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.
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.
In the spatial or spatio-temporal context, specifying the correct covariance function is fundamental to obtain efficient predictions, and to understand the underlying physical process of interest...
Applied sciences, both physical and social, such as atmospheric, biological, climate, demographic, economic, ecological, environmental, oceanic and political, routinely gather large volumes of...
This book proposes a number of promising models and methods for adaptive segmentation, swarm partition, permissible segmentation, and transform properties, as well as techniques for spatio-temporal...
Spatio-Temporal Databases explores recent trends in flexible querying and reasoning about time- and space-related information in databases. It shows how flexible querying enhances ...
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