Interactive Visual Data Analysis

Description

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. Features: Dedicated to the synthesis of visual, interactive, and analysis methods Systematic top-down view on visualization, interaction, and automatic analysis Broad coverage of fundamental and advanced visualization techniques Comprehensive chapter on interacting with visual representations Extensive integration of automatic computational methods Accessible portrayal of cutting-edge visual analytics technology Foreword by Jack van Wijk 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/ Cover Half Title Series Page Title Page Copyright Page Dedication Contents Foreword Preface Authors Chapter 1: Introduction 1.1 BASIC CONSIDERATIONS 1.1.1 Visualization, Interaction, and Computation 1.1.2 Five Ws of Interactive Visual Data Analysis 1.2 INTRODUCTORY EXAMPLES 1.2.1 Starting Simple 1.2.2 Enhancing the Data Analysis 1.2.3 Considering Advanced Techniques 1.3 BOOK OUTLINE Chapter 2: Criteria, Factors, and Models 2.1 CRITERIA 2.2 INFLUENCING FACTORS 2.2.1 The Subject: Data 2.2.2 The Objective: Analysis Tasks 2.2.3 The Context: Users and Technologies 2.2.4 Demonstrating Example 2.3 PROCESS MODELS 2.3.1 Design 2.3.2 Data Transformation 2.3.3 Knowledge Generation 2.4 SUMMARY Chapter 3: Visualization Methods and Techniques 3.1 VISUAL ENCODING AND PRESENTATION 3.1.1 Encoding Data Values 3.1.2 Presentation 3.2 MULTIVARIATE DATA VISUALIZATION 3.2.1 Table-based Visualization 3.2.2 Combined Bivariate Visualization 3.2.3 Polyline-based Visualization 3.2.4 Glyph-based Visualization 3.2.5 Pixel-based Visualization 3.2.6 Nested Visualization 3.3 VISUALIZATION OF TEMPORAL DATA 3.3.1 Time and Temporal Data 3.3.2 Visualization Techniques 3.4 VISUALIZATION OF GEO-SPATIAL DATA 3.4.1 Geographic Space and Geo-spatial Data 3.4.2 General Visualization Strategies 3.4.3 Visualizing Spatio-temporal Data 3.5 GRAPH VISUALIZATION 3.5.1 Graph Data 3.5.2 Basic Visual Representations 3.5.3 Visualizing Multi-faceted Graphs 3.6 SUMMARY Chapter 4: Interacting with Visualizations 4.1 HUMAN IN THE LOOP 4.1.1 Interaction Intents and Action Patterns 4.1.2 The Action Cycle 4.2 REQUIREMENTS FOR EFFICIENT INTERACTION 4.2.1 Interaction Costs 4.2.2 Directness of Interaction 4.2.3 Design Guidelines 4.3 BASIC OPERATIONS FOR INTERACTION 4.3.1 Taking Action 4.3.2 Generating Feedback 4.4 INTERACTIVE SELECTION AND ACCENTUATION 4.4.1 Specifying Selections 4.4.2 Visual Emphasis and Attenuation 4.4.3 Enhanced Selection Support 4.5 NAVIGATING ZOOMABLE VISUALIZATIONS 4.5.1 Basics and Conceptual Considerations 4.5.2 Visual Interface and Interaction 4.5.3 Interaction Aids and Visual Cues 4.5.4 Beyond Zooming in Two Dimensions 4.6 INTERACTIVE LENSES 4.6.1 Conceptual Model 4.6.2 Adjustable Properties 4.6.3 Lenses in Action 4.7 INTERACTIVE VISUAL COMPARISON 4.7.1 Basics and Requirements 4.7.2 Naturally Inspired Comparison 4.7.3 Reducing Comparison Costs 4.8 INTERACTION BEYOND MOUSE AND KEYBOARD 4.8.1 Touching Visualizations 4.8.2 Interacting with Tangibles 4.8.3 Moving the Body to Explore Visualizations 4.9 SUMMARY Chapter 5: Automatic Analysis Support 5.1 DECLUTTERING VISUAL REPRESENTATIONS 5.1.1 Computing and Visualizing Density 5.1.2 Bundling Geometrical Primitives 5.2 FOCUSING ON RELEVANT DATA 5.2.1 Degree of Interest 5.2.2 Feature-based Visual Analysis 5.2.3 Analyzing Features of Chaotic Movement 5.3 ABSTRACTING DATA 5.3.1 Sampling and Aggregation 5.3.2 Exploring Multi-scale Data Abstractions 5.4 GROUPING SIMILAR DATA ELEMENTS 5.4.1 Classification 5.4.2 Data Clustering 5.4.3 Clustering Multivariate Dynamic Graphs 5.5 REDUCING DIMENSIONALITY 5.5.1 Principal Component Analysis 5.5.2 Visual Data Analysis with Principal Components 5.6 SUMMARY Chapter 6: Advanced Concepts 6.1 VISUALIZATION IN MULTI-DISPLAY ENVIRONMENTS 6.1.1 Environment and Requirements 6.1.2 Supporting Collaborative Visual Data Analysis 6.1.3 Multi-display Analysis of Climate Change Impact 6.2 GUIDING THE USER 6.2.1 Characterization of Guidance 6.2.2 Guiding the Navigation in Hierarchical Graphs 6.2.3 Guiding the Visual Analysis of Heterogeneous Data 6.3 PROGRESSIVE VISUAL DATA ANALYSIS 6.3.1 Conceptual Considerations 6.3.2 Multi-threading Architecture 6.3.3 Scenarios 6.4 SUMMARY Chapter 7: Summary 7.1 WHAT’S BEEN DISCUSSED 7.2 HOW TO CONTINUE Bibliography Index Figure Credits