Prof. Dr. - Ing. Morris Riedel

Publications

Scientific Big Data Analytics by HPC

Publications

Scientific Big Data Analytics by HPC

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...

HPDBSCAN Highly Parallel DBSCAN 0

Publications

HPDBSCAN – Highly Parallel DBSCAN

HPDBSCAN – Highly Parallel DBSCAN Goetz, M., Bodenstein, C., Riedel, M.: HPDBSCAN – Highly Parallel DBSCAN, in conference proceedings of ACM/IEEE International Conference for High Performance Computing, Networking, Storage, and Analysis (SC 2015), Machine Learning in HPC Environments (MLHPC 2015) Workshop, November 15-20, 2015, Austin, Texas, USA [ EVENT ] [ DOI ] [ JUSER ] [ RESEARCHGATE ] Abstract: Clustering algorithms in the field of data-mining are used to aggregate similar objects into common groups. One of the best-known of these algorithms is called DBSCAN. Its distinct design enables the search for an apriori unknown number of arbitrarily shaped...

image classification 0

Publications

On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods

On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods Gabriele Cavallaro, Morris Riedel, Matthias Richerzhagen, Jon Atli Benediktsson, Antonio Plaza IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Issue 99, pp. 1-13, 2015 [ DOI ] [ Juelich ] Abstract: Owing to the recent development of sensor resolutions onboard different Earth observation platforms, remote sensing is an important source of information for mapping and monitoring natural and man-made land covers. Of particular importance is the increasing amounts of available hyperspectral data originating from airborne and satellite sensors such as AVIRIS,...

Ultrascan Scientific Gateway 0

Publications

Improvements of the UltraScan Scientific Gateway to Enable Computational Jobs on Large-scale and Open-standards based Cyberinfrastructures

Improvements of the UltraScan Scientific Gateway to Enable Computational Jobs on Large-scale and Open-standards based Cyberinfrastructures Memon, M.S., Attig, N., Demeler, B., Gorbet, G., Girmshaw, A., Gunathilake, L., Janetzko, F., Lippert, T., Marru, S., Singh, R., Riedel, M.: Improvements of the UltraScan Scientific Gateway to enable Computational Jobs on Large-scale and Open-standards based Cyberinfrastructures, in conference proceedings of the 2013 XSEDE Conference on Extreme Science and Engineering Discovery Environment Gateway to Discovery (XSEDE 2013), July 22-25, 2013, San Diego, California, USA [ EVENT ] [ DOI ] [ JUSER ] [ RESEARCHGATE ] Abstract: The UltraScan data analysis application is...

Morris Riedel Digital Library 0

Publications

Digital Library

Digital Library Deep Learning and Physical Models Wiewel, S., Becher, M., Thuerey, N.: Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow, arXiv, submitted to NIPS 2018, 2018 Abstract: Our work explores methods for the data-driven inference of temporal evolutions of physical functions with deep learning techniques. More specifically, we target fluid flow problems, and we propose a novel LSTM-based approach to predict the changes of the pressure field over time. The central challenge in this context is the high dimensionality of Eulerian space-time data sets. Key for arriving at a feasible algorithm is a technique for dimensionality reduction...