Uncertain Spatiotemporal Data Management For The Semantic Web
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Uncertain Spatiotemporal Data Management For The Semantic Web
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Author : Bai, Luyi
language : en
Publisher: IGI Global
Release Date : 2024-03-01
Uncertain Spatiotemporal Data Management For The Semantic Web written by Bai, Luyi and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-01 with Computers categories.
In the world of data management, one of the most formidable challenges faced by academic scholars is the effective handling of spatiotemporal data within the semantic web. As our world continues to change dynamically with time, nearly every aspect of our lives, from environmental monitoring to urban planning and beyond, is intrinsically linked to time and space. This synergy has given rise to an avalanche of spatiotemporal data, and the pressing question is how to manage, model, and query this voluminous information effectively. The existing approaches often fall short in addressing the intricacies and uncertainties that come with spatiotemporal data, leaving scholars struggling to unlock its full potential. Uncertain Spatiotemporal Data Management for the Semantic Web is the definitive solution to the challenges faced by academic scholars in the realm of spatiotemporal data. This book offers a visionary approach to an all-encompassing guide in modeling and querying spatiotemporal data using innovative technologies like XML and RDF. Through a meticulously crafted set of chapters, this book sheds light on the nuances of spatiotemporal data and also provides practical solutions that empower scholars to navigate the complexities of this domain effectively.
Uncertain Spatiotemporal Data Management For The Semantic Web
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Author : LUYI. BAI
language : en
Publisher:
Release Date : 2023
Uncertain Spatiotemporal Data Management For The Semantic Web written by LUYI. BAI and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with categories.
Uncertain Spatiotemporal Data Management For The Semantic Web
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Author : Bai
language : en
Publisher: Engineering Science Reference
Release Date : 2023-12-15
Uncertain Spatiotemporal Data Management For The Semantic Web written by Bai and has been published by Engineering Science Reference this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-12-15 with categories.
In the world of data management, one of the most formidable challenges faced by academic scholars is the effective handling of spatiotemporal data within the semantic web. As our world continues to change dynamically with time, nearly every aspect of our lives, from environmental monitoring to urban planning and beyond, is intrinsically linked to time and space. This synergy has given rise to an avalanche of spatiotemporal data, and the pressing question is how to manage, model, and query this voluminous information effectively. The existing approaches often fall short in addressing the intricacies and uncertainties that come with spatiotemporal data, leaving scholars struggling to unlock its full potential. Uncertain Spatiotemporal Data Management for the Semantic Web is the definitive solution to the challenges faced by academic scholars in the realm of spatiotemporal data. This book offers a visionary approach to an all-encompassing guide in modeling and querying spatiotemporal data using innovative technologies like XML and RDF. Through a meticulously crafted set of chapters, this book sheds light on the nuances of spatiotemporal data and also provides practical solutions that empower scholars to navigate the complexities of this domain effectively. This book caters specifically to the academic community, offering in-depth insights, innovative frameworks, and real-world applications that unlock the true potential of spatiotemporal data. With a comprehensive range of topics, from modeling to prediction and query optimization, this book equips scholars with the knowledge and tools they need to pioneer advancements in their field. Seasoned researchers and budding academics alike will find guidance within the pages of Uncertain Spatiotemporal Data Management for the Semantic Web along a transformative journey towards harnessing the power of spatiotemporal data in the semantic web, shaping the future of data management.
The Semantic Web For Knowledge And Data Management
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Author : Ma, Zongmin
language : en
Publisher: IGI Global
Release Date : 2008-08-31
The Semantic Web For Knowledge And Data Management written by Ma, Zongmin and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-31 with Computers categories.
Provides a single record of technologies and practices of the Semantic approach to the management, organization, interpretation, retrieval, and use of Web-based data.
Digital Technologies In Modeling And Management Insights In Education And Industry
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Author : Prakasha, G. S.
language : en
Publisher: IGI Global
Release Date : 2024-04-04
Digital Technologies In Modeling And Management Insights In Education And Industry written by Prakasha, G. S. and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-04 with Computers categories.
Digital Technologies in Modeling and Management: Insights in Education and Industry explores the use of digital technologies in the modeling and control of complex systems in various fields, such as social networks, education, technical systems, and their protection and security. The book consists of two parts, with the first part focusing on modeling complex systems using digital technologies, while the second part deals with the digitalization of economic processes and their management. The book results from research conducted by leading universities' teaching staff and contains the results of many years of scientific experiments and theoretical conclusions. The book is for a wide range of readers, including the teaching staff of higher educational institutions, graduate students, students in computer science and modeling, and management technologies, including economics. It is also a valuable resource for IT professionals and business analysts interested in using digital technologies to model and control complex systems.
Modeling Fuzzy Spatiotemporal Data With Xml
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Author : Zongmin Ma
language : en
Publisher: Springer Nature
Release Date : 2020-03-04
Modeling Fuzzy Spatiotemporal Data With Xml written by Zongmin Ma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-04 with Technology & Engineering categories.
This book offers in-depth insights into the rapidly growing topic of technologies and approaches to modeling fuzzy spatiotemporal data with XML. The topics covered include representation of fuzzy spatiotemporal XML data, topological relationship determination for fuzzy spatiotemporal XML data, mapping between the fuzzy spatiotemporal relational database model and fuzzy spatiotemporal XML data model, and consistencies in fuzzy spatiotemporal XML data updating. Offering a comprehensive guide to the latest research on fuzzy spatiotemporal XML data management, the book is intended to provide state-of-the-art information for researchers, practitioners, and graduate students of Web intelligence, as well as data and knowledge engineering professionals confronted with non-traditional applications that make the use of conventional approaches difficult or impossible.
Big Data Quantification For Complex Decision Making
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Author : Zhang, Chao
language : en
Publisher: IGI Global
Release Date : 2024-04-16
Big Data Quantification For Complex Decision Making written by Zhang, Chao and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-16 with Business & Economics categories.
Many professionals are facing a monumental challenge: navigating the intricate landscape of information to make impactful choices. The sheer volume and complexity of big data have ushered in a shift, demanding innovative methodologies and frameworks. Big Data Quantification for Complex Decision-Making tackles this challenge head-on, offering a comprehensive exploration of the tools necessary to distill valuable insights from datasets. This book serves as a tool for professionals, researchers, and students, empowering them to not only comprehend the significance of big data in decision-making but also to translate this understanding into real-world decision making. The central objective of the book is to examine the relationship between big data and decision-making. It strives to address multiple objectives, including understanding the intricacies of big data in decision-making, navigating methodological nuances, managing uncertainty adeptly, and bridging theoretical foundations with real-world applications. The book's core aspiration is to provide readers with a comprehensive toolbox, seamlessly integrating theoretical frameworks, practical applications, and forward-thinking perspectives. This equips readers with the means to effectively navigate the data-rich landscape of modern decision-making, fostering a heightened comprehension of strategic big data utilization. Tailored for a diverse audience, this book caters to researchers and academics in data science, decision science, machine learning, artificial intelligence, and related domains.
Technological Advancements In Data Processing For Next Generation Intelligent Systems
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Author : Sharma, Shanu
language : en
Publisher: IGI Global
Release Date : 2024-03-18
Technological Advancements In Data Processing For Next Generation Intelligent Systems written by Sharma, Shanu and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-18 with Computers categories.
Technological Advancements in Data Processing for Next Generation Intelligent Systems presents an in-depth exploration of cutting-edge data processing technologies that drive the development of next-generation intelligent systems in the context of the digital transformation era. This comprehensive book delves into the role data plays as a critical asset for organizations across diverse industries, and how recent technological breakthroughs have unlocked unprecedented potential for handling vast data volumes and real-time analysis. The book begins by providing a thorough overview of novel technologies such as artificial intelligence (AI) or machine learning (ML), edge computing, federated learning, quantum computing, and more. These revolutionary technologies, when integrated with big data frameworks, in-memory computing, and AI/ML algorithms, have transformed data processing capabilities, enabling the creation of intelligent systems that fuel innovation, optimize operations, and deliver personalized experiences. The ultimate aim of this integration is to empower devices with the ability to make autonomous intelligent decisions, maximizing computing power. This book serves as a valuable resource for research scholars, academicians, and industry professionals working towards the future advancement of optimized intelligent systems and intelligent data processing approaches. The chapters encompass a wide range of topics, including architecture and frameworks for intelligent systems, applications in diverse domains, cloud-based solutions, quantum processing, federated learning, in-memory data processing, real-time stream processing, trustworthy AI for Internet of Things (IoT) sensory data, and more.
The Convergence Of Self Sustaining Systems With Ai And Iot
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Author : Rajappan, Roopa Chandrika
language : en
Publisher: IGI Global
Release Date : 2024-04-26
The Convergence Of Self Sustaining Systems With Ai And Iot written by Rajappan, Roopa Chandrika and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-04-26 with Business & Economics categories.
Picture a world where autonomous systems operate continuously and intelligently, utilizing real-time data to make informed decisions. Such systems have the potential to revolutionize agriculture, urban infrastructure, and industrial automation. This transformation, often termed the Internet of Self-Sustaining Systems (IoSS), is a pivotal topic that demands academic attention and exploration. Addressing this critical issue head-on is The Convergence of Self-Sustaining Systems With AI and IoT, which offers an in-depth examination of this transformative convergence. It serves as a guiding light for academic scholars seeking to unravel the vast potential of self-sustaining systems coupled with AI and IoT. Inside its pages, readers will delve into AI-driven autonomous agriculture, eco-friendly transportation solutions, and intelligent energy management. Moreover, the book explores emerging technologies, security concerns, ethical considerations, and governance frameworks. Join us on this intellectual journey and position yourself at the forefront of the AI and IoT revolution that promises a sustainable, autonomous future.
Advancing Software Engineering Through Ai Federated Learning And Large Language Models
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Author : Sharma, Avinash Kumar
language : en
Publisher: IGI Global
Release Date : 2024-05-02
Advancing Software Engineering Through Ai Federated Learning And Large Language Models written by Sharma, Avinash Kumar and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-02 with Computers categories.
The rapid evolution of software engineering demands innovative approaches to meet the growing complexity and scale of modern software systems. Traditional methods often need help to keep pace with the demands for efficiency, reliability, and scalability. Manual development, testing, and maintenance processes are time-consuming and error-prone, leading to delays and increased costs. Additionally, integrating new technologies, such as AI, ML, Federated Learning, and Large Language Models (LLM), presents unique challenges in terms of implementation and ethical considerations. Advancing Software Engineering Through AI, Federated Learning, and Large Language Models provides a compelling solution by comprehensively exploring how AI, ML, Federated Learning, and LLM intersect with software engineering. By presenting real-world case studies, practical examples, and implementation guidelines, the book ensures that readers can readily apply these concepts in their software engineering projects. Researchers, academicians, practitioners, industrialists, and students will benefit from the interdisciplinary insights provided by experts in AI, ML, software engineering, and ethics.