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000697267 020__ $$a9781461492429 $$qelectronic book
000697267 020__ $$a1461492424 $$qelectronic book
000697267 020__ $$z9781461492412
000697267 020__ $$z1461492416
000697267 035__ $$aSP(OCoLC)ocn871042971
000697267 035__ $$aSP(OCoLC)871042971
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000697267 050_4 $$aQA76.9.D343
000697267 08204 $$a006.3/12$$223
000697267 24500 $$aLarge-scale data analytics$$h[electronic resource]  /$$cAris Gkoulalas-Divanis, Abderrahim Labbi, editors.
000697267 264_1 $$aNew York :$$bSpringer,$$c[2014]
000697267 264_4 $$c©2014
000697267 300__ $$a1 online resource (xxiii, 257 pages) :$$billustrations
000697267 336__ $$atext$$btxt$$2rdacontent
000697267 337__ $$acomputer$$bc$$2rdamedia
000697267 338__ $$aonline resource$$bcr$$2rdacarrier
000697267 504__ $$aIncludes bibliographical references and index.
000697267 5050_ $$aThe Family of Map-Reduce -- Optimization of Massively Parallel Data Flows -- Mining Tera-Scale Graphs with "Pegasus" -- Customer Analyst for the Telecom Industry -- Machine Learning Algorithm Acceleration using Hybrid (CPU-MPP) MapReduce Clusters -- Large-Scale Social Network Analysis -- Visual Analysis and Knowledge Discovery for Text -- Practical Distributed Privacy-Preserving Data Analysis at Large Scale
000697267 506__ $$aAccess limited to authorized users.
000697267 5208_ $$aThis edited book collects state-of-the-art research related to large-scale data analytics that has been accomplished over the last few years. This is among the first books devoted to this important area based on contributions from diverse scientific areas such as databases, data mining, supercomputing, hardware architecture, data visualization, statistics, and privacy. There is increasing need for new approaches and technologies that can analyze and synthesize very large amounts of data, in the order of petabytes, that are generated by massively distributed data sources. This requires new distributed architectures for data analysis. Additionally, the heterogeneity of such sources imposes significant challenges for the efficient analysis of the data under numerous constraints, including consistent data integration, data homogenization and scaling, privacy and security preservation. The authors also broaden reader understanding of emerging real-world applications in domains such as customer behavior modeling, graph mining, telecommunications, cyber-security, and social network analysis, all of which impose extra requirements for large-scale data analysis. Large-Scale Data Analytics is organized in 8 chapters, each providing a survey of an important direction of large-scale data analytics or individual results of the emerging research in the field. The book presents key recent research that will help shape the future of large-scale data analytics, leading the way to the design of new approaches and technologies that can analyze and synthesize very large amounts of heterogeneous data. Students, researchers, professionals and practitioners will find this book an authoritative and comprehensive resource
000697267 588__ $$aDescription based on print version record.
000697267 650_0 $$aData mining.
000697267 7001_ $$aGkoulalas-Divanis, Aris,$$eeditor of compilation.
000697267 7001_ $$aLabbi, Abderrahim,$$eeditor of compilation.
000697267 77608 $$iPrint version:$$tLarge-scale data analytics$$z9781461492412$$w(OCoLC)870324636
000697267 85280 $$bebk$$hSpringerLink
000697267 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://dx.doi.org/10.1007/978-1-4614-9242-9$$zOnline Access
000697267 909CO $$ooai:library.usi.edu:697267$$pGLOBAL_SET
000697267 980__ $$aEBOOK
000697267 980__ $$aBIB
000697267 982__ $$aEbook
000697267 983__ $$aOnline
000697267 994__ $$a92$$bISE