Scientific Big Data Analytics by HPC Thomas Lippert, Daniel Mallmann, Morris Riedel Publication Series of the John von Neumann Institute for Computing (NIC) NIC Series 48, 417, ISBN 978-3-95806-109-5, pp. 1 – 10, 2016 [ PID ] [ Juelich ] Abstract: Storing, managing, sharing, curating and especially analyzing huge amounts of data face an immense visibility and importance in industry and economy as well as in science and research. Industry and economy exploit ’Big Data’ for predictive analysis, to increase the efficiency of infrastructures, customer segmentation, and tailored services. In science, Big Data allows for addressing problems with complexities that...