Visualization of Time-Oriented Data

This is an open access book. Time is an exceptional dimension with high relevance in medicine, engineering, business, science, biography, history, planning, or project management. Understanding time-oriented data via visual representations enables us to learn from the past in order to predict, plan,...

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Published inHuman-computer interaction series
Main Authors Aigner, Wolfgang, Miksch, Silvia, Schumann, Heidrun, Tominski, Christian
Format eBook
LanguageEnglish
Published London Springer Nature 2023
Springer London, Limited
Edition2
SeriesHuman–Computer Interaction Series
Subjects
Online AccessGet full text
ISBN9781447175278
1447175263
9781447175261
1447175271
ISSN1571-5035
2524-4477
DOI10.1007/978-1-4471-7527-8

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Abstract This is an open access book. Time is an exceptional dimension with high relevance in medicine, engineering, business, science, biography, history, planning, or project management. Understanding time-oriented data via visual representations enables us to learn from the past in order to predict, plan, and build the future. This second edition builds upon the great success of the first edition. It maintains a brief introduction to visualization and a review of historical time-oriented visual representations. At its core, the book develops a systematic view of the visualization of time-oriented data. Separate chapters discuss interaction techniques and computational methods for supporting the visual data analysis. Many examples and figures illustrate the introduced concepts and techniques. So, what is new for the second edition? First of all, the second edition is now published as an open-access book so that anyone interested in the visualization of time and time-oriented data can read it. Second, the entire content has been revised and expanded to represent state-of-the-art knowledge. The chapter on interaction support now includes advanced methods for interacting with visual representations of time-oriented data. The second edition also covers the topics of data quality as well as segmentation and labeling. The comprehensive survey of classic and contemporary visualization techniques now provides more than 150 self-contained descriptions accompanied by illustrations and corresponding references. A completely new chapter describes how the structured survey can be used for the guided selection of suitable visualization techniques. For the second edition, our TimeViz Browser, the digital pendant to the survey of visualization techniques, received a major upgrade. It includes the same set of techniques as the book, but comes with additional filter and search facilities allowing scientists and practitioners to find exactly the solutions they are interested in. ; Time is an exceptional data dimension due to its importance and unique influence. This implies that special methods are needed for dealing with time in data sets in order to take advantage of these specifics. Visual methods are a key part of this tool set allowing for presentation, analysis, and exploration of temporal data sets. Visualisation of Time-Oriented Data provides an overview of the field of visualisation techniques for time-oriented data, but even more importantly analyses the specifics and similarities of the investigated techniques in order to identify clusters for structuring and making the field manageable. It also analyses the techniqes uniformly along different sets of attributes making it easy to grasp their main characteristics and support researchers as well as developers in selecting appropriate techniques for their demands. The theoretic foundations of visualization itself are also discussed, the data dimension time, tasks related to time-oriented data, possible mappings of time, various examples of early (pre-computer age) representation techniques, and a conceptual framework for visually analyzing time-oriented data.
AbstractList This is an open access book. Time is an exceptional dimension with high relevance in medicine, engineering, business, science, biography, history, planning, or project management. Understanding time-oriented data via visual representations enables us to learn from the past in order to predict, plan, and build the future. This second edition builds upon the great success of the first edition. It maintains a brief introduction to visualization and a review of historical time-oriented visual representations. At its core, the book develops a systematic view of the visualization of time-oriented data. Separate chapters discuss interaction techniques and computational methods for supporting the visual data analysis. Many examples and figures illustrate the introduced concepts and techniques. So, what is new for the second edition? First of all, the second edition is now published as an open-access book so that anyone interested in the visualization of time and time-oriented data can read it. Second, the entire content has been revised and expanded to represent state-of-the-art knowledge. The chapter on interaction support now includes advanced methods for interacting with visual representations of time-oriented data. The second edition also covers the topics of data quality as well as segmentation and labeling. The comprehensive survey of classic and contemporary visualization techniques now provides more than 150 self-contained descriptions accompanied by illustrations and corresponding references. A completely new chapter describes how the structured survey can be used for the guided selection of suitable visualization techniques. For the second edition, our TimeViz Browser, the digital pendant to the survey of visualization techniques, received a major upgrade. It includes the same set of techniques as the book, but comes with additional filter and search facilities allowing scientists and practitioners to find exactly the solutions they are interested in. ; Time is an exceptional data dimension due to its importance and unique influence. This implies that special methods are needed for dealing with time in data sets in order to take advantage of these specifics. Visual methods are a key part of this tool set allowing for presentation, analysis, and exploration of temporal data sets. Visualisation of Time-Oriented Data provides an overview of the field of visualisation techniques for time-oriented data, but even more importantly analyses the specifics and similarities of the investigated techniques in order to identify clusters for structuring and making the field manageable. It also analyses the techniqes uniformly along different sets of attributes making it easy to grasp their main characteristics and support researchers as well as developers in selecting appropriate techniques for their demands. The theoretic foundations of visualization itself are also discussed, the data dimension time, tasks related to time-oriented data, possible mappings of time, various examples of early (pre-computer age) representation techniques, and a conceptual framework for visually analyzing time-oriented data.
Author Tominski, Christian
Aigner, Wolfgang
Schumann, Heidrun
Miksch, Silvia
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SubjectTerms Book Industry Communication
Combinatorics & graph theory
Computer science
Computer-Generated Visual Representations
Computing & information technology
Human-computer interaction
Mathematics
Mathematics & science
Time-Oriented Data
User interface design & usability
Visual analytics
Visualization
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TableOfContents Intro -- Foreword -- Preface -- About the Authors -- Acknowledgements -- Contents -- Chapter 1 Introduction -- 1.1 Introduction to Visualization -- 1.2 Application Example: Health Record Visualization -- 1.3 Book Outline -- References -- Chapter 2 Historical Background -- 2.1 Classic Ways of Graphing Time -- 2.2 Time in Visual Storytelling &amp -- Arts -- 2.3 Summary -- References -- Chapter 3 Time &amp -- Time-Oriented Data -- 3.1 Modeling Time -- 3.1.1 Design Aspects -- 3.1.2 Granularities &amp -- Time Primitives -- 3.2 Characterizing Data -- 3.3 Relating Data &amp -- Time -- 3.4 Considering Data Quality -- 3.5 Summary -- References -- Chapter 4 Crafting Visualizations of Time-Oriented Data -- 4.1 Characterization of the Visualization Problem -- 4.1.1 What? - Time &amp -- Data -- 4.1.2 Why? - User Tasks -- 4.1.3 How? - Visual Representation -- 4.2 Visualization Design Examples -- 4.2.1 Data Level -- 4.2.2 Task Level -- 4.2.3 Presentation Level -- 4.3 Summary -- References -- Chapter 5 Involving the Human via Interaction -- 5.1 Motivation &amp -- User Intents -- 5.2 Interaction Fundamentals -- 5.2.1 Conceptual Background -- 5.2.2 User Interface -- 5.3 Basic Interaction with Time-Oriented Data -- 5.3.1 Navigation in Time -- 5.3.2 Direct Manipulation -- 5.3.3 Brushing &amp -- Linking -- 5.3.4 Dynamic Queries -- 5.4 Advanced Interaction Methods -- 5.4.1 Interactive Lenses -- 5.4.2 Interactive Visual Comparison -- 5.4.3 Guiding the User -- 5.4.4 Integrating Interaction and Automation via Events -- 5.4.5 Interaction Beyond Mouse and Keyboard -- 5.5 Summary -- References -- Chapter 6 Computational Analysis Support -- 6.1 Temporal Analysis Tasks -- 6.2 Principles of Temporal Data Abstraction -- 6.3 Classification via Segmentation and Labeling -- 6.3.1 Data Classification in Medical Contexts -- 6.3.2 Segmentation and Labeling of Multivariate Time Series
6.3.3 Linking Temporal and Visual Abstraction -- 6.4 Clustering Time Series -- 6.5 Principal Component Analysis for Time-Oriented Data -- 6.5.1 Basic Method -- 6.5.2 Gaining Insight into Climate Data with PCA -- 6.5.3 Determining Relevant Components in Census Data -- 6.6 Summary -- References -- Chapter 7 Guiding the Selection of Visualization Techniques -- 7.1 Structuring the Space of Solutions -- 7.2 The TimeViz Browser -- 7.3 Overview of Visualization Techniques -- 7.4 Guided Search for Visualization Techniques -- 7.4.1 Example Scenarios -- 7.4.2 Towards a Multi-Faceted Selection Process -- 7.5 Summary -- References -- Chapter 8 Conclusion -- 8.1 Book Summary -- 8.2 Practical Concerns -- 8.3 From Visualization to Visual Analytics -- 8.4 Future Research Opportunities -- References -- Appendix A Survey of Visualization Techniques -- A.1 List of Techniques -- A.2 Techniques for Abstract Time-Oriented Data -- A.3 Techniques Supporting a Spatial Frame of Reference -- Appendix B Examples of Data Quality Issues -- B.1 Single-Source Problems -- B.1.1 Missing Data -- B.1.2 Duplicate Data -- B.1.3 Implausible Data -- B.1.4 Outdated Data -- B.1.5 Wrong Data -- B.1.6 Ambiguous Data -- B2 Multi-Source Problems -- B.2.1 Heterogeneous Syntaxes -- B.2.2 Heterogeneous Semantics -- B.2.3 References -- B.3 Summary -- References -- Index
Title Visualization of Time-Oriented Data
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