The main approach to understanding and creating knowledge engineering concepts is static knowledge. Currently, there is a need to approach knowledge through a dynamic lens and address changing relations on an elaborated syntactic and semantic basis. Dynamic Knowledge Representation in Scientific Domains provides emerging research on the internal and external changes in knowledge within various subject areas and their visual representations. While highlighting topics such as behavior diagrams, distribution analysis, and qualitative modeling, this publication explores the structural development and assessment of knowledge models. This book is an important resource for academicians, researchers, students, and practitioners seeking current research on information visualization in order to foster research and collaboration.

As organizations continue to develop, there is an increasing need for technological methods that can keep up with the rising amount of data and information that is being generated. Machine learning is a tool that has become powerful due to its ability to analyze large amounts of data quickly. Machine learning is one of many technological advancements that is being implemented into a multitude of specialized fields. An extensive study on the execution of these advancements within professional industries is necessary. The Handbook of Research on Big Data Clustering and Machine Learning is an essential reference source that synthesizes the analytic principles of clustering and machine learning to big data and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. Featuring research on topics such as project management, contextual data modeling, and business information systems, this book is ideally designed for engineers, economists, finance officers, marketers, decision makers, business professionals, industry practitioners, academicians, students, and researchers seeking coverage on the implementation of big data and machine learning within specific professional fields.

In the world of mathematics and computer science, technological advancements are constantly being researched and applied to ongoing issues. Setbacks in social networking, engineering, and automation are themes that affect everyday life, and researchers have been looking for new techniques in which to solve these challenges. Graph theory is a widely studied topic that is now being applied to real-life problems. The Handbook of Research on Advanced Applications of Graph Theory in Modern Society is an essential reference source that discusses recent developments on graph theory, as well as its representation in social networks, artificial neural networks, and many complex networks. The book aims to study results that are useful in the fields of robotics and machine learning and will examine different engineering issues that are closely related to fuzzy graph theory. Featuring research on topics such as artificial neural systems and robotics, this book is ideally designed for mathematicians, research scholars, practitioners, professionals, engineers, and students seeking an innovative overview of graphic theory.

Continual advancements in web technology have highlighted the need for formatted systems that computers can utilize to easily read and sift through the hundreds of thousands of data points across the internet. Therefore, having the most relevant data in the least amount of time to optimize the productivity of users becomes a priority. Semantic Web Science and Real-World Applications provides emerging research exploring the theoretical and practical aspects of semantic web science and real-world applications within the area of big data. Featuring coverage on a broad range of topics such as artificial intelligence, social media monitoring, and microblogging recommendation systems, this book is ideally designed for IT consultants, academics, professionals, and researchers of web science seeking the current developments, requirements and standards, and technology spaces presented across academia and industries.

In the evolving environment of education, academic scholars face the daunting challenge of navigating a multitude of pedagogical approaches and technologies. The design of effective learning activities demands a nuanced understanding of didactic innovation, instructional design, and the integration of technology. As educators strive to meet the diverse needs of learners, the demand for innovative solutions to enhance teaching methodologies becomes more pressing than ever. Technological Tools for Innovative Teaching emerges as a comprehensive solution to the challenges educators encounter in the modern academic arena. The book unravels the intricacies of pedagogical scenarios, providing a step-by-step guide to designing learning activities that align with educational objectives. By addressing topics such as the pedagogy of error, flipped classroom strategies, and tech pedagogy, the book equips scholars with a diverse toolkit to revolutionize their teaching methods.

With the development of Web 2.0 technologies, the internet has become a huge platform for information and data sharing. As such, web services provide an important foundation for branching technologies in end-user computing and applications. To make online technology more accessible for users, it is important to optimize web services to function properly or offer a personalized experience. Innovative Solutions and Applications of Web Services Technology is a collection of innovative research on the methods and applications of existing technologies for web service usability and accessibility. Highlighting a range of topics including business processes, cyber-physical systems, and recommendation accuracy, this book is ideally designed for IT professionals, researchers, graduate-level students, software developers, academicians, and computer engineers seeking current research on adapting online information and services to user needs.

In today’s market, emerging technologies are continually assisting in common workplace practices as companies and organizations search for innovative ways to solve modern issues that arise. Prevalent applications including internet of things, big data, and cloud computing all have noteworthy benefits, but issues remain when separately integrating them into the professional practices. Significant research is needed on converging these systems and leveraging each of their advantages in order to find solutions to real-time problems that still exist. Challenges and Opportunities for the Convergence of IoT, Big Data, and Cloud Computing is a pivotal reference source that provides vital research on the relation between these technologies and the impact they collectively have in solving real-world challenges. While highlighting topics such as cloud-based analytics, intelligent algorithms, and information security, this publication explores current issues that remain when attempting to implement these systems as well as the specific applications IoT, big data, and cloud computing have in various professional sectors. This book is ideally designed for academicians, researchers, developers, computer scientists, IT professionals, practitioners, scholars, students, and engineers seeking research on the integration of emerging technologies to solve modern societal issues.