Spatiotemporal Big Data Systems
Springer Nature Switzerland AG (Verlag)
978-981-96-8805-0 (ISBN)
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In the core technology section, particular emphasis is placed on the pivotal role of GIS (Geographic Information Systems) technology in the processing of spatiotemporal big data, comprehensively covering the entire process from data collection, pre-processing, in-depth analysis to final visualization. Through carefully selected real-world cases and detailed technical explanations, readers will gain proficiency in leveraging GIS technology to uncover the latent value of spatiotemporal big data.
The book also delves into the essential techniques and algorithms required for building efficient spatiotemporal big data systems, such as efficient data storage and management, intelligent data mining and analysis, alongside specific application cases under the smart city framework, including advanced practices in urban planning optimization, traffic management innovation, and environmental monitoring upgrades. Rich in content and blending theoretical depth with practical guidance, it serves as a valuable resource and reference guide for both academic experts and practitioners in the field, as well as a quality read for those intrigued by spatiotemporal big data systems.
The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.
Yuan Sui, graduated from Beijing Normal University, is currently the head of the Spatio-Temporal Data Team and a senior architect at JD Technology Group. He is a committee member of CCF’s Database, Big Data, and Distributed Computing Special Interest Groups. Sui has 15 years of research and work experience in spatiotemporal data, covering areas such as GIS, quantitative remote sensing, and smart cities. He led the team in developing JD City’s Spatio-Temporal Data Engine JUST. Under his leadership, the team has published over 20 papers in top international journals, applied for more than 60 patents, and successfully served over 20 major smart city projects involving urban governance, monitoring and early warning, smart parks, intelligent public security, and government-citizen interaction. Zisheng Yu, graduated from Xidian University, is a Corresponding Executive Committee member of CCF’s Database Professional Committee. His research focus is on urban computing and spatiotemporal data management and analysis. He is the author of the book "GeoMesa Spatiotemporal Data Management". Rubin Wang, holding a Master’s degree in Computer Science and Technology from Southwest Jiaotong University, primarily researches urban computing and spatiotemporal data management.
Chapter 1: The Emergence of Spatiotemporal Big Data Systems.- Chapter 2: Basic Knowledge of Spatiotemporal Data.- Chapter 3: Data Perception and Access.- Chapter 4: Data Storage and Indexing.- Chapter 5: Spatiotemporal Big Data Analysis and Mining.- Chapter 6: Data Services and Sharing.- Chapter 7: Data Visualization.- Chapter 8: Application of Spatiotemporal Data in Online Shopping.- Chapter 9: Application of Spatiotemporal Data in Logistics Services.- Chapter 10: Hazardous Materials Vehicle Supervision.
| Erscheinungsdatum | 13.06.2025 |
|---|---|
| Reihe/Serie | Big Data Management |
| Zusatzinfo | 244 Illustrations, black and white |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Mathematik / Informatik ► Informatik ► Datenbanken |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| Naturwissenschaften ► Geowissenschaften ► Geografie / Kartografie | |
| Schlagworte | data mining techniques • Data Science • Environmental monitoring • Geographic Information Systems (GIS) • High-Performance Computing • multidimensional data analysis • Smart City Technologies • Spatial Data Visualization • Spatiotemporal Big Data • Urban Planning Applications |
| ISBN-10 | 981-96-8805-1 / 9819688051 |
| ISBN-13 | 978-981-96-8805-0 / 9789819688050 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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