EuroVisSTAR2023

Permanent URI for this collection

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

Browse

Browsing EuroVisSTAR2023 by Subject "Computing methodologies"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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.