Buch, Englisch, 257 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 4336 g
Buch, Englisch, 257 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 4336 g
ISBN: 978-1-4939-4225-1
Verlag: Springer US
This 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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
The 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.
