This book presents a systematic discussion about methods and techniques used to extract the maximum informative value from complex data sets. A multitude of approaches and techniques can be applied for that purpose, including data fusion and model integration, multimodal data analysis in different physical domains, audio-video display of data through techniques of “sonification”, multimedia machine learning, and hybrid methods of data analysis. The book begins with the domain of geosciences, before moving on to other scientific areas, like diagnostic medicine and various engineering sectors. As such, it will appeal to a large audience, including geologists and geophysicists, data scientists, physicians and cognitive scientists, and experts in social sciences and knowledge management.

The ongoing economic and financial digitalization is making individual data a key input and source of value for companies across sectors, from bigtechs and pharmaceuticals to manufacturers and financial services providers. Data on human behavior and choices—our “likes,” purchase patterns, locations, social activities, biometrics, and financing choices—are being generated, collected, stored, and processed at an unprecedented scale.

Industrial Tomography: Systems and Applications, Second Edition thoroughly explores the important techniques of industrial tomography, also discusses image reconstruction, systems, and applications. This book presents complex processes, including the way three-dimensional imaging is used to create multiple cross-sections, and how computer software helps monitor flows, filtering, mixing, drying processes, and chemical reactions inside vessels and pipelines. This book is suitable for materials scientists and engineers and applied physicists working in the photonics and optoelectronics industry or in the applications industries. - Provides a comprehensive discussion on the different formats of tomography, including advances in visualization and data fusion - Includes an excellent overview of image reconstruction using a wide range of applications - Presents a comprehensive discussion of tomography systems and their applications in a wide variety of industrial processes

The developmental status of any country is based on its intellectual property (IP). In recent years, intellectual property rights (IPR) have expanded far beyond what was previously imagined. The main purpose of IP law is to encourage the creation of a wide variety of intellectual goods. Intellectual Property - Global Perspective Advances and Challenges discusses issues, recent developments, and solutions in IP. Written by researchers from all over the world, chapters address such topics as copyright, traditional cultural expressions, plagiarism, the role of IP in culture and gender diversity, IP and Blockchain, and much more.

How well does your organization manage the risks associated with information quality? Managing information risk is becoming a top priority on the organizational agenda. The increasing sophistication of IT capabilities along with the constantly changing dynamics of global competition are forcing businesses to make use of their information more effectively. Information is becoming a core resource and asset for all organizations; however, it also brings many potential risks to an organization, from strategic, operational, financial, compliance, and environmental to societal. If you continue to struggle to understand and measure how information and its quality affects your business, this book is for you. This reference is in direct response to the new challenges that all managers have to face. Our process helps your organization to understand the "pain points" regarding poor data and information quality so you can concentrate on problems that have a high impact on core business objectives. This book provides you with all the fundamental concepts, guidelines and tools to ensure core business information is identified, protected and used effectively, and written in a language that is clear and easy to understand for non-technical managers. - Shows how to manage information risk using a holistic approach by examining information from all sources - Offers varied perspectives of an author team that brings together academics, practitioners and researchers (both technical and managerial) to provide a comprehensive guide - Provides real-life case studies with practical insight into the management of information risk and offers a basis for broader discussion among managers and practitioners

This open access book presents the foundations of the Big Data research and innovation ecosystem and the associated enablers that facilitate delivering value from data for business and society. It provides insights into the key elements for research and innovation, technical architectures, business models, skills, and best practices to support the creation of data-driven solutions and organizations. The book is a compilation of selected high-quality chapters covering best practices, technologies, experiences, and practical recommendations on research and innovation for big data. The contributions are grouped into four parts: · Part I: Ecosystem Elements of Big Data Value focuses on establishing the big data value ecosystem using a holistic approach to make it attractive and valuable to all stakeholders. · Part II: Research and Innovation Elements of Big Data Value details the key technical and capability challenges to be addressed for delivering big data value. · Part III: Business, Policy, and Societal Elements of Big Data Value investigates the need to make more efficient use of big data and understanding that data is an asset that has significant potential for the economy and society. · Part IV: Emerging Elements of Big Data Value explores the critical elements to maximizing the future potential of big data value. Overall, readers are provided with insights which can support them in creating data-driven solutions, organizations, and productive data ecosystems. The material represents the results of a collective effort undertaken by the European data community as part of the Big Data Value Public-Private Partnership (PPP) between the European Commission and the Big Data Value Association (BDVA) to boost data-driven digital transformation.