Stefan Oehmcke

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Diary studies are often applied in HCI research to collect qualitative user impressions. Unfortunately, the period between creation of a diary entry and the later reflection can be too long, which leads to a limited currentness and contextuality. This eventually results in incomplete or misinterpreted data. In this paper we present Storyteller, a mobile(More)
The imputation of partially missing multivariate time series data is critical for its correct analysis. The biggest problems in time series data are consecutively missing values that would result in serious information loss if simply dropped from the dataset. To address this problem, we adapt the k-Nearest Neighbors algorithm in a novel way for multivariate(More)
Virtual sensors are getting more and more important as replacement and quality control tool for expensive and fragile hardware sensors. We introduce a virtual sensor application with marine sensor data from two data sources. The virtual sensor models are built upon recurrent neural networks (RNNs). To take full advantage of past data, we employ the time(More)
In this work, we propose a new dimensionality reduction approach for generating low-dimensional embeddings of high-dimensional data based on an iterative procedure. The data set's dimensions are sorted depending on their variance. Starting with the highest variance, the dimensions are iteratively projected onto the embedding. The projection can be seen as(More)
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