UID:
almahu_9947421355502882
Format:
PDFs (342 pages) :
,
illustrations.
ISBN:
9781466647008 (ebook)
Content:
"This book discusses the exponential growth of information size and the innovative methods for data capture, storage, sharing, and analysis for big data"--Provided by publisher.
Note:
Technologies for big data / Kapil Bakshi -- Applying the K-means algorithm in big raw data sets with Hadoop and MapReduce / Ilias K. Savvas, Georgia N. Sofianidou, M-Tahar Kechadi -- Synchronizing execution of big data in distributed and parallelized environments / Gueyoung Jung, Tridib Mukherjee -- Parallel data reduction techniques for big datasets / Ahmet Artu Yildirim, Cem Özdogan, Dan Watson -- Techniques for sampling online text-based data sets / Lynne M. Webb, Yuanxin Wang -- Big data warehouse automatic design methodology / Francesco Di Tria, Ezio Lefons, Filippo Tangorra -- Big data management in the context of real-time data warehousing / M. Asif Naeem, Gillian Dobbie, Gerald Weber -- Big data sharing among academics / Jeonghyun Kim -- Scalable data mining, archiving, and big data management for the next generation astronomical telescopes / Chris A. Mattmann [and 12 others] / Efficient metaheuristic approaches for exploration of online social networks / Zorica Stanimirovic, Stefan Miškovic -- Big data at scale for digital humanities: an architecture for the HathiTrust research center / Stacy T. Kowalczyk [and 12 others] -- GeoBase: indexing NetCDF files for large-scale data analysis / Tanu Malik -- Large-scale sensor network analysis: applications in structural health monitoring / Joaquin Vanschoren, Ugo Vespier, Shengfa Miao, Marvin Meeng, Ricardo Cachucho, Arno Knobbe -- Accelerating large-scale genome-wide association studies with graphics processors / Mian Lu, Qiong Luo -- The need to consider hardware selection when designing big data applications supported by metadata / Nathan Regola, David A. Cieslak, Nitesh V. Chawla -- Excess entropy in computer systems / Charles Loboz -- A review of system benchmark standards and a look ahead towards an industry standard for benchmarking big data workloads / Raghunath Nambiar, Meikel Poess.
,
Also available in print.
,
Mode of access: World Wide Web.
Additional Edition:
Print version: ISBN 1466646993
Additional Edition:
ISBN 9781466646995
Language:
English
DOI:
10.4018/978-1-4666-4699-5
URL:
Chapter PDFs via platform:
Bookmarklink
