Health Figures: An Open Source JavaScript Library for Health Data Visualization

The way we look at data has a great impact on how we can understand it, particularly when the data is related to health and wellness. Due to the increased use of self-tracking devices and the ongoing shift towards preventive medicine, better understanding of our health data is an important part of improving the general welfare of the citizens. Electronic Health Records, self-tracking devices and mobile applications provide a rich variety of data but it often becomes difficult to understand. We implemented the hFigures library inspired on the hGraph visualization with additional improvements. The purpose of the library is to provide a visual representation of the evolution of health measurements in a complete and useful manner. We researched the usefulness and usability of the library by building an application for health data visualization in a health coaching program. We performed a user evaluation with Heuristic Evaluation, Controlled User Testing and Usability Questionnaires. In the Heuristics Evaluation the average response was 6.3 out of 7 points and the Cognitive Walkthrough done by usability experts indicated no design or mismatch errors. In the CSUQ usability test the system obtained an average score of 6.13 out of 7, and in the ASQ usability test the overall satisfaction score was 6.64 out of 7. We developed hFigures, an open source library for visualizing a complete, accurate and normalized graphical representation of health data. The idea is based on the concept of the hGraph but it provides additional key features, including a comparison of multiple health measurements over time. We conducted a usability evaluation of the library as a key component of an application for health and wellness monitoring. The results indicate that the data visualization library was helpful in assisting users in understanding health data and its evolution over time.Comment: BMC Medical Informatics and Decision Making 16.1 (2016

Relay Backpropagation for Effective Learning of Deep Convolutional Neural Networks

Learning deeper convolutional neural networks becomes a tendency in recent years. However, many empirical evidences suggest that performance improvement cannot be gained by simply stacking more layers. In this paper, we consider the issue from an information theoretical perspective, and propose a novel method Relay Backpropagation, that encourages the propagation of effective information through the network in training stage. By virtue of the method, we achieved the first place in ILSVRC 2015 Scene Classification Challenge. Extensive experiments on two challenging large scale datasets demonstrate the effectiveness of our method is not restricted to a specific dataset or network architecture. Our models will be available to the research community later.Comment: Technical report for our submissions to the ILSVRC 2015 Scene Classification Challenge, where we won the first plac

The State-of-the-Art of Set Visualization

Sets comprise a generic data model that has been used in a variety of data analysis problems. Such problems involve analysing and visualizing set relations between multiple sets defined over the same collection of elements. However, visualizing sets is a non-trivial problem due to the large number of possible relations between them. We provide a systematic overview of state-of-the-art techniques for visualizing different kinds of set relations. We classify these techniques into six main categories according to the visual representations they use and the tasks they support. We compare the categories to provide guidance for choosing an appropriate technique for a given problem. Finally, we identify challenges in this area that need further research and propose possible directions to address these challenges. Further resources on set visualization are available at http://www.setviz.net

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

Applications of big knowledge summarization

Advanced technologies have resulted in the generation of large amounts of data ( Big Data ). The Big Knowledge derived from Big Data could be beyond humans\u27 ability of comprehension, which will limit the effective and innovative use of Big Knowledge repository. Biomedical ontologies, which play important roles in biomedical information systems, constitute one kind of Big Knowledge repository. Biomedical ontologies typically consist of domain knowledge assertions expressed by the semantic connections between tens of thousands of concepts. Without some high-level visual representation of Big Knowledge in biomedical ontologies, humans cannot grasp the big picture of those ontologies. Such Big Knowledge orientation is required for the proper maintenance of ontologies and their effective use. This dissertation is addressing the Big Knowledge challenge - How to enable humans to use Big Knowledge correctly and effectively (referred to as the Big Knowledge to Use (BK2U) problem) - with a focus on biomedical ontologies. In previous work, Abstraction Networks (AbNs) have been demonstrated successful for the summarization, visualization and quality assurance (QA) of biomedical ontologies. Based on the previous research, this dissertation introduces new AbNs of various granularities for Big Knowledge summarization and extends the applications of AbNs. This dissertation consists of three main parts. The first part introduces two advanced AbNs. One is the weighted aggregate partial-area taxonomy with a parameter to flexibly control the summarization granularity. The second is the Ingredient Abstraction Network (IAbN) for the National Drug File - Reference Terminology (NDF-RT) Chemical Ingredients hierarchy, for which the previously developed AbNs for hierarchies with outgoing relationships, are not applicable. Since NDF-RT\u27s Chemical Ingredients hierarchy has no outgoing relationships. The second part describes applications of the two advanced AbNs. A study utilizing the weighted aggregate partial-area taxonomy for the identification of major topics in SNOMED CT\u27s Specimen hierarchy is reported. A multi-layer interactive visualization system of required granularity for ontology comprehension, based on the weighted aggregate partial-area taxonomy, is demonstrated to comprehend the Neoplasm subhierarchy of National Cancer Institute thesaurus (NCIt). The IAbN is applied for drug-drug interaction (DDI) discovery. The third part reports eight family-based QA studies on NCIt\u27s Neoplasm, Gene, and Biological Process hierarchies, SNOMED CT\u27s Infectious disease hierarchy, the Chemical Entities of Biological Interest ontology, and the Chemical Ingredients hierarchy in NDF-RT. There is no one-size-fits-all QA method and it is impossible to find a QA method for each individual ontology. Hence, family-based QA is an effective way, i.e., one QA technique could be applicable to a whole family of structurally similar ontologies. The results of these studies demonstrate that complex concepts and uncommonly modeled concepts are more likely to have errors. Furthermore, the three studies on overlapping concepts in partial-area taxonomies reported in this dissertation combined with previous three studies prove the success of overlapping concepts as a QA methodology for a whole family of 76 similar ontologies in BioPortal