Data transfer, storage management, sharing, curation and most notably data analysis of often geographically
dispersed large quantities of data of experiments, observations, or computational simulations become ever more
important for science, research, industry and governments. Scientists and engineers that analyse these massive
datasets require therefore reliable infrastructures as well as scalable tools in order to perform ‘scientific big data
analytics (SBDA)’. This keynote will take stock of selected scientific and engineering use cases that take advantage
of parallel machine learning algorithms (e.g. classification, clustering, regression) in combination with established
statistical data mining methods in the light of new challenges faced with ‘big data’. It will critically review practice
and experience of selected community approaches and thus address several important questions: Is big data
always better data for analytics? Are big data analytics frameworks really providing the functionality they promise
or scientists require? How can the scientific big data analytics process be properly structured? What is the role of
the Research Data Alliance and Open Grid Forum in this context? Do we need a peer-review process for steering
the scientific big data analytics applications and evolution when using valuable storage and compute resources?
Bio:
Dr. - Ing. Morris Riedel is an Adjunct Associate Professor at the School of Engineering and Natural Sciences of the
University of Iceland. He received his PhD from the Karlsruhe Institute of Technology (KIT) and started the work in
parallel and distributed systems in the field of scientific visualization and computational steering of e-science
applications on large-scale HPC resources. He previously held various positions at the Juelich Supercomputing
Centre in Germany. At this institute, he is the head of a scientific research group focussed on ‘High Productivity
Data Processing’ as part of the Federated Systems and Data Division. Lectures given in universities such as the
University of Iceland, University of Applied Sciences of Cologne and University of Technology Aachen (RWTH
Aachen) include 'High Performance Computing & Big Data', ‘Statistical Data Mining', ‘Handling of large datasets’
and ‘Scientific and Grid computing’. His current research focusses on 'high productivity processing of big data' in
the context of scientific computing applications. He is currently a co-chair of the ‘big data analytics interest group’
of the Research Data Alliance (RDA) and contributes to the Open Grid Forum (OGF) in selected areas.
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