Conference Presentation (Other)

FZJ-2016-00938

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Practice & Experience with Scalable Clustering Algorithms for Statistical Earth Science Data Mining

Götz, M. (Corresponding author)FZJ*

2015

Abstract: Big Data has plausibly reached the peak of its technology “hype” cycle, at least in geosciences. For a new technology to evolve, mature, and realize its maximum potential, it must successfully survive the transition through the ensuing "trough of disillusionment". Currently, the term “Big Data” seems to be subject to individuals’ interpretations, especially in a community such as ours, i.e. Earth Science, which has had a long, if not the longest, history in trying to use, and make sense out of, large volumes of data. Therefore, we seek abstracts in this session that can help the community 1) to better define the Big Data challenges in Earth Science, 2) to report and describe on-going or up-coming “Big Data” practices, or 3) to identify the opportunities for addressing the challenges and reaping benefits, with an aim to focus our collective efforts on the challenges and nurture the maturation of Big Data in Earth Science.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 512 - Data-Intensive Science and Federated Computing (POF3-512) (POF3-512)

Appears in the scientific report 2015

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