the Urban Computing challenge and in particular we exemplify it in the context of traffic management. From our previous experiences in the field we draw requirements in terms of capacity to cope with heterogeneity in representation, semantics and defaults; with scale; with time-dependency of data; and with noisy, uncertain and(More)
This paper discusses a machine learning approach for binary classification problems which satisfies the specific requirements of safety-related applications. The approach is based on ensembles of local models. Each local model utilizes only a small subspace of the complete input space. This ensures the interpretability and verifiability of the local models,(More)
Real-world applications often require the joint use of data-driven and knowledge-based models. While data-driven models are learned from available process data, knowledge-based models are able to provide additional information not contained in the data. In this contribution, we propose a method to divide the input space on the basis of the validity ranges(More)