This is the first book that focuses entirely on the fundamental questions in visualization. Unlike other existing books in the field, it contains discussions that go far beyond individual visual representations and individual visualization algorithms. It offers a collection of investigative discourses that probe these questions from different perspectives, including concepts that help frame these questions and their potential answers, mathematical methods that underpin the scientific reasoning of these questions, empirical methods that facilitate the validation and falsification of potential answers, and case studies that stimulate hypotheses about potential answers while providing practical evidence for such hypotheses. Readers are not instructed to follow a specific theory, but their attention is brought to a broad range of schools of thoughts and different ways of investigating fundamental questions. As such, the book represents the by now most significant collective effort for gathering a large collection of discourses on the foundation of data visualization. Data visualization is a relatively young scientific discipline. Over the last three decades, a large collection of computer-supported visualization techniques have been developed, and the merits and benefits of using these techniques have been evidenced by numerous applications in practice. These technical advancements have given rise to the scientific curiosity about some fundamental questions such as why and how visualization works, when it is useful or effective and when it is not, what are the primary factors affecting its usefulness and effectiveness, and so on. This book signifies timely and exciting opportunities to answer such fundamental questions by building on the wealth of knowledge and experience accumulated in developing and deploying visualization technology in practice.

Billions of smartphones--fantastically powerful, varied, constantly changing---can be had for as little as $50 US. Why is that? They are made by an open ecosystem cooperative community of companies working together, engaging talent from around the world--dedicated to creating ever-more-innovative offerings that in turn support magnitudes of apps, each of which is capable of supporting additional ecosystems, which in turn transform our daily personal ecosystems. The result? The largest economic cooperative in history. This is the story of how this co-op came to be and what we can learn from it in order to thrive.

This book demonstrates the significance of domain-specific conceptual modeling through new research and development approaches that are manifested in each of the chapters. They include novel modelling methods and tools that emphasize the recent results accomplished and their adequacy to assess specific aspects of a domain. Each chapter offers detailed instructions on how to build models in a particular domain, such as product-service engineering, enterprise engineering, digital business ecosystems, and enterprise modelling and capability management. All chapters are enriched with case studies, related information, and tool implementations. The tools are based on the ADOxx metamodelling platform and are provided free of charge via OMiLAB. Furthermore, the book emphasizes possible future developments and potential research directions. The collection of works presented here will benefit experts and practitioners from academia and industry alike, including members of the conceptual modeling community as well as lecturers and students.

The goal of the Asilomar Workshop on Fault-Tolerant Distributed Computing, held March 17-19, 1986, was to facilitate interaction between theoreticians and practitioners by inviting speakers and choosing topics so as to present a broad overview of the field. This volume contains 22 papers stemming from the workshop, most of them revised and rewritten, presenting research results in distributed systems and fault-tolerant architectures and systems. The volume should be of use to students, researchers and developers.

This book surveys fundamental concepts and practical methods for creating and curating large knowledge bases.