Titlebook: Large-Scale Data Analytics; Aris Gkoulalas-Divanis,Abderrahim‘Labbi Book 2014 Springer Science+Business Media New York 2014 Big data.GPU p

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Customer Analyst for the Telecom Industry,f the Open Directory Project categories to generate user profiles. We then describe an end-to-end analysis flow and discuss its challenges. Last, we validate our methods through extensive experiments based on real data sets.

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Machine Learning Algorithm Acceleration Using Hybrid (CPU-MPP) MapReduce Clusters, In this chapter, we propose the integration of massively threaded shared-memory multiprocessors into MapReduce-based clusters, creating a unified heterogeneous architecture that enables executing Map and Reduce operators on thousands of threads across multiple GPU devices and nodes, while maintaini

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Practical Distributed Privacy-Preserving Data Analysis at Large Scale,dition steps, which can be privately evaluated using efficient cryptographic tools, namely verifiable secret sharing over . field (e.g., 32 or 64 bits), which have the same cost as regular, non-private arithmetic. This paradigm supports a large number of statistical learning algorithms, including SV

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0940-7677 t attention very recently in the context of quantum magnetisation of glasses and other frustrated systems. This book gives a detailed discussion of the various theoretical techniques developed for the study of transverse Ising models and of the results of these studies with regular and random frustr

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XI Analyse 6: Melles Rhizom und Kehlmanns Figurenpuzzle,riments sollten sich aber die Episoden – »jede abgeschlossen, aber alle eng zusammengehörend in einem großen Bogen« – zu einem Ganzen fügen. Die Gattungsdeklaration . unterstreicht den wirkungsästhetischen Anspruch auf eine gewisse Totalität ebenfalls.

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Wayne L. Shebilskeller Modell für Customer Knowledge Management basiert auf Praxiserfahrungen und Forschungsarbeiten, die durch Fallstudien namhafter Unternehmen systematisch und fundiert aufbereitet werden..978-3-642-62452-0978-3-642-55496-4Series ISSN 1616-0002 Series E-ISSN 2364-236X