© 2016

Big Data Optimization: Recent Developments and Challenges

  • Ali Emrouznejad
  • Presents recent developments and challenges in big data optimization

  • Collects various recent algorithms in large-scale optimization all in one book

  • Presents useful big data optimization applications in a variety of industries, both for academics and practitioners

  • Include some guideline to use cloud computing and Hadoop in large-scale and big data optimization


Part of the Studies in Big Data book series (SBD, volume 18)

Table of contents

  1. Front Matter
    Pages i-xv
  2. Roberto V. Zicari, Marten Rosselli, Todor Ivanov, Nikolaos Korfiatis, Karsten Tolle, Raik Niemann et al.
    Pages 17-47
  3. Florin Pop, Catalin Negru, Sorin N. Ciolofan, Mariana Mocanu, Valentin Cristea
    Pages 49-70
  4. Yan Li, Qi Guo, Guancheng Chen
    Pages 71-96
  5. Tuomo Valkonen
    Pages 97-131
  6. Enayat Rajabi, Seyed-Mehdi-Reza Beheshti
    Pages 133-145
  7. Mikael Vejdemo-Johansson, Primoz Skraba
    Pages 147-176
  8. Mo Jamshidi, Barney Tannahill, Maryam Ezell, Yunus Yetis, Halid Kaplan
    Pages 177-199
  9. Kristian Helmholt, Bram van der Waaij
    Pages 231-250
  10. Anastasios Maronidis, Elisavet Chatzilari, Spiros Nikolopoulos, Ioannis Kompatsiaris
    Pages 251-280
  11. Peter Brezany, Olga Štěpánková, Markéta Janatová, Miroslav Uller, Marek Lenart
    Pages 281-317
  12. Berit Dangaard Brouer, Christian Vad Karsten, David Pisinger
    Pages 319-344
  13. Maoguo Gong, Qing Cai, Lijia Ma, Licheng Jiao
    Pages 345-373
  14. Yi Cao, Dengfeng Sun
    Pages 375-389

About this book


The main objective of this book is to provide the necessary background to work with big data by introducing some novel optimization algorithms and codes capable of working in the big data setting as well as introducing some applications in big data optimization for both academics and practitioners interested, and to benefit society, industry, academia, and government. Presenting applications in a variety of industries, this book will be useful for the researchers aiming to analyses large scale data. Several optimization algorithms for big data including convergent parallel algorithms, limited memory bundle algorithm, diagonal bundle method, convergent parallel algorithms, network analytics, and many more have been explored in this book.


Big Data Big Data Algorithms Big Data Optimization Business Analytics Optimization Big Data Analytics

Editors and affiliations

  • Ali Emrouznejad
    • 1
  1. 1.Aston Business SchoolAston UniversityBirminghamUnited Kingdom

Bibliographic information

  • Book Title Big Data Optimization: Recent Developments and Challenges
  • Editors Ali Emrouznejad
  • Series Title Studies in Big Data
  • Series Abbreviated Title Studies in Big Data
  • DOI
  • Copyright Information Springer International Publishing Switzerland 2016
  • Publisher Name Springer, Cham
  • eBook Packages Engineering Engineering (R0)
  • Hardcover ISBN 978-3-319-30263-8
  • Softcover ISBN 978-3-319-80765-2
  • eBook ISBN 978-3-319-30265-2
  • Series ISSN 2197-6503
  • Series E-ISSN 2197-6511
  • Edition Number 1
  • Number of Pages XV, 487
  • Number of Illustrations 22 b/w illustrations, 160 illustrations in colour
  • Topics Computational Intelligence
    Artificial Intelligence
    Operations Research/Decision Theory
  • Buy this book on publisher's site
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“It can be used as a reference book on big data, to obtain a broad view of the direction and landscape. In addition, it can be used by specialists in specific areas of big data, especially optimization-related areas. In this respect, the preview of chapter titles and brief explanations provided in this review reveal specific areas of interest for the intended specialists. I like this edited volume and recommend it.” (M. M. Tanik, Computing Reviews, January, 2017)