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Home > TERATEC FORUM > Workshop 2
Big Data: Optimizing decision making through Data Analytics
Parallel tools for predictive analytics of multiple unstructured datasets
Marc WOLFF, Ingénieur MathWorks HPC/Big Data
The rise of the Internet of Things and Big Data Analytics has made multiple data sources available to companies and allows them to take advantage of sophisticated tools like Machine Learning algorithms to assist or even automate decision making processes. This approach opens new perspectives, for instance, in the areas of industrial automation and control.
Exploiting data sets presents however many challenges: raw data are available under various forms, often unstructured and in very large amounts. Processing these large volumes of information generally requires well-suited infrastructures and appropriate analysis algorithms that take advantage of supercomputers. Ideally, the data processing complexity should be hidden from the user, to facilitate analysis and interpretation.
In this talk, we will present a set of tools that allow importing unstructured datasets from various sources and applying predictive analytics techniques. We will also introduce parallel programming techniques that allow engineers and scientists to easily scale their algorithms to large HPC infrastructures, without devoting significant time to the implementation.
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Marc Wolff is application engineer HPC and Big Data in MathWorks France and participates in pre-sales activities on the adoption of HPC solutions, Big Data, Data Analytics and Machine Learning.Previously, Marc worked in the Transtec and Bruker Biospin companies. He holds a PhD in applied mathematics from the University of Strasbourg.Marc obtained his PhD in Applied Mathematics at the University of Strasbourg. He did his thesis at CEA (Commissariat à l'Energie Atomique) and studied the construction of high-order numerical schemes for the MHD equations, which required the development of a parallel and high-performance simulation platform (C++ language, MPI library). |
