Eye Tracking Support for Visual Analytics Systems

Visual analytics (VA) research provides helpful solutions for interactive visual data analysis when exploring large and complex datasets. Due to recent advances in eye tracking technology, promising opportunities arise to extend these traditional VA approaches. Therefore, we discuss foundations for eye tracking support in VA systems. We first review and discuss the structure and range of typical VA systems. Based on a widely used VA model, we present five comprehensive examples that cover a wide range of usage scenarios. Then, we demonstrate that the VA model can be used to systematically explore how concrete VA systems could be extended with eye tracking, to create supportive and adaptive analytics systems. This allows us to identify general research and application opportunities, and classify them into research themes. In a call for action, we map the road for future research to broaden the use of eye tracking and advance visual analytics

Guidance in the human–machine analytics process

In this paper, we list the goals for and the pros and cons of guidance, and we discuss the role that it can play not only in key low-level visualization tasks but also the more sophisticated model-generation tasks of visual analytics. Recent advances in artificial intelligence, particularly in machine learning, have led to high hopes regarding the possibilities of using automatic techniques to perform some of the tasks that are currently done manually using visualization by data analysts. However, visual analytics remains a complex activity, combining many different subtasks. Some of these tasks are relatively low-level, and it is clear how automation could play a role—for example, classification and clustering of data. Other tasks are much more abstract and require significant human creativity, for example, linking insights gleaned from a variety of disparate and heterogeneous data artifacts to build support for decision making. In this paper, we outline the potential applications of guidance, as well as the inputs to guidance. We discuss challenges in implementing guidance, including the inputs to guidance systems and how to provide guidance to users. We propose potential methods for evaluating the quality of guidance at different phases in the analytic process and introduce the potential negative effects of guidance as a source of bias in analytic decision making

Towards a Task-based Guidance in Exploratory Visual Analytics

Exploring large datasets and identifying meaningful information is still an active topic in many application fields. Dealing with large datasets is currently not only a matter of simply collecting and structuring data for retrieval, but sometimes it also requires the provision of adequate means for guiding the user through the exploration process. Visualizations have shown to be an effective method in this context, the reason being that since they are grounded on visual cognition, people understand them and can naturally perform visual operations such as clustering, filtering and comparing quantities. However, systems which help us to create visualizations often require specific knowledge in data analysis, which ordinary users typically do not possess. To address this gap, we propose a system that guides the user in the data analysis process. To achieve this, the system observes current user behavior, tries to infer the task of the user and recommends the next analysis steps to help her to carry out the task

Contributions to the cornerstones of interaction in visualization: strengthening the interaction of visualization

Visualization has become an accepted means for data exploration and analysis. Although interaction is an important component of visualization approaches, current visualization research pays less attention to interaction than to aspects of the graphical representation. Therefore, the goal of this work is to strengthen the interaction side of visualization. To this end, we establish a unified view on interaction in visualization. This unified view covers four cornerstones: the data, the tasks, the technology, and the human.Visualisierung hat sich zu einem unverzichtbaren Werkzeug für die Exploration und Analyse von Daten entwickelt. Obwohl Interaktion ein wichtiger Bestandteil solcher Werkzeuge ist, wird der Interaktion in der aktuellen Visualisierungsforschung weniger Aufmerksamkeit gewidmet als Aspekten der graphischen Repräsentation. Daher ist es das Ziel dieser Arbeit, die Interaktion im Bereich der Visualisierung zu stärken. Hierzu wird eine einheitliche Sicht auf Interaktion in der Visualisierung entwickelt

시각화 초심자에게 시각적 비교를 돕는 정보 시각화 기술의 디자인

학위논문(박사)--서울대학교 대학원 :공과대학 컴퓨터공학부,2020. 2. 서진욱.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 novices⁠—general 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.시각적 비교는 정보 시각화를 이용한 핵심적인 데이터 분석 과정 중 하나로써, 분산되어 있는 정보들을 사람들이 서로 정리, 평가, 병합할 수 있도록 돕는다. 예를 들어, 사람들은 시간의 흐름에 따른 데이터의 변화를 보거나, 서로 다른 출처의 데이터를 비교하거나, 같은 데이터를 여러 분석 모델들을 이용해 평가하기 위해 시각적 비교 과업을 흔히 수행하게 된다. 효과적인 시각화 디자인을 위한 여러 연구가 정보 시각화 분야에서 이루어지고 있는 반면, 어떤 디자인을 통해 효과적으로 시각적 비교를 지원할 수 있는지에 대한 이해는 다음의 제약들로 인해 아직까지 불분명하다. (1) 경험적 통찰들과 실용적 설계 지침들이 파편화되어 있으며 (2) 비교 시각화를 지원하는 방법을 이해하기 위한 사용자 실험의 수가 여전히 제한적이다. 본 논문에서는 시각화 초심자들에게 효과적으로 시각적 비교를 지원하기 위한 정보 시각화 디자인 방법을 더 깊이 이해하기 위해서 일련의 세 연구를 진행하고 이에 대한 결과를 제시한다. 특별히, 시각화 초심자들이 시각적 비교를 할 때 어려움을 경험할 수 있는 두 주요 시각화 단계를 확인함으로써, 본 연구에서는 시각적 인코딩 비교 (인코딩 장벽) 및 정보 비교 (해석 장벽) 과업들에 초점을 맞춘다. 첫째, 비교 시각화 디자인을 제시한 문헌들(N = 104)을 체계적으로 조사 및 분석함으로써 시각화 연구자들이 사용자 실험과 시각화 설계 과정을 통해 얻은 실용적 통찰들을 정리하였다. 이 문헌조사를 기반으로 비교 시각화 설계에 대한 지침들을 정립하고, 비교 시각화를 위한 디자인 공간을 더 깊이 이해하고 탐색하는 데 도움을 줄 수 있는 시각화 분류 및 예시들을 제공한다. 둘째, 초심자들이 시각화 추천 인터페이스에서 어떻게 새로운 시각적 인코딩들을 서로 비교하고 사용하는지에 대한 이해를 돕기 위해 사용자 실험(N = 24)을 수행하였다. 이 실험의 결과를 기반으로, 초심자들의 주요 어려움들과 이들을 해결하기 위한 디자인 지침들을 제시한다. 셋째, 생명정보학자가 시각적으로 다수 개의 클러스터링 결과들을 비교 및 분석할 수 있도록 도와주는 시각화 시스템, XCluSim을 디자인하고 구현하는 디자인 스터디를 수행하였다. 사례 연구를 통해 실제로 생명정보학자가 XCluSim을 이용하여 많은 클러스터링 결과들을 쉽게 비교 및 평가할 수 있다는 것을 보였다. 마지막으로, 이 세 연구 결과들을 기반으로 비교 시각화 분야에서 유망한 향후 연구들을 제시한다.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

Content-prioritised video coding for British Sign Language communication.

Video communication of British Sign Language (BSL) is important for remote interpersonal communication and for the equal provision of services for deaf people. However, the use of video telephony and video conferencing applications for BSL communication is limited by inadequate video quality. BSL is a highly structured, linguistically complete, natural language system that expresses vocabulary and grammar visually and spatially using a complex combination of facial expressions (such as eyebrow movements, eye blinks and mouth/lip shapes), hand gestures, body movements and finger-spelling that change in space and time. Accurate natural BSL communication places specific demands on visual media applications which must compress video image data for efficient transmission. Current video compression schemes apply methods to reduce statistical redundancy and perceptual irrelevance in video image data based on a general model of Human Visual System (HVS) sensitivities. This thesis presents novel video image coding methods developed to achieve the conflicting requirements for high image quality and efficient coding. Novel methods of prioritising visually important video image content for optimised video coding are developed to exploit the HVS spatial and temporal response mechanisms of BSL users (determined by Eye Movement Tracking) and the characteristics of BSL video image content. The methods implement an accurate model of HVS foveation, applied in the spatial and temporal domains, at the pre-processing stage of a current standard-based system (H.264). Comparison of the performance of the developed and standard coding systems, using methods of video quality evaluation developed for this thesis, demonstrates improved perceived quality at low bit rates. BSL users, broadcasters and service providers benefit from the perception of high quality video over a range of available transmission bandwidths. The research community benefits from a new approach to video coding optimisation and better understanding of the communication needs of deaf people

iBall: Augmenting Basketball Videos with Gaze-moderated Embedded Visualizations

We present iBall, a basketball video-watching system that leverages gaze-moderated embedded visualizations to facilitate game understanding and engagement of casual fans. Video broadcasting and online video platforms make watching basketball games increasingly accessible. Yet, for new or casual fans, watching basketball videos is often confusing due to their limited basketball knowledge and the lack of accessible, on-demand information to resolve their confusion. To assist casual fans in watching basketball videos, we compared the game-watching behaviors of casual and die-hard fans in a formative study and developed iBall based on the fndings. iBall embeds visualizations into basketball videos using a computer vision pipeline, and automatically adapts the visualizations based on the game context and users' gaze, helping casual fans appreciate basketball games without being overwhelmed. We confrmed the usefulness, usability, and engagement of iBall in a study with 16 casual fans, and further collected feedback from 8 die-hard fans.Comment: ACM CHI2

ARTYCUL: A Privacy-Preserving ML-Driven Framework to Determine the Popularity of a Cultural Exhibit on Display.

We present ARTYCUL (ARTifact popularitY for CULtural heritage), a machine learning(ML)-based framework that graphically represents the footfall around an artifact on display at a museum or a heritage site. The driving factor of this framework was the fact that the presence of security cameras has become universal, including at sites of cultural heritage. ARTYCUL used the video streams of closed-circuit televisions (CCTV) cameras installed in such premises to detect human figures, and their coordinates with respect to the camera frames were used to visualize the density of visitors around the specific display items. Such a framework that can display the popularity of artifacts would aid the curators towards a more optimal organization. Moreover, it could also help to gauge if a certain display item were neglected due to incorrect placement. While items of similar interest can be placed in vicinity of each other, an online recommendation system may also use the reputation of an artifact to catch the eye of the visitors. Artificial intelligence-based solutions are well suited for analysis of internet of things (IoT) traffic due to the inherent veracity and volatile nature of the transmissions. The work done for the development of ARTYCUL provided a deeper insight into the avenues for applications of IoT technology to the cultural heritage domain, and suitability of ML to process real-time data at a fast pace. While we also observed common issues that hinder the utilization of IoT in the cultural domain, the proposed framework was designed keeping in mind the same obstacles and a preference for backward compatibility

A Survey of Information Visualization Books

Information visualization is a rapidly evolving field with a growing volume of scientific literature and texts continually published.To keep abreast of the latest developments in the domain, survey papers and state-of-the-art reviews provide valuable tools formanaging the large quantity of scientific literature. Recently a survey of survey papers (SoS) was published to keep track ofthe quantity of refereed survey papers in information visualization conferences and journals. However no such resources existto inform readers of the large volume of books being published on the subject, leaving the possibility of valuable knowledgebeing overlooked. We present the first literature survey of information visualization books that addresses this challenge bysurveying the large volume of books on the topic of information visualization and visual analytics. This unique survey addressessome special challenges associated with collections of books (as opposed to research papers) including searching, browsingand cost. This paper features a novel two-level classification based on both books and chapter topics examined in each book,enabling the reader to quickly identify to what depth a topic of interest is covered within a particular book. Readers can usethis survey to identify the most relevant book for their needs amongst a quickly expanding collection. In indexing the landscapeof information visualization books, this survey provides a valuable resource to both experienced researchers and newcomers inthe data visualization discipline