| Abstract: |
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As the Tsunami of captured data continues to rise unabated, traditional analytics tools are not well suited to capturing the full value of big data. The volume of data is too large for comprehensive analysis, and the range of potential quantitative and qualitative correlations and relationships between disparate data sources, from back-end customer databases to live web based clickstreams, are overwhelming analysts to test different hypotheses and derive the value buried in the data.
Freed from the limitations of human scale thinking and analysis, machine learning and deep learning practitioners can discover and display the patterns buried in the data. In this presentation, we will cover advances in supercomputing and scalable analytics that will enable supercomputer users to extract value from captured data. We will also examine how the technologies and systems used to solve these problems are emerging and evolving and how changes in system architecture and a more holistic approach can change the game for how the supercomputing community can achieve orders of magnitude improvements in processing massive data and discovering new insights faster in the future. |
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