© 2015

Sublinear Algorithms for Big Data Applications


Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Dan Wang, Zhu Han
    Pages 1-7
  3. Dan Wang, Zhu Han
    Pages 9-21
  4. Dan Wang, Zhu Han
    Pages 23-46
  5. Dan Wang, Zhu Han
    Pages 47-67
  6. Dan Wang, Zhu Han
    Pages 69-82
  7. Dan Wang, Zhu Han
    Pages 83-85

About this book


The brief focuses on applying sublinear algorithms to manage critical big data challenges. The text offers an essential introduction to sublinear algorithms, explaining why they are vital to large scale data systems. It also demonstrates how to apply sublinear algorithms to three familiar big data applications: wireless sensor networks, big data processing in Map Reduce and smart grids. These applications present common experiences, bridging the theoretical advances of sublinear algorithms and the application domain. Sublinear Algorithms for Big Data Applications is suitable for researchers, engineers and graduate students in the computer science, communications and signal processing communities.


Big data applications Big data processing Smart grids Sublinear algorithms Wireless sensor networks

Authors and affiliations

  1. 1.Department of ComputingThe Hong Kong Polytechnic UniversityKowloonHong Kong SAR
  2. 2.Department of EngineeringUniversity of HoustonHoustonUSA

Bibliographic information

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“Wang and Han’s book focuses on sublinear algorithms for processing big data. … For the researcher, this book also shows that there is room for improvement and new discoveries in this flourishing area. … The book is thus recommended mainly to researchers, but just as a piece of the bigger puzzle of sublinear algorithms for big data processing and applications.” (Corrado Mencar, Computing Reviews,, August, 2016)