000 04270nam a22005535i 4500
001 978-3-319-13497-0
003 DE-He213
005 20200421111204.0
007 cr nn 008mamaa
008 150209s2015 gw | s |||| 0|eng d
020 _a9783319134970
_9978-3-319-13497-0
024 7 _a10.1007/978-3-319-13497-0
_2doi
050 4 _aTK5105.5-5105.9
072 7 _aUKN
_2bicssc
072 7 _aCOM075000
_2bisacsh
082 0 4 _a004.6
_223
100 1 _aSrinivasa, K.G.
_eauthor.
245 1 0 _aGuide to High Performance Distributed Computing
_h[electronic resource] :
_bCase Studies with Hadoop, Scalding and Spark /
_cby K.G. Srinivasa, Anil Kumar Muppalla.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aXVII, 304 p. 43 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aComputer Communications and Networks,
_x1617-7975
505 0 _aPart I: Programming Fundamentals of High Performance Distributed Computing -- Introduction -- Getting Started with Hadoop -- Getting Started with Spark -- Programming Internals of Scalding and Spark -- Part II: Case studies using Hadoop, Scalding and Spark -- Case Study I: Data Clustering using Scalding and Spark -- Case Study II: Data Classification using Scalding and Spark -- Case Study III: Regression Analysis using Scalding and Spark -- Case Study IV: Recommender System using Scalding and Spark.
520 _aThis timely text/reference describes the development and implementation of large-scale distributed processing systems using open source tools and technologies such as Hadoop, Scalding and Spark. Comprehensive in scope, the book presents state-of-the-art material on building high performance distributed computing systems, providing practical guidance and best practices as well as describing theoretical software frameworks. Topics and features: Describes the fundamentals of building scalable software systems for large-scale data processing in the new paradigm of high performance distributed computing Presents an overview of the Hadoop ecosystem, followed by step-by-step instruction on its installation, programming and execution Reviews the basics of Spark, including resilient distributed datasets, and examines Hadoop streaming and working with Scalding Provides detailed case studies on approaches to clustering, data classification and regression analysis Explains the process of creating a working recommender system using Scalding and Spark Supplies a complete list of supplementary source code and datasets at an associated website Fulfilling the need for both introductory material for undergraduate students of computer science and detailed discussions for software engineering professionals, this book will aid a broad audience to understand the esoteric aspects of practical high performance computing through its use of solved problems, research case studies and working source code. K.G. Srinivasa is Professor and Head of the Department of Computer Science and Engineering at M.S. Ramaiah Institute of Technology (MSRIT), Bangalore, India. His other publications include the Springer title Soft Computing for Data Mining Applications. Anil Kumar Muppalla is also a researcher at MSRIT.
650 0 _aComputer science.
650 0 _aComputer communication systems.
650 0 _aComputer programming.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aImage processing.
650 1 4 _aComputer Science.
650 2 4 _aComputer Communication Networks.
650 2 4 _aProgramming Techniques.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aImage Processing and Computer Vision.
700 1 _aMuppalla, Anil Kumar.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319134963
830 0 _aComputer Communications and Networks,
_x1617-7975
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-13497-0
912 _aZDB-2-SCS
942 _cEBK
999 _c54043
_d54043