MARC 主機 00000nam a2200541 i 4500 
001    978-3-319-13497-0 
003    DE-He213 
005    20150915141211.0 
006    m     o  d         
007    cr nn 008maaau 
008    150209s2015    gw      s         0 eng d 
020    9783319134970 (electronic bk.) 
020    9783319134963 (paper) 
024 7  10.1007/978-3-319-13497-0|2doi 
040    GP|cGP|erda|dAS 
041 0  eng 
050  4 QA76.88 
082 04 004.11|223 
100 1  Srinivasa, K.G.,|eauthor 
245 10 Guide to high performance distributed computing :|bcase 
       studies with Hadoop, Scalding and Spark /|cby K.G. 
       Srinivasa, Anil Kumar Muppalla 
264  1 Cham :|bSpringer International Publishing :|bImprint: 
300    1 online resource (xvii, 304 pages) :|billustrations, 
       digital ;|c24 cm 
336    text|btxt|2rdacontent 
337    computer|bc|2rdamedia 
338    online resource|bcr|2rdacarrier 
347    text file|bPDF|2rda 
490 1  Computer communications and networks,|x1617-7975 
505 0  Part 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    This 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 
590    Springer 
630 00 Apache Hadoop 
630 00 SPARK (Electronic resource) 
650  0 High performance computing|vCase studies 
650  0 Electronic data processing|xDistributed processing|vCase 
650 14 Computer Science 
650 24 Computer Communication Networks 
650 24 Programming Techniques 
650 24 Data Mining and Knowledge Discovery 
650 24 Artificial Intelligence (incl. Robotics) 
650 24 Image Processing and Computer Vision 
700 1  Muppalla, Anil Kumar,|eauthor 
710 2  SpringerLink (Online service) 
773 0  |tSpringer eBooks 
830  0 Computer communications and networks 
856 40 |u