Spatiotemporal Data Analytics and Modeling : Techniques and Applications / edited by John A, Satheesh Abimannan, El-Sayed M. El-Alfy, Yue-Shan Chang.

Format:

Book

Contributor:

A., John, editor.

Series:

Big Data Management, 2522-0187

Language:

English

Subjects (All):

Data mining.

Quantitative research.

Big data.

Engineering--Data processing.

Engineering.

Data Mining and Knowledge Discovery.

Data Analysis and Big Data.

Big Data.

Data Engineering.

Local Subjects:

Data Mining and Knowledge Discovery.

Data Analysis and Big Data.

Big Data.

Data Engineering.

Physical Description:

1 online resource (253 pages)

Edition:

1st ed. 2024.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2024.

Summary:

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. .

Contents:

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. .

Notes:

Includes bibliographical references.

ISBN:

981-9996-51-1