Contours in Visualization

This thesis studies the visualization of set collections either via or defines as the relations among contours. In the first part, dynamic Euler diagrams are used to communicate and improve semimanually the result of clustering methods which allow clusters to overlap arbitrarily. The contours of the Euler diagram are rendered as implicit surfaces called blobs in computer graphics. The interaction metaphor is the moving of items into or out of these blobs. The utility of the method is demonstrated on data arising from the analysis of gene expressions. The method works well for small datasets of up to one hundred items and few clusters. In the second part, these limitations are mitigated employing a GPU-based rendering of Euler diagrams and mixing textures and colors to resolve overlapping regions better. The GPU-based approach subdivides the screen into triangles on which it performs a contour interpolation, i.e. a fragment shader determines for each pixel which zones of an Euler diagram it belongs to. The rendering speed is thus increased to allow multiple hundred items. The method is applied to an example comparing different document clustering results. The contour tree compactly describes scalar field topology. From the viewpoint of graph drawing, it is a tree with attributes at vertices and optionally on edges. Standard tree drawing algorithms emphasize structural properties of the tree and neglect the attributes. Adapting popular graph drawing approaches to the problem of contour tree drawing it is found that they are unable to convey this information. Five aesthetic criteria for drawing contour trees are proposed and a novel algorithm for drawing contour trees in the plane that satisfies four of these criteria is presented. The implementation is fast and effective for contour tree sizes usually used in interactive systems and also produces readable pictures for larger trees. Dynamical models that explain the formation of spatial structures of RNA molecules have reached a complexity that requires novel visualization methods to analyze these model\''s validity. The fourth part of the thesis focuses on the visualization of so-called folding landscapes of a growing RNA molecule. Folding landscapes describe the energy of a molecule as a function of its spatial configuration; they are huge and high dimensional. Their most salient features are described by their so-called barrier tree -- a contour tree for discrete observation spaces. The changing folding landscapes of a growing RNA chain are visualized as an animation of the corresponding barrier tree sequence. The animation is created as an adaption of the foresight layout with tolerance algorithm for dynamic graph layout. The adaptation requires changes to the concept of supergraph and it layout. The thesis finishes with some thoughts on how these approaches can be combined and how the task the application should support can help inform the choice of visualization modality

VMap: An Interactive Rectangular Space-filling Visualization for Map-like Vertex-centric Graph Exploration

We present VMap, a map-like rectangular space-filling visualization, to perform vertex-centric graph exploration. Existing visualizations have limited support for quality optimization among rectangular aspect ratios, vertex-edge intersection, and data encoding accuracy. To tackle this problem, VMap integrates three novel components: (1) a desired-aspect-ratio (DAR) rectangular partitioning algorithm, (2) a two-stage rectangle adjustment algorithm, and (3) a simulated annealing based heuristic optimizer. First, to generate a rectangular space-filling layout of an input graph, we subdivide the 2D embedding of the graph into rectangles with optimization of rectangles' aspect ratios toward a desired aspect ratio. Second, to route graph edges between rectangles without vertex-edge occlusion, we devise a two-stage algorithm to adjust a rectangular layout to insert border space between rectangles. Third, to produce and arrange rectangles by considering multiple visual criteria, we design a simulated annealing based heuristic optimization to adjust vertices' 2D embedding to support trade-offs among aspect ratio quality and the encoding accuracy of vertices' weights and adjacency. We evaluated the effectiveness of VMap on both synthetic and application datasets. The resulting rectangular layout has better aspect ratio quality on synthetic data compared with the existing method for the rectangular partitioning of 2D points. On three real-world datasets, VMap achieved better encoding accuracy and attained faster generation speed compared with existing methods on graphs' rectangular layout generation. We further illustrate the usefulness of VMap for vertex-centric graph exploration through three case studies on visualizing social networks, representing academic communities, and displaying geographic information.Comment: Submitted to IEEE Visualization Conference (IEEE VIS) 2019 and 202

Visualizing multidimensional data similarities:Improvements and applications

Multidimensional data is increasingly more prominent and important in many application domains. Such data typically consist of a large set of elements, each of which described by several measurements (dimensions). During the design of techniques and tools to process this data, a key component is to gather insights into their structure and patterns, which can be described by the notion of similarity between elements. Among these techniques, multidimensional projections and similarity trees can effectively capture similarity patterns and handle a large number of data elements and dimensions. However, understanding and interpreting these patterns in terms of the original data dimensions is still hard. This thesis addresses the development of visual explanatory techniques for the easy interpretation of similarity patterns present in multidimensional projections and similarity trees, by several contributions. First, we propose methods that make the computation of similarity trees efficient for large datasets, and also enhance its visual representation to allow the exploration of more data in a limited screen. Secondly, we propose methods for the visual explanation of multidimensional projections in terms of groups of similar elements. These are automatically annotated to describe which dimensions are more important to define their notion of group similarity. We show next how these explanatory mechanisms can be adapted to handle both static and time-dependent data. Our proposed techniques are designed to be easy to use, work nearly automatically, and are demonstrated on a variety of real-world large data obtained from image collections, text archives, scientific measurements, and software engineering

Explorative Graph Visualization

Netzwerkstrukturen (Graphen) sind heutzutage weit verbreitet. Ihre Untersuchung dient dazu, ein besseres Verständnis ihrer Struktur und der durch sie modellierten realen Aspekte zu gewinnen. Die Exploration solcher Netzwerke wird zumeist mit Visualisierungstechniken unterstützt. Ziel dieser Arbeit ist es, einen Überblick über die Probleme dieser Visualisierungen zu geben und konkrete Lösungsansätze aufzuzeigen. Dabei werden neue Visualisierungstechniken eingeführt, um den Nutzen der geführten Diskussion für die explorative Graphvisualisierung am konkreten Beispiel zu belegen.Network structures (graphs) have become a natural part of everyday life and their analysis helps to gain an understanding of their inherent structure and the real-world aspects thereby expressed. The exploration of graphs is largely supported and driven by visual means. The aim of this thesis is to give a comprehensive view on the problems associated with these visual means and to detail concrete solution approaches for them. Concrete visualization techniques are introduced to underline the value of this comprehensive discussion for supporting explorative graph visualization

트리 구조를 이용한 3차원 공간 내 데이터 시각화 연구

학위논문 (박사)-- 서울대학교 대학원 : 미술대학 디자인학부 디자인전공, 2019. 2. 김수정.Speculative visualization combines both data visualization methods and aesthetics to draw attention to specific social, political and environmental issues. The speculative data visualization project proposed in this work explores electronic waste trade and the environmental performance of various nations. Illegal trading of electronic waste without proper disposal and recycling measures has a severe impact on both human health and the environment. This trade can be represented as a network data structure. The overall environmental health and ecosystem vitality of those trading countries, represented by their Environmental Performance Index (EPI), can also give greater insight into this issue. This EPI data has a hierarchical structure. This work explores methods to visualize these two data sets simultaneously in a manner that allows for analytical exploration of the data while communicating its underlying meaning. This project-based design research specifically focuses on visualizing hierarchical datasets with a node-link type tree structure and suggests a novel data visualization method, called the data garden, to visualize these hierarchical datasets within a spatial network. This draws inspiration from networks found between trees in nature. This is applied to the illegal e-waste trade and environmental datasets to provoke discussion, provide a holistic understanding and improve the peoples awareness on these issues. This uses both analytical data visualization techniques, along with a more aesthetic approach. The data garden approach is used to create a 3D interactive data visualization that users can use to navigate and explore the data in a meaningful way while also providing an emotional connection to the subject. This is due to the ability of the data garden approach to accurately show the underlying data while also closely mimicking natural structures. The visualization project intends to encourage creative professionals to create both visually appealing and thought-provoking data visualizations on significant issues that can reach a mass audience and improve awareness of citizens. Additionally, this design research intends to cause further discussion on the role of aesthetics and creative practices in data visualizations.사변적 시각화(speculative visualization)는 데이터 시각화 방법과 미학을 결합하여 특정한 사회, 정치 및 환경 문제에 관심을 유도하는 것입니다. 제안한 사변적 데이터 시각화 프로젝트를 통해 다양한 국가의 전자 폐기물 거래와 환경 성과를 살펴봅니다. 적절한 처리와 재활용 조치가 이뤄지지 않은 전자폐기물의 불법 거래는 환경과 인간에 심각한 영향을 미칩니다. 이 거래는 네트워크 데이터 구조로 표현할 수 있습니다. 환경성과지수(EPI)를 통해 이 거래에 참여하는 국가들의 전반적인 환경 보건과 생태계 활력을 살펴보는 것은 이 문제에 더 깊은 통찰력을 제공할 수 있습니다. 이 환경성과지수는 계층 구조로 되어 있습니다. 이 연구는 데이터를 분석적으로 탐구할 수 있도록 하는 방법을 통해 두 가지 데이터를 동시에 시각화하고, 이를 통해 표면에 드러나지 않는 데이터의 의미를 전달하는 방법을 탐구합니다. 본 연구는 프로젝트를 기반으로 하는 디자인 연구로, 노드 링크 유형 트리 구조를 통해 계층적 데이터를 시각화하는 것에 중점을 두고 있습니다. 자연에서 발견할 수 있는 나무 간 네트워크에서 영감을 얻어 공간 네트워크에서 계층적 데이터 세트를 시각화합니다. 데이터 정원이라고 하는 이 새로운 데이터 시각화 방법을 불법 전자 폐기물 거래와 환경 데이터에 적용하여 토론을 유발하고 전체적인 이해를 제공하며 이러한 문제에 대한 사람들의 인식을 개선하고자 합니다. 이는 보다 미적인 접근과 분석적 데이터 시각화 기술을 모두 사용합니다. 데이터 정원을 통한 접근으로 삼차원 대화형 데이터 시각화를 만들 수 있습니다. 이 시각화를 통해 사용자는 데이터를 의미 있는 방식으로 살펴보는 동시에 주제와 감성적인 연결을 받을 수 있습니다. 이는 데이터 정원 방법이 데이터를 정확하게 보여주는 동시에 자연 구조를 면밀하게 모방하기 때문입니다. 본 시각화 프로젝트는 창의적인 전문가들이 중요한 문제에 대해 시각적으로 매력적이고 생각을 자극하는 데이터 시각화를 만들어 대중에게 도달하고 시민들의 인식을 향상할 수 있도록 권장합니다. 또한, 본 디자인 연구는 데이터 시각화에서 미학과 창조적인 실천의 역할에 대한 더 많은 논의를 유도하고자 합니다.Abstract I Table of Contents III List of Figures VI 1. Introduction 1 1.1 Research Background 2 1.2 Research Goal and Method 6 1.3 Terminology 9 2. Hierarchical Relationships: Trees 14 2.1 The History of Tree Diagrams 16 2.1.1 Significance of Trees 16 2.1.2 Aristotles Hierarchical Order of Life 19 2.1.3 Early Religious Depictions of Hierarchical Structures 22 2.1.4 Depicting Evolution 26 2.2 Tree Structures 29 2.3 Tree Layouts 31 3. Complex Relationships: Networks 34 3.1 Attributes of Networks 36 3.1.1 Interdependence and Interconnectedness 38 3.1.2 Decentralization 42 3.1.3 Nonlinearity 45 3.1.4 Multiplicity 46 3.2 Spatial Networks 46 3.3 Combining Tree Structures and Networks 48 4. Design Study Goals and Criteria 51 4.1 Objectives of the Design Study 71 4.2 Data Visualization Approaches 54 4.3 Criteria of Data Visualization 57 4.3.1 Aesthetics 58 4.3.2 Information Visualization Principles 62 4.3.2.1 Visual Cues in Data Visualization 62 4.3.2.2 Gestalt Principles 65 4.3.2.3 Increasing Efficiency of Network Visualizations 67 4.4 Case Study 70 5. Design Study: Data Garden Method 78 5.1 Concept of the Data Garden Structure 79 5.2 Data Garden Tree Structure 84 5.2.1 360°Vertical Branches 85 5.2.2 Break Point of the Branches 87 5.2.3 Aligning Hierarchy Levels 89 5.2.3.1 Design 01 – Extend Method 90 5.2.3.2 Design 02 – Collapse Method 91 5.2.4 Node Placement Technique 92 5.3 Conveying 3D Information 95 6. Design Study: Visualization Project 98 6.1 Theme 99 6.1.1 E-waste Trade 100 6.1.2 Environmental Performance Index 102 6.2 Visual Design Concept 104 6.3 Assigning Attributes 105 6.4 Visual Design Process 107 6.4.1 Leaf (Node) Design Process 107 6.4.1.1 Leaf Inspiration 107 6.4.1.2 Leaf Design 108 6.4.1.3 Leaf Area Calculation and Alignment 113 6.4.2 Stem (Branch) Design Process 116 6.4.3 Root (Link) Design Process 117 6.5 Interaction Design 118 6.5.1 Navigation 118 6.5.2 User Interface 119 6.5.3 Free and Detail Modes 120 6.5.4 Data Details 121 6.6 Visualization Renders 122 6.7 Exhibition 129 7. Conclusion 131 7.1 Conclusion 132 7.2 Limitations and Further Research 133 Bibliography 135 국문초록 (Abstract in Korean) 144Docto

Design of Interactive Visualizations for Next-Generation Ultra-Large Communication Networks

With the increasing size and complexity of next-generation communication networks, it is critical to utilize interactive information visualization to support the network monitoring, planning, and management. Effectively visualizing large-scale networks has been considered difficult with traditional methods because of the high link density and complicated node relationship. Given the limited screen space, it is essential to explore how to present ultra-large-scale network data efficiently that can assist Internet Service Provider’s (ISP) network planning and management activities. This work proposes a design of the real-time interactive visualization system that combines the idea of progressive disclosure and multiple panels to elegantly visualize the large-scale network and avoid the information-overloaded problem. The system also visualizes the configuration of the network elements and provides the network performance information, including the port-level Quality of Service (QoS) metrics. Additionally, the system enables navigation through the port-level connection and provides different modes for multiple purposes

Design of Interactive Visualizations for Next-Generation Ultra-Large Communication Networks

© 2013 IEEE. With the increasing size and complexity of next-generation communication networks, it is critical to utilize interactive visualizations to support the monitoring, planning, and management of networks. Effectively visualizing large-scale networks is difficult with traditional methods because of the high link density and complex node relationships. Given the limited screen space, to assist Internet Service Provider\u27s (ISP) network planning and management activities, investigating how to present ultra-large-scale network data efficiently is crucial. This paper presents a real-Time interactive visualization system that combines the design strategies of progressive disclosure and multiple panels to elegantly visualize the large-scale networks and avoid the information-overload problem. The system also visualizes the configuration of the network elements and provides the network performance information, including the port-level Quality of Service (QoS) metrics. Furthermore, the system enables navigation through the port-level connection and provides different modes for multiple purposes