LEADER 03532oam 2200493 450 001 9910298565403321 005 20190911103512.0 010 $a1-4614-9242-4 024 7 $a10.1007/978-1-4614-9242-9 035 $a(OCoLC)871042971 035 $a(MiFhGG)GVRL6UZC 035 $a(EXLCZ)993710000000083336 100 $a20131105d2014 uy 0 101 0 $aeng 135 $aurun|---uuuua 181 $ctxt 182 $cc 183 $acr 200 00$aLarge-scale data analytics /$fAris Gkoulalas-Divanis, Abderrahim Labbi, editors 205 $a1st ed. 2014. 210 1$aNew York :$cSpringer,$d2014. 215 $a1 online resource (xxiii, 257 pages) $cillustrations (chiefly color) 225 0 $aGale eBooks 300 $aDescription based upon print version of record. 311 $a1-4614-9241-6 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aThe Family of Map-Reduce -- Optimization of Massively Parallel Data Flows -- Mining Tera-Scale Graphs with "Pegasus" -- Customer Analyst for the Telecom Industry -- Machine Learning Algorithm Acceleration using Hybrid (CPU-MPP) MapReduce Clusters -- Large-Scale Social Network Analysis -- Visual Analysis and Knowledge Discovery for Text -- Practical Distributed Privacy-Preserving Data Analysis at Large Scale. 330 $aThis edited book collects state-of-the-art research related to large-scale data analytics that has been accomplished over the last few years. This is among the first books devoted to this important area based on contributions from diverse scientific areas such as databases, data mining, supercomputing, hardware architecture, data visualization, statistics, and privacy. There is increasing need for new approaches and technologies that can analyze and synthesize very large amounts of data, in the order of petabytes, that are generated by massively distributed data sources. This requires new distributed architectures for data analysis. Additionally, the heterogeneity of such sources imposes significant challenges for the efficient analysis of the data under numerous constraints, including consistent data integration, data homogenization and scaling, privacy and security preservation. The authors also broaden reader understanding of emerging real-world applications in domains such as customer behavior modeling, graph mining, telecommunications, cyber-security, and social network analysis, all of which impose extra requirements for large-scale data analysis. Large-Scale Data Analytics is organized in 8 chapters, each providing a survey of an important direction of large-scale data analytics or individual results of the emerging research in the field. The book presents key recent research that will help shape the future of large-scale data analytics, leading the way to the design of new approaches and technologies that can analyze and synthesize very large amounts of heterogeneous data. Students, researchers, professionals and practitioners will find this book an authoritative and comprehensive resource. 606 $aData mining 606 $aDatabase management 615 0$aData mining. 615 0$aDatabase management. 676 $a004 676 $a005.7 676 $a005.74 676 $a005.8 702 $aGkoulalas-Divanis$b Aris 702 $aLabbi$b Abderrahim 801 0$bMiFhGG 801 1$bMiFhGG 906 $aBOOK 912 $a9910298565403321 996 $aLarge-Scale Data Analytics$92135960 997 $aUNINA