Big Data Processing With Hadoop

Revathi, T.|Muneeswaran, K.|Blessa Binolin Pepsi, M.

ISBN 10: 1522586954 / ISBN 13: 9781522586951

Verlag: Engineering Science Reference, 2018

Beschreibung

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Discusses possible solutions for millions of users working with a variety of data applications, who expect fast turnaround responses, but encounter issues with processing data at the rate it comes in. The book features research on a range of topics, includi.

Bestandsnummer des Verkäufers 448011405

Bibliografische Details

• Titel: Big Data Processing With Hadoop
• Verlag: Engineering Science Reference
• Erscheinungsjahr: 2018
• Sprache: Englisch
• ISBN-10: 1522586954
• ISBN-13: 9781522586951
• Einband: Softcover
• Zustand: New

Über diesen Titel

Inhaltsangabe

Due to the increasing availability of affordable internet services, the number of users, and the need for a wider range of multimedia-based applications, internet usage is on the rise. With so many users and such a large amount of data, the requirements of analyzing large data sets leads to the need for further advancements to information processing.

Big Data Processing With Hadoop is an essential reference source that discusses possible solutions for millions of users working with a variety of data applications, who expect fast turnaround responses, but encounter issues with processing data at the rate it comes in. Featuring research on topics such as market basket analytics, scheduler load simulator, and writing YARN applications, this book is ideally designed for IoT professionals, students, and engineers seeking coverage on many of the real-world challenges regarding big data.

Über die Autorinnen und Autoren

T. Revathi has completed her B.E. Degree in Electrical & Electronics Engineering from Madurai Kamaraj University, Madurai, India in the year 1986. Immediately she started her career as Lecturer in Mepco Schlenk Engineering College, Sivakasi. She completed her Master degree in Computer Science and Engineering from Bharathiar University, Coimbatore in the year 1995. Her research work is in the field of Computer Networks and she obtained Ph.D. in the year 2008 from Manonmainam Sundaranar University, Tirunelveli. Currently she is working as Senior Professor & Head in Information Technology Department of Mepco Schlenk Engineering College, Sivakasi. She has more than 32 years of teaching experience. She has published 83 papers in various International & National journals & conferences. Her research interests include Big data analytics, Streaming analytics, and Sensor networks.

Muneeswaran Karuppiah received the Bachelor of Engineering degree in Electronics and Communication Engineering from Madurai Kamarajar University, Tamilnadu, India in 1984 and the Master of Engineering in Computer Science and Engineering from Bharathiyar University, Tamilnadu, India, in 1990. In 2006, he received the Ph.D. degree in Computer Science and Engineering from M.S. University, Tamilnadu, India. He is in teaching and research for the past 34 years and 17 years respectively and currently, he is working as Senior Professor in Computer Science and Engineering Department at Mepco Schlenk Engineering College, Tamilnadu. His research interests are image analysis, machine learning techniques, and Data Analytics. He has authored or co-authored about 125 publications in journal/conference level and one book on Compiler Design with Oxford University.

M. Blessa Binolin Pepsi , M.E. (CS), B.tech (IT), Senior Assistant Professor, currently working in the department of Information Technology, Mepco Schlenk Engineering College, Sivakasi has work experience of 5 years since 2013. Authoress had published 8 papers in International Journals and 7 papers in various conferences. Authoress is the recipient of Venus International Award for Young Women in Engineering. Authoress’s research interest includes big data and machine learning. Had guided nearly 15 UG and PG scholars in this research area to develop projects connected with recent era.