EuroVisSTAR2023

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https://diglib.eg.org/handle/10.2312/2633340

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EuroVis 2023 CGF 42-3 STARs: Frontmatter
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Bruckner, Stefan; Raidou, Renata G.; Turkay, Cagatay; Bruckner, Stefan; Raidou, Renata G.; Turkay, Cagatay
The State of the Art in Creating Visualization Corpora for Automated Chart Analysis
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Chen, Chen; Liu, Zhicheng; Bruckner, Stefan; Raidou, Renata G.; Turkay, Cagatay
We present a state-of-the-art report on visualization corpora in automated chart analysis research. We survey 56 papers that created or used a visualization corpus as the input of their research techniques or systems. Based on a multi-level task taxonomy that identifies the goal, method, and outputs of automated chart analysis, we examine the property space of existing chart corpora along five dimensions: format, scope, collection method, annotations, and diversity. Through the survey, we summarize common patterns and practices of creating chart corpora, identify research gaps and opportunities, and discuss the desired properties of future benchmark corpora and the required tools to create them.
The State of the Art in Visualizing Dynamic Multivariate Networks
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Kale, Bharat; Sun, Maoyuan; Papka, Michael E.; Bruckner, Stefan; Raidou, Renata G.; Turkay, Cagatay
Most real-world networks are both dynamic and multivariate in nature, meaning that the network is associated with various attributes and both the network structure and attributes evolve over time. Visualizing dynamic multivariate networks is of great significance to the visualization community because of their wide applications across multiple domains. However, it remains challenging because the techniques should focus on representing the network structure, attributes and their evolution concurrently. Many real-world network analysis tasks require the concurrent usage of the three aspects of the dynamic multivariate networks. In this paper, we analyze current techniques and present a taxonomy to classify the existing visualization techniques based on three aspects: temporal encoding, topology encoding, and attribute encoding. Finally, we survey application areas and evaluation methods; and discuss challenges for future research.
State-of-the-art in Large-Scale Volume Visualization Beyond Structured Data
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Sarton, Jonathan; Zellmann, Stefan; Demirci, Serkan; Güdükbay, Ugur; Alexandre-Barff, Welcome; Lucas, Laurent; Dischler, Jean-Michel; Wesner, Stefan; Wald, Ingo; Bruckner, Stefan; Raidou, Renata G.; Turkay, Cagatay
Volume data these days is usually massive in terms of its topology, multiple fields, or temporal component. With the gap between compute and memory performance widening, the memory subsystem becomes the primary bottleneck for scientific volume visualization. Simple, structured, regular representations are often infeasible because the buses and interconnects involved need to accommodate the data required for interactive rendering. In this state-of-the-art report, we review works focusing on largescale volume rendering beyond those typical structured and regular grid representations.We focus primarily on hierarchical and adaptive mesh refinement representations, unstructured meshes, and compressed representations that gained recent popularity. We review works that approach this kind of data using strategies such as out-of-core rendering, massive parallelism, and other strategies to cope with the sheer size of the ever-increasing volume of data produced by today's supercomputers and acquisition devices. We emphasize the data management side of large-scale volume rendering systems and also include a review of tools that support the various volume data types discussed.
State-of-the-Art Report on Optimizing Particle Advection Performance
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Yenpure, Abhishek; Sane, Sudhanshu; Binyahib, Roba; Pugmire, David; Garth, Christoph; Childs, Hank; Bruckner, Stefan; Raidou, Renata G.; Turkay, Cagatay
The computational work to perform particle advection-based flow visualization techniques varies based on many factors, including number of particles, duration, and mesh type. In many cases, the total work is significant, and total execution time (''performance'') is a critical issue. This state-of-the-art report considers existing optimizations for particle advection, using two high-level categories: algorithmic optimizations and hardware efficiency. The sub-categories for algorithmic optimizations include solvers, cell locators, I/O efficiency, and precomputation, while the sub-categories for hardware efficiency all involve parallelism: shared-memory, distributed-memory, and hybrid. Finally, this STAR concludes by identifying current gaps in our understanding of particle advection performance and its optimizations.
VA + Embeddings STAR: A State-of-the-Art Report on the Use of Embeddings in Visual Analytics
(The Eurographics Association and John Wiley & Sons Ltd., 2023) Huang, Zeyang; Witschard, Daniel; Kucher, Kostiantyn; Kerren, Andreas; Bruckner, Stefan; Raidou, Renata G.; Turkay, Cagatay
Over the past years, an increasing number of publications in information visualization, especially within the field of visual analytics, have mentioned the term ''embedding'' when describing the computational approach. Within this context, embeddings are usually (relatively) low-dimensional, distributed representations of various data types (such as texts or graphs), and since they have proven to be extremely useful for a variety of data analysis tasks across various disciplines and fields, they have become widely used. Existing visualization approaches aim to either support exploration and interpretation of the embedding space through visual representation and interaction, or aim to use embeddings as part of the computational pipeline for addressing downstream analytical tasks. To the best of our knowledge, this is the first survey that takes a detailed look at embedding methods through the lens of visual analytics, and the purpose of our survey article is to provide a systematic overview of the state of the art within the emerging field of embedding visualization. We design a categorization scheme for our approach, analyze the current research frontier based on peer-reviewed publications, and discuss existing trends, challenges, and potential research directions for using embeddings in the context of visual analytics. Furthermore, we provide an interactive survey browser for the collected and categorized survey data, which currently includes 122 entries that appeared between 2007 and 2023.

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