Spatiotemporal Data Analytics and Modeling (Record no. 87851)
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| fixed length control field | 05775nam a22006255i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-981-99-9651-3 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730171842.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240415s2024 si | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9789819996513 |
| -- | 978-981-99-9651-3 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.312 |
| 245 10 - TITLE STATEMENT | |
| Title | Spatiotemporal Data Analytics and Modeling |
| Sub Title | Techniques and Applications / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XII, 245 p. 73 illus., 39 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Big Data Management, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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. . |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | 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. . |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Data processing. |
| 700 1# - AUTHOR 2 | |
| Author 2 | A, John. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Abimannan, Satheesh. |
| 700 1# - AUTHOR 2 | |
| Author 2 | El-Alfy, El-Sayed M. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Chang, Yue-Shan. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-981-99-9651-3 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Singapore : |
| -- | Springer Nature Singapore : |
| -- | Imprint: Springer, |
| -- | 2024. |
| 336 ## - | |
| -- | text |
| -- | txt |
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| -- | computer |
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| -- | rdamedia |
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| -- | online resource |
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| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data mining. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Quantitative research. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Big data. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Mining and Knowledge Discovery. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Analysis and Big Data. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Big Data. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Engineering. |
| 700 1# - AUTHOR 2 | |
| -- | (orcid) |
| -- | 0000-0003-3961-1581 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2522-0187 |
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