EuroVA2021

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https://diglib.eg.org/handle/10.2312/2633050

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Customizable Coordination of Independent Visual Analytics Tools
(The Eurographics Association, 2021) Nonnemann, Lars; Hogräfer, Marius; Schumann, Heidrun; Urban, Bodo; Schulz, Hans-Jörg; Vrotsou, Katerina and Bernard, Jürgen
While it is common to use multiple independent analysis tools in combination, it is still cumbersome to carry out a cross-tool visual analysis. Some dedicated frameworks addressing this issue exist, yet in order to use them, a Visual Analytics tool must support their API or architecture. In this paper, we do not rely on a single predetermined exchange mechanism for the whole ensemble of VA tools. Instead, we propose using any available channel for exchanging data between two subsequently used VA tools. This effectively allows to mix and match different data exchange strategies within one cross-tool analysis, which considerably reduces the overhead of adding a new VA tool to a given tool ensemble. We demonstrate our approach with a first implementation called AnyProc and its application to a use case of three VA tools in a Health IT data analysis scenario.
A Taxonomy of Attribute Scoring Functions
(The Eurographics Association, 2021) Schmid, Jenny; Bernard, Jürgen; Vrotsou, Katerina and Bernard, Jürgen
Shifting the analysis from items to the granularity of attributes is a promising approach to address complex decision-making problems. In this work, we study attribute scoring functions (ASFs), which transform values from data attributes to numerical scores. As the output of ASFs for different attributes is always comparable and scores carry user preferences, ASFs are particularly useful for analysis goals such as multi-attribute ranking, multi-criteria optimization, or similarity modeling. However, non-programmers cannot yet fully leverage their individual preferences on attribute values, as visual analytics (VA) support for the creation of ASFs is still in its infancy, and guidelines for the creation of ASFs are missing almost entirely. We present a taxonomy of eight types of ASFs and an overview of tools for the creation of ASFs as a result of an extensive literature review. Both the taxonomy and the tools overview have descriptive power, as they represent and combine non-visual math and statistics perspectives with the VA perspective. We underpin the usefulness of VA support for broader user groups in real-world cases for all eight types of ASFs, unveil missing VA support for the ASF creation, and discuss the integration of ASF in VA workflows.
Multi-resolution Analysis for Vector Plots of Time Series Data
(The Eurographics Association, 2021) Nguyen, Bao; Hewett, Rattikorn; Dang, Tommy; Vrotsou, Katerina and Bernard, Jürgen
Vector plots can directly visualize both temporal variation and spatial distribution, so it is interesting to use this type of plot for displaying multivariate time series. However, vector plots cannot reveal global temporal information. This paper introduces an interactive visualization that allows comparisons between different resolutions for easing this limit. The proposed approach is applied to two real data to demonstrate its benefits and potential.
EuroVa 2021: Frontmatter
(The Eurographics Association, 2021) Bernard, Jürgen; Vrotsou, Katerina; Vrotsou, Katerina and Bernard, Jürgen
Rumble Flow++ Interactive Visual Analysis of Dota2 Encounters
(The Eurographics Association, 2021) Weixelbaum, Wilma; Matkovic, Kresimir; Vrotsou, Katerina and Bernard, Jürgen
In the last decade, the popularity of ESports has grown rapidly. The financial leader in the tournament scene is Dota2, a complex and strategic multiplayer game. Analysis and exploration of game data could lead to better outcomes. Available data resources include the combat log, which logs every event at an atomic level and excels at providing great detail at the expense of readability, and concise third-party summaries that provide little detail. In this paper, we introduce Rumble Flow++, a web-based exploratory analysis application that provides details in an easy-to-understand manner while providing meaningful aggregations. Rumble Flow++ supports exploration and analysis at different levels of granularity. It supports analysis at the level of the entire match, at the level of individual team fights, and at the level of individual heroes. The user can easily switch between levels in a fully interactive environment. Rumble Flow++ provides much more detail than a summary visualization typically uses, and much better readability than an atomic log file.
LFPeers: Temporal Similarity Search in Covid-19 Data
(The Eurographics Association, 2021) Burmeister, Jan; Bernard, Jürgen; Kohlhammer, Jörn; Vrotsou, Katerina and Bernard, Jürgen
While there is a wide variety of visualizations and dashboards to help understand the data of the Covid-19 pandemic, hardly any of these support important analytical tasks, especially of temporal attributes. In this paper, we introduce a general concept for the analysis of temporal and multimodal data and the system LFPeers that applies this concept to the analysis of countries in a Covid-19 dataset. Our concept divides the analysis in two phases: a search phase to find the most similar objects to a target object before a time point t0, and an exploration phase to analyze this subset of objects after t0. LFPeers targets epidemiologists and the public who want to learn from the Covid-19 pandemic and distinguish successful and ineffective measures.
Immersive 3D Visualization of Multi-Modal Brain Connectivity
(The Eurographics Association, 2021) Pester, Britta; Winke, Oliver; Ligges, Carolin; Dachselt, Raimund; Gumhold, Stefan; Vrotsou, Katerina and Bernard, Jürgen
In neuroscience, the investigation of connectivity between different brain regions suffers from the lack of adequate solutions for visualizing detected networks. One reason is the high number of dimensions that have to be combined within the same view: neuroscientists examine brain connectivity in its natural spatial context across the additional dimensions time and frequency. To combine all these dimensions without prior merging or filtering steps, we propose a visualization in virtual reality to realize multiple coordinated views of the networks in a virtual visual analysis lab. We implemented a prototype of the new idea. In a first qualitative user study we included experts in the field of computer science, psychology as well as neuroscience. Time series of electroencephalography recordings evoked by visual stimuli were used to provide a first proof of concept trial.The positive user feedback shows that our application successfully fills a gap in the visualization of high-dimensional brain networks.
Immersive Analytics of Heterogeneous Biological Data Informed through Need-finding Interviews
(The Eurographics Association, 2021) Ripken, Christine; Tusk, Sebastian; Tominski, Christian; Vrotsou, Katerina and Bernard, Jürgen
The goal of this work is to improve existing biological analysis processes by means of immersive analytics. In a first step, we conducted need-finding interviews with 12 expert biologists to understand the limits of current practices and identify the requirements for an enhanced immersive analysis. Based on the gained insights, a novel immersive analytics solution is being developed that enables biologists to explore highly interrelated biological data, including genomes, transcriptomes, and phenomes. We use an abstract tabular representation of heterogeneous data projected onto a curved virtual wall. Several visual and interactive mechanisms are offered to allow biologists to get an overview of large data, to access details and additional information on the fly, to compare selected parts of the data, and to navigate up to about 5 million data values in real-time. Although a formal user evaluation is still pending, initial feedback indicates that our solution can be useful to expert biologists.
Lessons learned while supporting Cyber Situational Awareness
(The Eurographics Association, 2021) Blasilli, Graziano; Paoli, Emiliano De; Lenti, Simone; Picca, Sergio; Vrotsou, Katerina and Bernard, Jürgen
The increasing number of cyberattacks against critical infrastructures has pushed researchers to develop many Visual Analytics solutions to provide valid defensive approaches and improve the situational awareness of the security operators. Applying such solutions to complex infrastructures is often challenging, and existing tools can present limitations and exhibit various issues. In this paper, supported by cybersecurity experts of a world leader company in the military domain, we apply an existing Visual Analytics solution, MAD, to a complex network of a critical infrastructure, highlighting its limitations in this scenario and proposing further solutions to improve the cyber situational awareness in both proactive and reactive risk analyses. The results of this research contribute to characterize the activities performed by domain experts in this domain and their implications for the design of Visual Analytics solutions that aim at supporting them.
Towards the Detection and Visual Analysis of COVID-19 Infection Clusters
(The Eurographics Association, 2021) Antweiler, Dario; Sessler, David; Ginzel, Sebastian; Kohlhammer, Jörn; Vrotsou, Katerina and Bernard, Jürgen
A major challenge for departments of public health (DPHs) in dealing with the ongoing COVID-19 pandemic is tracing contacts in exponentially growing SARS-CoV2 infection clusters. Prevention of further disease spread requires a comprehensive registration of the connections between individuals and clusters. Due to the high number of infections with unknown origin, the healthcare analysts need to identify connected cases and clusters through accumulated epidemiological knowledge and the metadata of the infections in their database. Here we contribute a visual analytics framework to identify, assess and visualize clusters in COVID-19 contact tracing networks. Additionally, we demonstrate how graph-based machine learning methods can be used to find missing links between infection clusters and thus support the mission to get a comprehensive view on infection events. This work was developed through close collaboration with DPHs in Germany. We argue how our systems supports the identification of clusters by public health experts and discuss ongoing developments and possible extensions.
Talk2Hand: Knowledge Board Interaction in Augmented Reality Easing Analysis with Machine Learning Assistants
(The Eurographics Association, 2021) Hong, Yu-Lun; Watson, Benjamin; Thompson, Kenneth; Davis, Paul; Vrotsou, Katerina and Bernard, Jürgen
Analysts now often use machine learning (ML) assistants, but find them difficult to use, since most have little ML expertise. Talk2Hand improves the usability of ML assistants by supporting interaction with them using knowledge boards, which intuitively show association, visually aid human recall, and offer natural interaction that eases improvement of displayed associations and addition of new data into emerging models. Knowledge boards are familiar to most and studied by analytics researchers, but not in wide use, because of their large size and the challenges of using them for several projects simultaneously. Talk2Hand uses augmented reality to address these shortcomings, overlaying large but virtual knowledge boards onto typical analyst offices, and enabling analysts to switch easily between different knowledge boards. This paper describes our Talk2Hand prototype.

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