Spatiotemporal Big Data Systems : Concepts, Principles and Applications
Book Details
Format
Hardback or Cased Book
Book Series
Big Data Management
ISBN-10
9819688051
ISBN-13
9789819688050
Publisher
Springer Nature Switzerland AG
Imprint
Springer Nature Switzerland AG
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Jul 21st, 2026
Print length
299 Pages
Product Classification:
Social research & statisticsSocial research and statisticsGeographical information systems (GIS) & remote sensingGeographical information systems, geodata and remote sensingDigital and Information technology: general topicsInformation technology: general issuesDatabasesDatabases / Data managementMathematical theory of computationImage processing
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This book demystifies the core principles and architectural design of spatiotemporal big data systems, offering incisive analysis of the unique attributes of spatiotemporal data and its broad applicability in real-world scenarios, aiming to impart a profound understanding of the intrinsic value of spatiotemporal big data and its far-reaching impact across various fields. 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.
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