000 03927nam a22005535i 4500
001 978-1-4614-9242-9
003 DE-He213
005 20200421111853.0
007 cr nn 008mamaa
008 140108s2014 xxu| s |||| 0|eng d
020 _a9781461492429
_9978-1-4614-9242-9
024 7 _a10.1007/978-1-4614-9242-9
_2doi
050 4 _aQA76.9.D3
072 7 _aUN
_2bicssc
072 7 _aUMT
_2bicssc
072 7 _aCOM021000
_2bisacsh
082 0 4 _a005.74
_223
245 1 0 _aLarge-Scale Data Analytics
_h[electronic resource] /
_cedited by Aris Gkoulalas-Divanis, Abderrahim Labbi.
264 1 _aNew York, NY :
_bSpringer New York :
_bImprint: Springer,
_c2014.
300 _aXXIII, 257 p. 83 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _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.
520 _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.
650 0 _aComputer science.
650 0 _aInformation technology.
650 0 _aBusiness
_xData processing.
650 0 _aComputer security.
650 0 _aComputers.
650 0 _aDatabase management.
650 0 _aData mining.
650 1 4 _aComputer Science.
650 2 4 _aDatabase Management.
650 2 4 _aInformation Systems and Communication Service.
650 2 4 _aIT in Business.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aSystems and Data Security.
700 1 _aGkoulalas-Divanis, Aris.
_eeditor.
700 1 _aLabbi, Abderrahim.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781461492412
856 4 0 _uhttp://dx.doi.org/10.1007/978-1-4614-9242-9
912 _aZDB-2-SCS
942 _cEBK
999 _c56249
_d56249