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Big Data Optimization: Recent Developments and Challenges

  • Book
  • © 2016

Overview

Editors:
  1. Ali Emrouznejad

    1. Aston Business School, Aston University, Birmingham, United Kingdom

    View editor publications

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  • 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
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (20 chapters)

  1. Front Matter

    Pages i-xv
    Download chapter PDF

  2. Big Data: Who, What and Where? Social, Cognitive and Journals Map of Big Data Publications with Focus on Optimization

    • Ali Emrouznejad, Marianna Marra
    Pages 1-16

  3. Setting Up a Big Data Project: Challenges, Opportunities, Technologies and Optimization

    • Roberto V. Zicari, Marten Rosselli, Todor Ivanov, Nikolaos Korfiatis, Karsten Tolle, Raik Niemann et al.
    Pages 17-47

  4. Optimizing Intelligent Reduction Techniques for Big Data

    • Florin Pop, Catalin Negru, Sorin N. Ciolofan, Mariana Mocanu, Valentin Cristea
    Pages 49-70

  5. Performance Tools for Big Data Optimization

    • Yan Li, Qi Guo, Guancheng Chen
    Pages 71-96

  6. Optimising Big Images

    • Tuomo Valkonen
    Pages 97-131

  7. Interlinking Big Data to Web of Data

    • Enayat Rajabi, Seyed-Mehdi-Reza Beheshti
    Pages 133-145

  8. Topology, Big Data and Optimization

    • Mikael Vejdemo-Johansson, Primoz Skraba
    Pages 147-176

  9. Applications of Big Data Analytics Tools for Data Management

    • Mo Jamshidi, Barney Tannahill, Maryam Ezell, Yunus Yetis, Halid Kaplan
    Pages 177-199

  10. Optimizing Access Policies for Big Data Repositories: Latency Variables and the Genome Commons

    • Jorge L. Contreras
    Pages 201-215

  11. Big Data Optimization via Next Generation Data Center Architecture

    • Jian Li
    Pages 217-229

  12. Big Data Optimization Within Real World Monitoring Constraints

    • Kristian Helmholt, Bram van der Waaij
    Pages 231-250

  13. Smart Sampling and Optimal Dimensionality Reduction of Big Data Using Compressed Sensing

    • Anastasios Maronidis, Elisavet Chatzilari, Spiros Nikolopoulos, Ioannis Kompatsiaris
    Pages 251-280

  14. Optimized Management of BIG Data Produced in Brain Disorder Rehabilitation

    • Peter Brezany, Olga Štěpánková, Markéta Janatová, Miroslav Uller, Marek Lenart
    Pages 281-317

  15. Big Data Optimization in Maritime Logistics

    • Berit Dangaard Brouer, Christian Vad Karsten, David Pisinger
    Pages 319-344

  16. Big Network Analytics Based on Nonconvex Optimization

    • Maoguo Gong, Qing Cai, Lijia Ma, Licheng Jiao
    Pages 345-373

  17. Large-Scale and Big Optimization Based on Hadoop

    • Yi Cao, Dengfeng Sun
    Pages 375-389

  18. Computational Approaches in Large-Scale Unconstrained Optimization

    • Saman Babaie-Kafaki
    Pages 391-417

  19. Numerical Methods for Large-Scale Nonsmooth Optimization

    • Napsu Karmitsa
    Pages 419-436

  20. Metaheuristics for Continuous Optimization of High-Dimensional Problems: State of the Art and Perspectives

    • Giuseppe A. Trunfio
    Pages 437-460
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Keywords

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.

Reviews

“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)

Editors and Affiliations

  • Aston Business School, Aston University, Birmingham, United Kingdom

    Ali Emrouznejad

Bibliographic Information

  • Book Title: Big Data Optimization: Recent Developments and Challenges

  • Editors: Ali Emrouznejad

  • Series Title: Studies in Big Data

  • DOI: https://doi.org/10.1007/978-3-319-30265-2

  • Publisher: Springer Cham

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer International Publishing Switzerland 2016

  • Hardcover ISBN: 978-3-319-30263-8Published: 07 June 2016

  • Softcover ISBN: 978-3-319-80765-2Published: 30 May 2018

  • eBook ISBN: 978-3-319-30265-2Published: 26 May 2016

  • 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

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