Editors:
- Describes the state-of-the-art modeling and storage methods for large data sets and discusses current challenges for the development of next generation data technologies
- Includes benchmarking, applications and case studies
- Edited and written by leading experts in the field
Part of the book series: Modeling and Optimization in Science and Technologies (MOST, volume 4)
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Table of contents (19 chapters)
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Front Matter
About this book
This book covers the latest advances in Big Data technologies and provides the readers with a comprehensive review of the state-of-the-art in Big Data processing, analysis, analytics, and other related topics. It presents new models, algorithms, software solutions and methodologies, covering the full data cycle, from data gathering to their visualization and interaction, and includes a set of case studies and best practices. New research issues, challenges and opportunities shaping the future agenda in the field of Big Data are also identified and presented throughout the book, which is intended for researchers, scholars, advanced students, software developers and practitioners working at the forefront in their field.
Editors and Affiliations
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Universitat Politècnica de Catalunya, Barcelona, Spain
Fatos Xhafa
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Fukuoka Institute of Technology (FIT), Fukuoka, Japan
Leonard Barolli
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University of Salerno, Salerno, Italy
Admir Barolli
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Canadian Institute of Technology, Tirana, Albania
Petraq Papajorgji
Bibliographic Information
Book Title: Modeling and Processing for Next-Generation Big-Data Technologies
Book Subtitle: With Applications and Case Studies
Editors: Fatos Xhafa, Leonard Barolli, Admir Barolli, Petraq Papajorgji
Series Title: Modeling and Optimization in Science and Technologies
DOI: https://doi.org/10.1007/978-3-319-09177-8
Publisher: Springer Cham
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer International Publishing Switzerland 2015
Hardcover ISBN: 978-3-319-09176-1Published: 13 November 2014
Softcover ISBN: 978-3-319-38500-6Published: 10 September 2016
eBook ISBN: 978-3-319-09177-8Published: 04 November 2014
Series ISSN: 2196-7326
Series E-ISSN: 2196-7334
Edition Number: 1
Number of Pages: XX, 516
Number of Illustrations: 167 b/w illustrations, 52 illustrations in colour
Topics: Computational Intelligence, Data Mining and Knowledge Discovery, Communications Engineering, Networks