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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,Mining Tera-Scale Graphs with “Pegasus”: Algorithms and Discoveries,r Peta-scale graphs? We propose a carefully selected set of fundamental operations, that help answer those questions, including diameter estimation, connected components, and eigenvalues. We package all these operations in ., which, to the best of our knowledge, is the first such library, implemente

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Visual Analysis and Knowledge Discovery for Text,isualization experts alike. The rapid growth of the data volume and heterogeneity, as well as the richness of metadata and the dynamic nature of text repositories, add to the complexity of the task. This chapter provides an overview of data visualization methods for gaining insight into large, heter

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Practical Distributed Privacy-Preserving Data Analysis at Large Scale, the challenges and opportunities in light of current and emerging analysis paradigms on large data sets. In particular, we present a framework for privacy-preserving distributed data analysis that is practical for many real-world applications. The framework is called . (P4P) and features a novel he

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