Linked Data Visualization [electronic resource] : Techniques, Tools, and Big Data / by Laura Po, Nikos Bikakis, Federico Desimoni, George Papastefanatos.
By: Po, Laura [author.]
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Contributor(s): Bikakis, Nikos [author.] | Desimoni, Federico [author.] | Papastefanatos, George [author.] | SpringerLink (Online service).
Material type: 
BookSeries: Synthesis Lectures on Data, Semantics, and Knowledge: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2020Edition: 1st ed. 2020.Description: XIV, 143 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031794902.Subject(s): Mathematics | Internet programming | Application software | Computer networks | Ontology | Mathematics | Web Development | Computer and Information Systems Applications | Computer Communication Networks | OntologyAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 510 Online resources: Click here to access online Preface -- Acknowledgments -- Introduction -- Principles of Data Visualization -- Linked Data Visualization Tools -- Visualization Use Cases -- Empirical Evaluation of Linked Data Visualization Tools -- Conclusions and Future Challenges -- Bibliography -- Authors' Biographies.
Linked Data (LD) is a well-established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from different scientific and commercial domains. The development of Linked Data Visualization techniques and tools has been followed as the primary means for the analysis of this vast amount of information by data scientists, domain experts, business users, and citizens. This book covers a wide spectrum of visualization issues, providing an overview of the recent advances in this area, focusing on techniques, tools, and use cases of visualization and visual analysis of LD. It presents the basic concepts related to data visualization and the LD technologies, the techniques employed for data visualization based on the characteristics of data techniques for Big Data visualization, use tools and use cases in the LD context, and finally a thorough assessment of the usability of these tools under different scenarios. The purpose of this book is to offer a complete guide to the evolution of LD visualization for interested readers from any background and to empower them to get started with the visual analysis of such data. This book can serve as a course textbook or a primer for all those interested in LD and data visualization.
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