In the age of big data, being able to make sense of data is an important key to success. Interactive Visual Data Analysis advocates the synthesis of visualization, interaction, and automatic computation to facilitate insight generation and knowledge crystallization from large and complex data.
The book provides a systematic and comprehensive overview of visual, interactive, and analytical methods. It introduces criteria for designing interactive visual data analysis solutions, discusses factors influencing the design, and examines the involved processes. The reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal data, geo-spatial data, and graph data. A dedicated chapter introduces general concepts for interacting with visualizations and illustrates how modern interaction technology can facilitate the visual data analysis in many ways. Addressing today’s large and complex data, the book covers relevant automatic analytical computations to support the visual data analysis. The book also sheds light on advanced concepts for visualization in multi-display environments, user guidance during the data analysis, and progressive visual data analysis.
The authors present a top-down perspective on interactive visual data analysis with a focus on concise and clean terminology. Many real-world examples and rich illustrations make the book accessible to a broad interdisciplinary audience from students, to experts in the field, to practitioners in data-intensive application domains.
For more information, you can also visit the author website, where the book's figures will be made available under the CC BY Open Access license: https://ivda-book.de/
"Christian Tominski and Heidrun Schumann give an excellent, encompassing introduction to concepts and techniques for the visual analysis of data. Their book stands out for its concise, integrated coverage of the main approaches for visual representation of and interaction with data, and automatic data analysis support. The work is very accessible and very well suited for classroom use. It is also an inspiring read for practitioners who wish to apply visual data analysis techniques to solve problems in business, engineering, science, and many other domains." --Professor Tobias Schreck, Institute of Computer Graphics and Knowledge Visualization, Graz University of Technology
Foreword by Jarke J. van Wijk. Preface. Author Bios. 1. Introduction. 2. Criteria, Factors, and Models. 3. Visualization Methods and Techniques. 4. Interacting with Visualizations. 5. Automatic Analysis Support. 6. Advanced Concepts. 7. Summary. Bibliography. Index. Figure Credits