Titlebook: Business Information Systems; 23rd International C Witold Abramowicz,Gary Klein Conference proceedings 2020 Springer Nature Switzerland AG

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https://doi.org/10.1007/978-3-030-53337-3IoT; artificial intelligence; big data; business process management; chatbots; data analytics; data scienc

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Business Information Systems978-3-030-53337-3Series ISSN 1865-1348 Series E-ISSN 1865-1356

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https://doi.org/10.1007/978-3-658-37350-4suitable early Parkinson Patterns, the investigation of this phenomenon is highly relevant. The analysis of sleep is currently done by manual analysis of polysomnography (PSG), which leads to divergent scoring results by different experts. Automated sleep stage detection can help deliver accurate, r

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https://doi.org/10.1007/978-3-642-46247-4 One of the most crucial steps in the planning of a system includes the modeling of the underlying architecture. However, as of now, no standardized approach exists that facilitates the modeling of big data system architectures (BDSA). In this research, a systematic approach is presented that delive

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https://doi.org/10.1007/978-3-642-46247-4cal analysis. However, especially when working with massive amounts of data, spreadsheet applications have their limitations. To cope with this issue, we introduce a human-in-the-loop approach for scalable data preprocessing using sampling. In contrast to state-of-the-art approaches, we also conside

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https://doi.org/10.1007/978-3-663-05365-1ance between event data and normative models, and enhancing all aspects of processes. Recently, new techniques have been developed to analyze event data containing uncertainty; these techniques strongly rely on representing uncertain event data through graph-based models capturing uncertainty. In th