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Spatio-Temporal Graph Data Analytics
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This book highlights some of the unique aspects of spatiotemporal 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. The next section of this book the authors go into depths of algorithmic development issues in s patio-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 spatiotemporal graph datasets, such as engine measurement data, and discuss some open research problems in the area. This b ook 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 researches and practitioners in the field of navigational a lgorithms.
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