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TY  - BOOK
ID  - 125102004
TI  - Interactive visual data analysis
AU  - Tominski, Christian
AU  - Schumann, Heidrun
PY  - 2020
SN  - 9780367898755 9781498753982
PB  - Boca Raton CRC Press, Taylor & Francis Group
DB  - UniCat
KW  - Big data.
KW  - Données massives.
KW  - Information visualization.
KW  - Data visualisation.
KW  - Visual communication.
KW  - Communication visuelle.
KW  - Operational research. Game theory
KW  - Données massives.
UR  - https://www.unicat.be/uniCat?func=search&query=sysid:125102004
AB  - "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." [Publisher]
ER  -