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Spatiotemporal data analytics and modeling = techniques and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Spatiotemporal data analytics and modeling/ edited by John A ... [et al.].
其他題名:	techniques and applications /
其他作者:	A., John.
出版者:	Singapore :Springer Nature Singapore : : 2024.,
面頁冊數:	xii, 245 p. :ill. (some col.), digital ;24 cm.
內容註:	PART I. Spatiotemporal Data Management Techniques. - Chapter 1. Introduction to Spatiotemporal Data -- Chapter 2. Recommendation System using Spatial-Temporal Network for Vehicle Demand Prediction -- Chapter 3. Spatial-based Big Data and Large-Scale Network Management -- Chapter 4. Handling Uncertainty in Spatiotemporal Data -- Chapter 5. Multimodal Spatial-Temporal Prediction and Classification using Deep Learning -- Chapter 6. Spatiotemporal Object Detection and Activity Recognition -- PART II. Applications of Spatiotemporal Data Analytics -- Chapter 7. Spatiotemporal Data Analytics for e-waste Management System using Hybrid Deep Belief Networks -- Chapter 8. Spatiotemporal and Intelligent Transportation Forecasting -- Chapter 9. Spatiotemporal Supply Chains and E-Commerce -- Chapter 10. Spatiotemporal Renewable Energy Techniques and Applications -- Chapter 11. Environmental Spatiotemporal Data Analytics -- Chapter 12. Future and ResearchPerspectives of Spatiotemporal Data Analytics and Modelling.
Contained By:	Springer Nature eBook
標題:	Geomatics. -
電子資源:	https://doi.org/10.1007/978-981-99-9651-3
ISBN:	9789819996513

Spatiotemporal data analytics and modeling = techniques and applications /
Spatiotemporal data analytics and modelingtechniques and applications /[electronic resource] :edited by John A ... [et al.]. - Singapore :Springer Nature Singapore :2024. - xii, 245 p. :ill. (some col.), digital ;24 cm. - Big data management,2522-0187. - Big data management..

PART I. Spatiotemporal Data Management Techniques. - Chapter 1. Introduction to Spatiotemporal Data -- Chapter 2. Recommendation System using Spatial-Temporal Network for Vehicle Demand Prediction -- Chapter 3. Spatial-based Big Data and Large-Scale Network Management -- Chapter 4. Handling Uncertainty in Spatiotemporal Data -- Chapter 5. Multimodal Spatial-Temporal Prediction and Classification using Deep Learning -- Chapter 6. Spatiotemporal Object Detection and Activity Recognition -- PART II. Applications of Spatiotemporal Data Analytics -- Chapter 7. Spatiotemporal Data Analytics for e-waste Management System using Hybrid Deep Belief Networks -- Chapter 8. Spatiotemporal and Intelligent Transportation Forecasting -- Chapter 9. Spatiotemporal Supply Chains and E-Commerce -- Chapter 10. Spatiotemporal Renewable Energy Techniques and Applications -- Chapter 11. Environmental Spatiotemporal Data Analytics -- Chapter 12. Future and ResearchPerspectives of Spatiotemporal Data Analytics and Modelling.

With the growing advances in technology and transformation to digital services, the world is becoming more connected and more complex. Huge heterogeneous data are generated at rapid speed from various types of sensors. Augmented with artificial intelligence and machine learning and internet of things, latent relations, and new insights can be captured helping in optimizing plans and resource utilization, improving infrastructure, and enhancing quality of services. A "spatial data management system" is a way to take care of data that has something to do with space. This could include data such as maps, satellite images, and GPS data. A temporal data management system is a system designed to manage data that has a temporal component. This could include data such as weather data, financial data, and social media data. Some advanced techniques used in spatial and temporal data management systems include geospatial indexing for efficient querying and retrieval of location-based data, time-series analysis for understanding and predicting temporal patterns in datasets like weather or financial trends, machine learning algorithms for uncovering hidden patterns and correlations in large and complex datasets, and integration with Internet of Things (IoT) technologies for real-time data collection and analysis. These techniques, augmented with artificial intelligence, enable the extraction of latent relations and insights, thereby optimizing plans, improving infrastructure, and enhancing the quality of services. This book provides essential technical knowledge, best practices, and case studies on the state-of-the-art techniques of artificial intelligence and machine learning for spatiotemporal data analysis and modeling. The book is composed of several chapters written by experts in their fields and focusing on several applications including recommendation systems, big data analytics, supply chains and e-commerce, energy consumption and demand forecasting, and traffic and environmental monitoring. It can be used as academic reference at graduate level or by professionals in science and engineering related fields such as data science and engineering, big data analytics and mining, artificial intelligence, machine learning and deep learning, cloud computing, and internet of things.

ISBN: 9789819996513

Standard No.: 10.1007/978-981-99-9651-3doiSubjects--Topical Terms:

596804
Geomatics.

LC Class. No.: QA76.9.D343 / S63 2024

Dewey Class. No.: 005.7

Spatiotemporal data analytics and modeling = techniques and applications /
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520 $a With the growing advances in technology and transformation to digital services, the world is becoming more connected and more complex. Huge heterogeneous data are generated at rapid speed from various types of sensors. Augmented with artificial intelligence and machine learning and internet of things, latent relations, and new insights can be captured helping in optimizing plans and resource utilization, improving infrastructure, and enhancing quality of services. A "spatial data management system" is a way to take care of data that has something to do with space. This could include data such as maps, satellite images, and GPS data. A temporal data management system is a system designed to manage data that has a temporal component. This could include data such as weather data, financial data, and social media data. Some advanced techniques used in spatial and temporal data management systems include geospatial indexing for efficient querying and retrieval of location-based data, time-series analysis for understanding and predicting temporal patterns in datasets like weather or financial trends, machine learning algorithms for uncovering hidden patterns and correlations in large and complex datasets, and integration with Internet of Things (IoT) technologies for real-time data collection and analysis. These techniques, augmented with artificial intelligence, enable the extraction of latent relations and insights, thereby optimizing plans, improving infrastructure, and enhancing the quality of services. This book provides essential technical knowledge, best practices, and case studies on the state-of-the-art techniques of artificial intelligence and machine learning for spatiotemporal data analysis and modeling. The book is composed of several chapters written by experts in their fields and focusing on several applications including recommendation systems, big data analytics, supply chains and e-commerce, energy consumption and demand forecasting, and traffic and environmental monitoring. It can be used as academic reference at graduate level or by professionals in science and engineering related fields such as data science and engineering, big data analytics and mining, artificial intelligence, machine learning and deep learning, cloud computing, and internet of things.
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