Titlebook: Conquering Big Data with High Performance Computing; Ritu Arora Book 2016 Springer International Publishing Switzerland 2016 Big data.High

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Big Data Processing in the eDiscovery Domain,er evidence that may be pertinent to legal proceedings, compliance needs, litigation or other investigations. The eDiscovery practitioners are typically required to produce results with short turnaround times, utilizing mostly commodity solutions that are available at their disposal, while still con

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Conquering Big Data Through the Usage of the Wrangler Supercomputer,gh Performance Computing (HPC) systems. Working with ‘big data’ can involve analyzing thousands of files that need to be rapidly opened, examined and cross-correlated—tasks that classic HPC systems might not be designed to do. Such tasks can be efficiently conducted on a data-intensive supercomputer

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Book 2016on Big Data, it covers a variety of alternatives to Hadoop, and explains the differences between HPC platforms and Hadoop..Written by professionals and researchers in a range of departments and fields, this book is designed for anyone studying Big Data and its future directions. Those studying HPC will also find the content valuable..

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,Die bunte Welt der „Ambient Intelligence“,es, techniques, and technologies, namely, High Performance Computing (HPC), High-Throughput Computing (HTC), and Hadoop. First, each of these topics is defined and discussed individually. These topics are then discussed further in the light of enabling short time to discoveries and, hence, with respect to their importance in conquering Big Data.

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An Introduction to Big Data, High Performance Computing, High-Throughput Computing, and Hadoop,es, techniques, and technologies, namely, High Performance Computing (HPC), High-Throughput Computing (HTC), and Hadoop. First, each of these topics is defined and discussed individually. These topics are then discussed further in the light of enabling short time to discoveries and, hence, with respect to their importance in conquering Big Data.

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Data Movement in Data-Intensive High Performance Computing,ssor level, with case study and contributions from researchers at the San Diego Supercomputer Center, the Oak Ridge National Laboratory, the Pacific Northwest National Laboratory, and the University of Delaware.

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Big Data Techniques as a Solution to Theory Problems,ertainty. In our applications of this algorithm we use Hierarchical Database Format (HDF5) distributed storage and I/O as well as message passing interface (MPI) for parallel computation of a large number of small optimization problems.