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Spatio-Temporal Graph Data Analytics

Benefits

  • Describes a unique overarching model which can support a wide variety of spatio-temporal graph data

  • Covers A* and bi-directional search for determining fastest paths over spatio-temporal graphs

  • Introduces spatio-temporal graph datasets, such as engine measurement data

  • Applications from the research covered in this book (navigational algorithms), can be used for Uber service and Google's autonomous cars

Book

Table of contents

  1. Front Matter
    Pages i-x
  2. Venkata M. V. Gunturi, Shashi Shekhar
    Pages 1-4
  3. Venkata M. V. Gunturi, Shashi Shekhar
    Pages 5-11
  4. Venkata M. V. Gunturi, Shashi Shekhar
    Pages 13-23
  5. Venkata M. V. Gunturi, Shashi Shekhar
    Pages 25-41
  6. Venkata M. V. Gunturi, Shashi Shekhar
    Pages 43-57
  7. Venkata M. V. Gunturi, Shashi Shekhar
    Pages 59-75
  8. Venkata M. V. Gunturi, Shashi Shekhar
    Pages 77-91
  9. Venkata M. V. Gunturi, Shashi Shekhar
    Pages 93-96
  10. Back Matter
    Pages 97-100

About this book

Introduction

This book highlights some of the unique aspects of spatio-temporal graph data from the perspectives of modeling and developing scalable algorithms. The authors discuss in the first part of this book, the semantic aspects of spatio-temporal graph data in two application domains, viz., urban transportation and social networks. Then the authors present representational models and data structures, which can effectively capture these semantics, while  ensuring support for computationally scalable algorithms.

In the first part of the book, the authors describe algorithmic development issues in spatio-temporal graph data. These algorithms internally use the semantically rich data structures developed in the earlier part of this book. Finally, the authors introduce some upcoming spatio-temporal graph datasets, such as engine measurement data, and discuss some open research problems in the area. 

This book will be useful as a secondary text for advanced-level students entering into relevant fields of computer science, such as transportation and urban planning. It may also be useful for  researchers and practitioners in the field of navigational algorithms.

Keywords

spatio-temporal networks spatial databases geographic information science graph algorithms transportation networks spatial networks urban transportation road networks road navigation time-varying graphs shortest path algorithms dynamic social networks

Authors and affiliations

  1. 1.Dept of Computer Science and EngineeringIndian Institute of Technology — RoparRupnagarIndia
  2. 2.Dept of Computer Science and EngineeringUniversity of MinnesotaMinneapolisUSA

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