Big Data with Hadoop Mapreduce: A Classroom Approach

Jeyaraj, Rathinaraja, Pugalendhi, Ganeshkumar, Paul, Anand

  • 出版商: Apple Academic Press
  • 出版日期: 2022-03-09
  • 售價: $2,060
  • 貴賓價: 9.5$1,957
  • 語言: 英文
  • 頁數: 406
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1774634848
  • ISBN-13: 9781774634844
  • 相關分類: Hadoop分散式架構大數據 Big-data
  • 海外代購書籍(需單獨結帳)

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作者簡介

Rathinaraja Jeyaraj is a Research Scholar in the Department of Information Technology at the National Institute of Technology Karnataka, India. He recently worked as a visiting researcher at Connected Computing and Media Processing Lab, Kyungpook National University, South Korea. His research interests include big data processing tools, cloud computing, IoT, and machine learning.

Ganeshkumar Pugalendhi, PhD, is an Assistant Professor in the Department of Information Technology, Anna University Regional Campus, Coimbatore, India. He is the resource person for delivering technical talks and seminars sponsored by various organizations, including the University Grants Commission of India, All India Council for Technical Education, Technical Education Quality Improvement Programme of Government of India, Indian Council of Medical Research, and many others. He has written two research-oriented textbooks: Data Classification Using Soft Computing and Soft Computing for Microarray Data Analysis.

Anand Paul, PhD, is an Associate Professor at the School of Computer Science and Engineering at Kyungpook National University, South Korea. He was a delegate representing South Korea for the M2M focus group in 2010-2012 and is serving as associate editor for the journals IEEE Access, IET Wireless Sensor Systems, ACM Applied Computing Reviews, Cyber Physical Systems, Human Behaviour and Emerging Technology, and the Journal of Platform Technology. He is the track chair for smart human computer interaction with the Association for Computing Machinery Symposium on Applied Computing 2014-2019, and general chair for the 8th International Conference on Orange Technology (ICOT 2020).

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