Spatio-Temporal Graph Data Analytics

Nonfiction, Science & Nature, Technology, Power Resources, Computers, Database Management, General Computing
Cover of the book Spatio-Temporal Graph Data Analytics by Venkata M. V. Gunturi, Shashi Shekhar, Springer International Publishing
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Author: Venkata M. V. Gunturi, Shashi Shekhar ISBN: 9783319677712
Publisher: Springer International Publishing Publication: December 15, 2017
Imprint: Springer Language: English
Author: Venkata M. V. Gunturi, Shashi Shekhar
ISBN: 9783319677712
Publisher: Springer International Publishing
Publication: December 15, 2017
Imprint: Springer
Language: English

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.

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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.

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