EuroVA2021

Permanent URI for this collection

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

Browse

Browsing EuroVA2021 by Subject "Visual analytics"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.