Handbook of Big Data Technologies

  • Albert Y. Zomaya
  • Sherif Sakr

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Fundamentals of Big Data Processing

    1. Front Matter
      Pages 1-1
    2. Dongyao Wu, Sherif Sakr, Liming Zhu
      Pages 3-29
    3. Dongyao Wu, Sherif Sakr, Liming Zhu
      Pages 31-63
    4. Jiannong Cao, Shailey Chawla, Yuqi Wang, Hanqing Wu
      Pages 65-99
    5. Loris Belcastro, Fabrizio Marozzo, Domenico Talia, Paolo Trunfio
      Pages 101-142
    6. Mohamed Y. Eltabakh
      Pages 143-178
    7. Mohamed A. Soliman
      Pages 179-217
    8. Paris Carbone, Gábor E. Gévay, Gábor Hermann, Asterios Katsifodimos, Juan Soto, Volker Markl et al.
      Pages 219-260
  3. Semantic Big Data Management

    1. Front Matter
      Pages 261-261
    2. Michelle Cheatham, Catia Pesquita
      Pages 263-305
    3. Manfred Hauswirth, Marcin Wylot, Martin Grund, Paul Groth, Philippe Cudré-Mauroux
      Pages 307-338
    4. Manfred Hauwirth, Marcin Wylot, Martin Grund, Sherif Sakr, Phillippe Cudré-Mauroux
      Pages 339-364
    5. Julian Eberius, Maik Thiele, Wolfgang Lehner
      Pages 365-407
    6. Yongrui Qin, Quan Z. Sheng
      Pages 409-427
  4. Big Graph Analytics

    1. Front Matter
      Pages 455-455
    2. Martin Junghanns, André Petermann, Martin Neumann, Erhard Rahm
      Pages 457-505
    3. Arijit Khan, Sayan Ranu
      Pages 531-582
    4. Ana Paula Appel, Luis G. Moyano
      Pages 583-616
    5. Sankar K. Pal, Suman Kundu
      Pages 617-651
  5. Big Data Applications

    1. Front Matter
      Pages 653-653
    2. Beniamino di Martino, Giuseppina Cretella, Antonio Esposito
      Pages 655-690
    3. Philip Church, Harald Mueller, Caspar Ryan, Spyridon V. Gogouvitis, Andrzej Goscinski, Houssam Haitof et al.
      Pages 691-718
    4. Xiang Shi, Peng Zhang, Samee U. Khan
      Pages 719-753
    5. K. Ashwin Kumar
      Pages 755-776
    6. H. Anzt, J. Dongarra, M. Gates, J. Kurzak, P. Luszczek, S. Tomov et al.
      Pages 777-806
    7. Ana Paula Appel, Heloisa Candello, Fábio Latuf Gandour
      Pages 807-850
    8. Dinusha Vatsalan, Ziad Sehili, Peter Christen, Erhard Rahm
      Pages 851-895

About this book


This handbook offers comprehensive coverage of recent advancements in Big Data technologies and related paradigms.  Chapters are authored by international leading experts in the field, and have been reviewed and revised for maximum reader value. The volume consists of twenty-five chapters organized into four main parts. Part one covers the fundamental concepts of Big Data technologies including data curation mechanisms, data models, storage models, programming models and programming platforms. It also dives into the details of implementing Big SQL query engines and big stream processing systems.  Part Two focuses on the semantic aspects of Big Data management including data integration and exploratory ad hoc analysis in addition to structured querying and pattern matching techniques.  Part Three presents a comprehensive overview of large scale graph processing. It covers the most recent research in large scale graph processing platforms, introducing several scalable graph querying and mining mechanisms in domains such as social networks.  Part Four details novel applications that have been made possible by the rapid emergence of Big Data technologies such as Internet-of-Things (IOT), Cognitive Computing and SCADA Systems.  All parts of the book discuss open research problems, including potential opportunities, that have arisen from the rapid progress of Big Data technologies and the associated increasing requirements of application domains. 

Designed for researchers, IT professionals and graduate students, this book is a timely contribution to the growing Big Data field. Big Data has been recognized as one of leading emerging technologies that will have a major contribution and impact on the various fields of science and varies aspect of the human society over the coming decades. Therefore, the content in this book will be an essential tool to help readers understand the development and future of the field.


Big Data MapReduce Hadoop Spark Big graph analytics Data analytics Big SQL Big Data applications Giraph Flink Big Data storage Big Data programming models Big Data query engines Big Data integration

Editors and affiliations

  • Albert Y. Zomaya
    • 1
  • Sherif Sakr
    • 2
  1. 1.School of Information TechnologiesThe University of SydneySydneyAustralia
  2. 2.The School of Computer ScienceThe University of New South WalesEveleighAustralia

Bibliographic information

  • DOI
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-49339-8
  • Online ISBN 978-3-319-49340-4
  • Buy this book on publisher's site
Industry Sectors
IT & Software