Lying at the intersection of education, art, and cultural heritage, visualization is a powerful tool for representing and interpreting complex information.

This unique text/reference reviews the evolution of the field of visualization, providing innovative examples of applied knowledge visualization from disciplines as varied as law, business management, the arts and humanities. With coverage of theoretical and practical aspects of visualization from ancient Sumerian tablets through to twenty-first century legal contracts, this work underscores the important role that the process of visualization plays in extracting, organizing, and crystallizing the concepts found in complex data.

Topics and features: contains contributions from an international selection of preeminent authorities; presents a thorough introduction to the discipline of knowledge visualization, its current state of affairs and possible future developments; examines how tables have been used for information visualization in historical textual documents; discusses the application of visualization techniques for knowledge transfer in business relationships, and for the linguistic exploration and analysis of sensory descriptions; investigates the use of visualization to understand orchestral music scores, the optical theory behind Renaissance art, and to assist in the reconstruction of an historic church; describes immersive 360 degree stereographic visualization, knowledge-embedded embodied interaction, and a novel methodology for the analysis of architectural forms.

This interdisciplinary collection of the state of the art in knowledge visualization will be of considerable interest to researchers from a broad spectrum of backgrounds in both industry and academia.

This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain. This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level. The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations. The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.

This text reviews the evolution of the field of visualization, providing innovative examples from various disciplines, highlighting the important role that visualization plays in extracting and organizing the concepts found in complex data. Features: presents a thorough introduction to the discipline of knowledge visualization, its current state of affairs and possible future developments; examines how tables have been used for information visualization in historical textual documents; discusses the application of visualization techniques for knowledge transfer in business relationships, and for the linguistic exploration and analysis of sensory descriptions; investigates the use of visualization to understand orchestral music scores, the optical theory behind Renaissance art, and to assist in the reconstruction of an historic church; describes immersive 360 degree stereographic visualization, knowledge-embedded embodied interaction, and a novel methodology for the analysis of architectural forms.

This book is devoted to the emerging field of integrated visual knowledge discovery that combines advances in artificial intelligence/machine learning and visualization/visual analytic. A long-standing challenge of artificial intelligence (AI) and machine learning (ML) is explaining models to humans, especially for live-critical applications like health care. A model explanation is fundamentally human activity, not only an algorithmic one. As current deep learning studies demonstrate, it makes the paradigm based on the visual methods critically important to address this challenge. In general, visual approaches are critical for discovering explainable high-dimensional patterns in all types in high-dimensional data offering "n-D glasses," where preserving high-dimensional data properties and relations in visualizations is a major challenge. The current progress opens a fantastic opportunity in this domain.  This book is a collection of 25 extended works of over 70 scholars presented at AI and visual analytics related symposia at the recent International Information Visualization Conferences with the goal of moving this integration to the next level.  The sections of this book cover integrated systems, supervised learning, unsupervised learning, optimization, and evaluation of visualizations.  The intended audience for this collection includes those developing and using emerging AI/machine learning and visualization methods. Scientists, practitioners, and students can find multiple examples of the current integration of AI/machine learning and visualization for visual knowledge discovery. The book provides a vision of future directions in this domain. New researchers will find here an inspiration to join the profession and to be involved for further development. Instructors in AI/ML and visualization classes can use it as a supplementary source in their undergraduate and graduate classes.