3 research outputs found
μκ°ν μ΄μ¬μμκ² μκ°μ λΉκ΅λ₯Ό λλ μ 보 μκ°ν κΈ°μ μ λμμΈ
νμλ
Όλ¬Έ(λ°μ¬)--μμΈλνκ΅ λνμ :곡과λν μ»΄ν¨ν°κ³΅νλΆ,2020. 2. μμ§μ±.The visual comparison is one of the fundamental tasks in information visualization (InfoVis) that enables people to organize, evaluate, and combine information fragmented in visualizations. For example, people perform visual comparison tasks to compare data over time, from different sources, or with different analytic models. While the InfoVis community has focused on understanding the effectiveness of different visualization designs for supporting visual comparison tasks, it is still unclear how to design effective comparative visualizations due to several limitations: (1) Empirical findings and practical implications from those studies are fragmented, and (2) we lack user studies that directly investigated the effectiveness of different visualization designs for visual comparison.
In this dissertation, we present the results of three studies to build our knowledge on how to support effective visual comparison to InfoVis novicesβ βgeneral people who are not familiar with visual representations and visual data exploration process. Identifying the major stages in the visualization construction process where novices confront challenges with visual comparison tasks, we explored two high-level comparison tasks with actual users: comparing visual mapping (encoding barrier) and comparing information (interpretation barrier) in visualizations. First, we conducted a systematical literature review on research papers (N = 104) that focused on supporting visual comparison tasks to gather and organize the practical insights that researchers gained in the wild. From this study, we offered implications for designing comparative visualizations, such as actionable guidelines, as well as the lucid categorization of comparative designs which can help researchers explore the design space. In the second study, we performed a qualitative user study (N = 24) to investigate how novices compare and understand visual mapping suggested in a visual-encoding recommendation interface. Based on the study, we present novices' main challenges in using visual encoding recommendations and design implications as remedies. In the third study, we conducted a design study in the area on bioinformatics to design and implement a visual analytics tool, XCluSim, that helps users to compare multiple clustering results. Case studies with a bioinformatician showed that our system enables analysts to easily evaluate the quality of a large number of clustering results. Based on the results of three studies in this dissertation, we suggest a future research agenda, such as designing recommendations for visual comparison and distinguishing InfoVis novices from experts.μκ°μ λΉκ΅λ μ 보 μκ°νλ₯Ό μ΄μ©ν ν΅μ¬μ μΈ λ°μ΄ν° λΆμ κ³Όμ μ€ νλλ‘μ¨, λΆμ°λμ΄ μλ μ 보λ€μ μ¬λλ€μ΄ μλ‘ μ 리, νκ°, λ³ν©ν μ μλλ‘ λλλ€. μλ₯Ό λ€μ΄, μ¬λλ€μ μκ°μ νλ¦μ λ°λ₯Έ λ°μ΄ν°μ λ³νλ₯Ό 보거λ, μλ‘ λ€λ₯Έ μΆμ²μ λ°μ΄ν°λ₯Ό λΉκ΅νκ±°λ, κ°μ λ°μ΄ν°λ₯Ό μ¬λ¬ λΆμ λͺ¨λΈλ€μ μ΄μ©ν΄ νκ°νκΈ° μν΄ μκ°μ λΉκ΅ κ³Όμ
μ νν μννκ² λλ€. ν¨κ³Όμ μΈ μκ°ν λμμΈμ μν μ¬λ¬ μ°κ΅¬κ° μ 보 μκ°ν λΆμΌμμ μ΄λ£¨μ΄μ§κ³ μλ λ°λ©΄, μ΄λ€ λμμΈμ ν΅ν΄ ν¨κ³Όμ μΌλ‘ μκ°μ λΉκ΅λ₯Ό μ§μν μ μλμ§μ λν μ΄ν΄λ λ€μμ μ μ½λ€λ‘ μΈν΄ μμ§κΉμ§ λΆλΆλͺ
νλ€. (1) κ²½νμ ν΅μ°°λ€κ³Ό μ€μ©μ μ€κ³ μ§μΉ¨λ€μ΄ ννΈνλμ΄ μμΌλ©° (2) λΉκ΅ μκ°νλ₯Ό μ§μνλ λ°©λ²μ μ΄ν΄νκΈ° μν μ¬μ©μ μ€νμ μκ° μ¬μ ν μ νμ μ΄λ€.
λ³Έ λ
Όλ¬Έμμλ μκ°ν μ΄μ¬μλ€μκ² ν¨κ³Όμ μΌλ‘ μκ°μ λΉκ΅λ₯Ό μ§μνκΈ° μν μ 보 μκ°ν λμμΈ λ°©λ²μ λ κΉμ΄ μ΄ν΄νκΈ° μν΄μ μΌλ ¨μ μΈ μ°κ΅¬λ₯Ό μ§ννκ³ μ΄μ λν κ²°κ³Όλ₯Ό μ μνλ€. νΉλ³ν, μκ°ν μ΄μ¬μλ€μ΄ μκ°μ λΉκ΅λ₯Ό ν λ μ΄λ €μμ κ²½νν μ μλ λ μ£Όμ μκ°ν λ¨κ³λ₯Ό νμΈν¨μΌλ‘μ¨, λ³Έ μ°κ΅¬μμλ μκ°μ μΈμ½λ© λΉκ΅ (μΈμ½λ© μ₯λ²½) λ° μ 보 λΉκ΅ (ν΄μ μ₯λ²½) κ³Όμ
λ€μ μ΄μ μ λ§μΆλ€. 첫째, λΉκ΅ μκ°ν λμμΈμ μ μν λ¬Ένλ€(N = 104)μ 체κ³μ μΌλ‘ μ‘°μ¬ λ° λΆμν¨μΌλ‘μ¨ μκ°ν μ°κ΅¬μλ€μ΄ μ¬μ©μ μ€νκ³Ό μκ°ν μ€κ³ κ³Όμ μ ν΅ν΄ μ»μ μ€μ©μ ν΅μ°°λ€μ μ 리νμλ€. μ΄ λ¬Ένμ‘°μ¬λ₯Ό κΈ°λ°μΌλ‘ λΉκ΅ μκ°ν μ€κ³μ λν μ§μΉ¨λ€μ μ 립νκ³ , λΉκ΅ μκ°νλ₯Ό μν λμμΈ κ³΅κ°μ λ κΉμ΄ μ΄ν΄νκ³ νμνλ λ° λμμ μ€ μ μλ μκ°ν λΆλ₯ λ° μμλ€μ μ 곡νλ€. λμ§Έ, μ΄μ¬μλ€μ΄ μκ°ν μΆμ² μΈν°νμ΄μ€μμ μ΄λ»κ² μλ‘μ΄ μκ°μ μΈμ½λ©λ€μ μλ‘ λΉκ΅νκ³ μ¬μ©νλμ§μ λν μ΄ν΄λ₯Ό λκΈ° μν΄ μ¬μ©μ μ€ν(N = 24)μ μννμλ€. μ΄ μ€νμ κ²°κ³Όλ₯Ό κΈ°λ°μΌλ‘, μ΄μ¬μλ€μ μ£Όμ μ΄λ €μλ€κ³Ό μ΄λ€μ ν΄κ²°νκΈ° μν λμμΈ μ§μΉ¨λ€μ μ μνλ€. μ
μ§Έ, μλͺ
μ 보νμκ° μκ°μ μΌλ‘ λ€μ κ°μ ν΄λ¬μ€ν°λ§ κ²°κ³Όλ€μ λΉκ΅ λ° λΆμν μ μλλ‘ λμμ£Όλ μκ°ν μμ€ν
, XCluSimμ λμμΈνκ³ κ΅¬ννλ λμμΈ μ€ν°λλ₯Ό μννμλ€. μ¬λ‘ μ°κ΅¬λ₯Ό ν΅ν΄ μ€μ λ‘ μλͺ
μ 보νμκ° XCluSimμ μ΄μ©νμ¬ λ§μ ν΄λ¬μ€ν°λ§ κ²°κ³Όλ€μ μ½κ² λΉκ΅ λ° νκ°ν μ μλ€λ κ²μ 보μλ€. λ§μ§λ§μΌλ‘, μ΄ μΈ μ°κ΅¬ κ²°κ³Όλ€μ κΈ°λ°μΌλ‘ λΉκ΅ μκ°ν λΆμΌμμ μ λ§ν ν₯ν μ°κ΅¬λ€μ μ μνλ€.CHAPTER 1. Introduction 1
1.1 Background and Motivation 1
1.2 Research Questions and Approaches 4
1.2.1 Revisiting Comparative Layouts: Design Space, Guidelines, and Future Directions 5
1.2.2 Understanding How InfoVis Novices Compare Visual Encoding Recommendation 6
1.2.3 Designing XCluSim: a Visual Analytics System for Comparing Multiple Clustering Results 7
1.3 Dissertation Outline 8
CHAPTER 2. Related Work 9
2.1 Visual Comparison Tasks 9
2.2 Visualization Designs for Comparison 10
2.2.1 Gleicher et al.s Comparative Layout 11
2.3 Understanding InfoVis Novices 12
2.4 Visualization Recommendation Interfaces 13
2.5 Comparative Visualizations for Cluster Analysis 14
CHAPTER 3. Comparative Layouts Revisited: Design Space, Guidelines, and Future Directions 19
3.1 Introduction 19
3.2 Literature Review 21
3.2.1 Method 22
3.3 Comparative Layouts in The Wild 23
3.3.1 Classifying Comparison Tasks in User Studies 25
3.3.2 Same LayoutIs Called Differently 26
3.3.3 Lucid Classification of Comparative Layouts 28
3.3.4 Advantages and Concerns of Using Each Layout 30
3.3.5 Trade-offs between Comparative Layouts 36
3.3.6 Approaches to Overcome the Concerns 38
3.3.7 Comparative Layout Explorer 42
3.4 Discussion 42
3.4.1 Guidelines for Comparative Layouts 44
3.4.2 Promising Directions for Future Research 48
3.5 Summary 49
CHAPTER 4. Understanding How InfoVis Novices Compare Visual Encoding Recommendation 51
4.1 Motivation 51
4.2 Interface 53
4.2.1 Visualization Goals 53
4.2.2 Recommendations 54
4.2.3 Representation Methods for Recommendations 54
4.2.4 Interface 58
4.2.5 Pilot Study 61
4.3 User Study 62
4.3.1 Participants 62
4.3.2 Interface 62
4.3.3 Tasks and Datasets 65
4.3.4 Procedure. 65
4.4 Findings 68
4.4.1 Poor Design Decisions 68
4.4.2 Role of Preview, Animated Transition, and Text 69
4.4.3 Challenges For Understanding Recommendations 70
4.4.4 Learning By Doing 71
4.4.5 Effects of Recommendation Order 71
4.4.6 Personal Criteria for Selecting Recommendations 72
4.5 Discussion 73
4.5.1 Design Implications 73
4.5.2 Limitations and FutureWork 75
4.6 Summary 77
CHAPTER 5. Designing XCluSim: a Visual Analytics System for Comparing Multiple Clustering Results 78
5.1 Motivation 78
5.2 Task Analysis and Design Goals 79
5.3 XCluSim 80
5.3.1 Color Encoding of Clusters Using Tree Colors 82
5.3.2 Overview of All Clustering Results 83
5.3.3 Visualization for Comparing Selected Clustering Results 86
5.3.4 Visualization for Individual Clustering Results 92
5.3.5 Implementation 100
5.4 CaseStudy 100
5.4.1 Elucidating the Role of Ferroxidase in Cryptococcus Neoformans Var. Grubii H99 (CaseStudy 1) 100
5.4.2 Finding a Clustering Result that Clearly Represents Biological Relations (CaseStudy 2) 103
5.5 Discussion 106
5.5.1 Limitations and FutureWork 108
5.6 Summary 108
CHAPTER 6. Future Research Agenda 110
6.0.1 Recommendation for Visual Comparison 110
6.0.2 Understanding the Perception of Subtle Difference 111
6.0.3 Distinguishing InfoVis Novices from Experts 112
CHAPTER 7. Conclusion. 113
Abstract (Korean) 129
Acknowledgments (Korean) 131Docto