EuroVisSTAR2020

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https://diglib.eg.org/handle/10.2312/2632896

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Privacy-Preserving Data Visualization: Reflections on the State of the Art and Research Opportunities
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Bhattacharjee, Kaustav; Chen, Min; Dasgupta, Aritra; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, Bei
Preservation of data privacy and protection of sensitive information from potential adversaries constitute a key socio-technical challenge in the modern era of ubiquitous digital transformation. Addressing this challenge needs analysis of multiple factors: algorithmic choices for balancing privacy and loss of utility, potential attack scenarios that can be undertaken by adversaries, implications for data owners, data subjects, and data sharing policies, and access control mechanisms that need to be built into interactive data interfaces. Visualization has a key role to play as part of the solution space, both as a medium of privacy-aware information communication and also as a tool for understanding the link between privacy parameters and data sharing policies. The field of privacy-preserving data visualization has witnessed progress along many of these dimensions. In this state-of-theart report, our goal is to provide a systematic analysis of the approaches, methods, and techniques used for handling data privacy in visualization. We also reflect on the road-map ahead by analyzing the gaps and research opportunities for solving some of the pressing socio-technical challenges involving data privacy with the help of visualization.
Survey on Individual Differences in Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Liu, Zhengliang; Crouser, R. Jordan; Ottley, Alvitta; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, Bei
Developments in data visualization research have enabled visualization systems to achieve great general usability and application across a variety of domains. These advancements have improved not only people's understanding of data, but also the general understanding of people themselves, and how they interact with visualization systems. In particular, researchers have gradually come to recognize the deficiency of having one-size-fits-all visualization interfaces, as well as the significance of individual differences in the use of data visualization systems. Unfortunately, the absence of comprehensive surveys of the existing literature impedes the development of this research. In this paper, we review the research perspectives, as well as the personality traits and cognitive abilities, visualizations, tasks, and measures investigated in the existing literature. We aim to provide a detailed summary of existing scholarship, produce evidence-based reviews, and spur future inquiry.
Survey on the Analysis of User Interactions and Visualization Provenance
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Xu, Kai; Ottley, Alvitta; Walchshofer, Conny; Streit, Marc; Chang, Remco; Wenskovitch, John; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, Bei
There is fast-growing literature on provenance-related research, covering aspects such as its theoretical framework, use cases, and techniques for capturing, visualizing, and analyzing provenance data. As a result, there is an increasing need to identify and taxonomize the existing scholarship. Such an organization of the research landscape will provide a complete picture of the current state of inquiry and identify knowledge gaps or possible avenues for further investigation. In this STAR, we aim to produce a comprehensive survey of work in the data visualization and visual analytics field that focus on the analysis of user interaction and provenance data. We structure our survey around three primary questions: (1) WHY analyze provenance data, (2) WHAT provenance data to encode and how to encode it, and (3) HOW to analyze provenance data. A concluding discussion provides evidence-based guidelines and highlights concrete opportunities for future development in this emerging area.
State of the Art in Time-Dependent Flow Topology: Interpreting Physical Meaningfulness Through Mathematical Properties
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Bujack, Roxana; Yan, Lin; Hotz, Ingrid; Garth, Christoph; Wang, Bei; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, Bei
We present a state-of-the-art report on time-dependent flow topology. We survey representative papers in visualization and provide a taxonomy of existing approaches that generalize flow topology from time-independent to time-dependent settings. The approaches are classified based upon four categories: tracking of steady topology, reference frame adaption, pathline classification or clustering, and generalization of critical points. Our unique contributions include introducing a set of desirable mathematical properties to interpret physical meaningfulness for time-dependent flow visualization, inferring mathematical properties associated with selective research papers, and utilizing such properties for classification. The five most important properties identified in the existing literature include coincidence with the steady case, induction of a partition within the domain, Lagrangian invariance, objectivity, and Galilean invariance.
A Survey of Seed Placement and Streamline Selection Techniques
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Sane, Sudhanshu; Bujack, Roxana; Garth, Christoph; Childs, Hank; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, Bei
Streamlines are an extensively utilized flow visualization technique for understanding, verifying, and exploring computational fluid dynamics simulations. One of the major challenges associated with the technique is selecting which streamlines to display. Using a large number of streamlines results in dense, cluttered visualizations, often containing redundant information and occluding important regions, whereas using a small number of streamlines could result in missing key features of the flow. Many solutions to select a representative set of streamlines have been proposed by researchers over the past two decades. In this state-of-the-art report, we analyze and classify seed placement and streamline selection (SPSS) techniques used by the scientific flow visualization community. At a high-level, we classify techniques into automatic and manual techniques, and further divide automatic techniques into three strategies: density-based, feature-based, and similarity-based. Our analysis evaluates the identified strategy groups with respect to focus on regions of interest, minimization of redundancy, and overall computational performance. Finally, we consider the application contexts and tasks for which SPSS techniques are currently applied and have potential applications in the future.
The State of the Art in Map-Like Visualization
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Hogräfer, Marius; Heitzler, Magnus; Schulz, Hans-Jörg; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, Bei
Cartographic maps have been shown to provide cognitive benefits when interpreting data in relation to a geographic location. In visualization, the term map-like describes techniques that incorporate characteristics of cartographic maps in their representation of abstract data. However, the field of map-like visualization is vast and currently lacks a clear classification of the existing techniques. Moreover, choosing the right technique to support a particular visualization task is further complicated, as techniques are scattered across different domains, with each considering different characteristics as map-like. In this paper, we give an overview of the literature on map-like visualization and provide a hierarchical classification of existing techniques along two general perspectives: imitation and schematization of cartographic maps. Each perspective is further divided into four principal categories that group common map-like techniques along the visual primitives they affect. We further discuss this classification from a task-centered view and highlight open research questions.
EuroVis 2020 CGF 39-3 STARs: Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Smit, Noeska; Oeltze-Jafra, Steffen; Wang, Bei; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, Bei
The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Chatzimparmpas, Angelos; Martins, Rafael M.; Jusufi, Ilir; Kucher, Kostiantyn; Rossi, Fabrice; Kerren, Andreas; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, Bei
Machine learning (ML) models are nowadays used in complex applications in various domains, such as medicine, bioinformatics, and other sciences. Due to their black box nature, however, it may sometimes be hard to understand and trust the results they provide. This has increased the demand for reliable visualization tools related to enhancing trust in ML models, which has become a prominent topic of research in the visualization community over the past decades. To provide an overview and present the frontiers of current research on the topic, we present a State-of-the-Art Report (STAR) on enhancing trust in ML models with the use of interactive visualization. We define and describe the background of the topic, introduce a categorization for visualization techniques that aim to accomplish this goal, and discuss insights and opportunities for future research directions. Among our contributions is a categorization of trust against different facets of interactive ML, expanded and improved from previous research. Our results are investigated from different analytical perspectives: (a) providing a statistical overview, (b) summarizing key findings, (c) performing topic analyses, and (d) exploring the data sets used in the individual papers, all with the support of an interactive web-based survey browser. We intend this survey to be beneficial for visualization researchers whose interests involve making ML models more trustworthy, as well as researchers and practitioners from other disciplines in their search for effective visualization techniques suitable for solving their tasks with confidence and conveying meaning to their data.
A Survey on Transit Map Layout - from Design, Machine, and Human Perspectives
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Wu, Hsiang-Yun; Niedermann, Benjamin; Takahashi, Shigeo; Roberts, Maxwell J.; Nöllenburg, Martin; Smit, Noeska and Oeltze-Jafra, Steffen and Wang, Bei
Transit maps are designed to present information for using public transportation systems, such as urban railways. Creating a transit map is a time-consuming process, which requires iterative information selection, layout design, and usability validation, and thus maps cannot easily be customised or updated frequently. To improve this, scientists investigate fully- or semi-automatic techniques in order to produce high quality transit maps using computers and further examine their corresponding usability. Nonetheless, the quality gap between manually-drawn maps and machine-generated maps is still large. To elaborate the current research status, this state-of-the-art report provides an overview of the transit map generation process, primarily from Design, Machine, and Human perspectives. A systematic categorisation is introduced to describe the design pipeline, and an extensive analysis of perspectives is conducted to support the proposed taxonomy. We conclude this survey with a discussion on the current research status, open challenges, and future directions.

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