Intelligent Agents In Data Intensive Computing
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Intelligent Agents in Data-intensive Computing
This book presents new approaches that advance research in all aspects of agent-based models, technologies, simulations and implementations for data intensive applications. The nine chapters contain a review of recent cross-disciplinary approaches in cloud environments and multi-agent systems, and important formulations of data intensive problems in distributed computational environments together with the presentation of new agent-based tools to handle those problems and Big Data in general. This volume can serve as a reference for students, researchers and industry practitioners working in or interested in joining interdisciplinary work in the areas of data intensive computing and Big Data systems using emergent large-scale distributed computing paradigms. It will also allow newcomers to grasp key concepts and potential solutions on advanced topics of theory, models, technologies, system architectures and implementation of applications in Multi-Agent systems and data intensive computing.
Data Intensive Computing Applications for Big Data
The book ‘Data Intensive Computing Applications for Big Data’ discusses the technical concepts of big data, data intensive computing through machine learning, soft computing and parallel computing paradigms. It brings together researchers to report their latest results or progress in the development of the above mentioned areas. Since there are few books on this specific subject, the editors aim to provide a common platform for researchers working in this area to exhibit their novel findings. The book is intended as a reference work for advanced undergraduates and graduate students, as well as multidisciplinary, interdisciplinary and transdisciplinary research workers and scientists on the subjects of big data and cloud/parallel and distributed computing, and explains didactically many of the core concepts of these approaches for practical applications. It is organized into 24 chapters providing a comprehensive overview of big data analysis using parallel computing and addresses the complete data science workflow in the cloud, as well as dealing with privacy issues and the challenges faced in a data-intensive cloud computing environment. The book explores both fundamental and high-level concepts, and will serve as a manual for those in the industry, while also helping beginners to understand the basic and advanced aspects of big data and cloud computing.
Smart Education and e-Learning 2017
This book gathers the contributions presented at the 4th International KES Conference on Smart Education and Smart e-Learning (KES-SEEL-17), which took place in Vilamoura, Algarve, Portugal, June 21–23, 2017. Smart education and smart e-Learning are emerging and rapidly growing areas. They represent the innovative integration of smart systems, technologies and objects, smart environments, smart pedagogy, smart learning and academic analytics, various branches of computer science and computer engineering, and state-of-the-art smart educational software and/or hardware systems. It contains a total of 48 peer-reviewed book chapters that are grouped into several parts: Part 1 – Smart Pedagogy, Part 2 – Smart e-Learning, Part 3 – Systems and Technologies for Smart Education, Part 4 – Smart Teaching, and Part 5 – Smart Education: National Initiatives and Approaches. The book offers a valuable source of research data, information on best practices, and case studies for educators, researchers, Ph.D. students, administrators, and practitioners—and all those who are interested in innovative areas of smart education and smart e-Learning.