Spatiotemporal Data Analytics and Modeling
Description
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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Persons
Dr. John A is currently working as a postdoctoral research fellow at AI and Sustainable Development Research Lab, National Taiwan University, Taipei, Taiwan. Pondicherry University awarded him an undergraduate degree in Computer Science and Engineering Discipline. He earned a postgraduate degree (MTech. in Computer Science and Engineering at Pondicherry University, India). In 2019, he completed his PhD in Computer Science and Engineering at Manonmaniam Sundaranar University, India. His research areas of interest are real-time applications, machine learning, data analysis and prediction, and spatial and temporal data management.
Satheesh Abimannan is currently a professor and deputy director in Amity School Engineering and Technology at Amity University, Mumbai. He served as a postdoctoral research fellow at National Taipei University, Taiwan, for one year. He received the ME degree in Computer Science and Engineering from the College of Engineering, Guindy, Anna University, Chennai, and a PhD degree in Computer Science and Engineering from the Periyar Maniammai University. He has more than 20 years of teaching, research, and administrative experience. He received an ISTE-Young Scientist Award in 2010. He has published more than 40 research articles in highly reputed international journals and visited Singapore, China, Taiwan, and Japan to present his research article at international conferences. His research interest includes deep learning, cloud computing, big data analytics, and information security.
El-Sayed M. El-Alfy (Senior Member, IEEE) is currently a professor with the Information and Computer Science Department, fellow of the SDAIA-KFUPM Joint Research Center for Artificial Intelligence, affiliate of Interdisciplinary Research Center on Intelligent Secure Systems, King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. He has over 25 years of experience in industry and academia, involving research, teaching, supervision, curriculum design, program assessment, and quality assurance in higher education. He is an approved ABET/CSAB program evaluator (PEV), and a reviewer and consultant for NCAAA and several universities and research agents in various countries. He is an active researcher with interests in fields related to machine learning, computer vision, nature-inspired computing and applications to data science and cybersecurity analytics, pattern recognition, multimedia forensics, and security systems. He has published numerously in peer-reviewed international journals and conferences, edited a number of books published by reputable international publishers, attended and contributed in the organization of many world-class international conferences, and supervised master and PhD students. He was also a member of ACM, the IEEE Computational Intelligence Society, the IEEE Computer Society, the IEEE Communication Society, and the IEEE Vehicular Technology Society. His work has been internationally recognized and received a number of awards. He has served as a guest editor for a number of special issues in international journals and has been on the editorial board of a number of premium international journals, including IEEE/CAA Journal of Automatica Sinica , IEEE Transactions on Neural Networks and Learning Systems , International Journal of Trust Management in Computing and Communications , and Journal of Emerging Technologies in Web Intelligence (JETWI).
Yue-Shan Chang (Senior Member, IEEE) received the PhD degree from the Department of Computer and Information Science, National Chiao Tung University, in 2001. In August 1992, he joined the Department of Electronic Engineering, Ming Hsing University of Science and Technology. In August 2004, he joined the Department of Computer Science and Information Engineering, National Taipei University, Taipei, Taiwan. In August 2010, he became a professor. He has been serving as the chairman of the Department, since 2014, and the dean of Student Affairs, since 2018. His research interests include information and knowledge fusion, big data analytics, cloud computing, intelligent computing, and the Internet of Things.
Content
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.