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Slides of the Keynote at FAIRvis (workshop at EuroVis 2023)

Exploring spatial data has emerged as a significant area of research in big data and visualization. However, working with three-dimensional data presents unique challenges, as many 3D datasets are dense, and important data features often need to be obscured or undefined. Selection is a fundamental and prerequisite step in data exploration. In this study, we will compare four spatial selection techniques for 3D point cloud data in the immersive VR environment that consider both spatial input and data distribution. We aim to investigate the potential advantages and limitations of the selection techniques in different data distribution scenarios. The finding of this study will provide valuable scientific guidelines for developing future selection techniques tailored to specific scenarios encountered by users.

We present a new way to merge any two point distribution approaches using distance fields. Our new process allows us to produce digital stippling that fills areas with stipple dots without visual artifacts as well as includes clear linear features without fussiness. Our merging thus benefits from past work that can optimize for either goal individually, yet typically by sacrificing the other. The new possibility of combining any two distributions using different distance field functions and their parameters also allows us to produce a vast range of stippling styles, which we demonstrate as well., Comment: 41 pages, 42 figures

This is the first survey of a series of surveys aiming to develop a validated scale that researchers can use to let study participants rate their perception of a visualization’s readability. In this survey, we will ask visualization experts to provide us with terms for rating the perceived readability of a visualization.

This is the second experiment in a series aimed at understanding how to design black-and-white textures for data visualization. In this experiment, we will ask the general public to rate the previously collected black-and-white texture visualization designs for aesthetics and whether they perceive a vibratory effect (Moiré effect), and rank them based on their overall preference. Our goal is to collect an overall preference for the shown designs for use in our follow-up experiment.

This is the third experiment in this series. In this experiment, we will invite the general public to participate in a chart-reading task that employs bar charts and pie charts, and rate them for aesthetics and readability. We will use the most visually appealing black-and-white texture designs that we have gathered and rated from our previous studies. The goal is to assess the effectiveness, aesthetics, and readability of the highest-rated geometric and iconic texture designs for bar charts and pie charts.

This is the first experiment of a series of experiments aiming to understand how to design black and white textures for data representations. In this experiment, we will ask visualization designers and researchers to provide us with black and white texture visualization designs and ask them for their design strategies and opinions on using texture for visualizations.

This project aims at studying three different interaction techniques to select 3D points in collaborative volumetric visualization using AR as a support.

This is the forth survey of a series of surveys aiming to develop a validated scale that researchers can use to let study participants rate the aesthetic pleasure of a visualization. In this survey, we will ask participants to use our scale and another scale that measures aesthetics to rate visualization, to confirm the reliability and validity of our scale.

This is the second survey of a series of surveys aiming to develop a validated scale that researchers can use to let study participants rate the aesthetic pleasure of a visualization. We have previously collected and curated a set of terms used to describe the aesthetics of visualization from literature and experts. In this survey, we will ask visualization experts to evaluate the relevance of these terms to the aesthetic pleasure of visualization.

This is the first survey of a series of surveys aiming to develop a validated scale that researchers can use to let study participants rate the aesthetics of a visualization. In this survey, we will ask visualization experts to provide us with terms for rating the aesthetics of a visualization.

This is the third survey of a series of surveys aiming to develop a validated scale that researchers can use to let study participants rate the aesthetic pleasure of a visualization. We have previously collected and curated a set of terms used to describe the aesthetics of visualization from literature and experts, and asked experts to evaluate this set of terms. In this survey, we will ask participants to use this set of terms to rate visualization, to find the potential factors behind these terms, and further reduce terms.

We present a manual spatial selection technique relying on a hybrid tangible multi-touch tablet + an Augmented Reality Head-Mounted Display (AR-HMD) system. We base our method on the Tangible Brush manual spatial selection technique, which relies on a hybrid tangible multi-touch tablet + an external 2D display system. This registration concerns the second part of the overall project.

In this article we revisit the concept of abstraction as it is used in visualization and put it on a solid formal footing. While the term \emph{abstraction} is utilized in many scientific disciplines, arts, as well as everyday life, visualization inherits the notion of data abstraction or class abstraction from computer science, topological abstraction from mathematics, and visual abstraction from arts. All these notions have a lot in common, yet there is a major discrepancy in the terminology and basic understanding about visual abstraction in the context of visualization. We thus root the notion of abstraction in the philosophy of science, clarify the basic terminology, and provide crisp definitions of visual abstraction as a process. Furthermore, we clarify how it relates to similar terms often used interchangeably in the field of visualization. Visual abstraction is characterized by a conceptual space where this process exists, by the purpose it should serve, and by the perceptual and cognitive qualities of the beholder. These characteristics can be used to control the process of visual abstraction to produce effective and informative visual representations.

Approximate computing (AC) is an emerging paradigm for energy-efficient computation. The basic idea of AC is to sacrifice high precision for low energy by allowing for hardware which only carries out "approximately correct" calculations. For software verification, this challenges the validity of verification results for programs run on approximate hardware. In this paper, we present a novel approach to examine program correctness in the context of approximate computing. In contrast to all existing approaches, we start with a standard program verification and compute the allowed tolerances for AC hardware from that verification run. More precisely, we derive a set of constraints which - when met by the AC hardware - guarantees the verification result to carry over to AC. Our approach is based on the framework of abstract interpretation. On the practical side, we furthermore (1) show how to extract tolerance constraints from verification runs employing predicate abstraction as an instance of abstract interpretation, and (2) show how to check such constraints on hardware designs. We exemplify our technique on example C programs and a number of recently proposed approximate adders.

We present the results of a comprehensive analysis of visualization paper keywords supplied for 4366 papers submitted to five main visualization conferences. We describe main keywords, topic areas, and 10-year historic trends from two datasets: (1) the standardized PCS taxonomy keywords in use for paper submissions for IEEE InfoVis, IEEE Vis-SciVis, IEEE VAST, EuroVis, and IEEE PacificVis since 2009 and (2) the author-chosen keywords for papers published in the IEEE Visualization conference series (now called IEEE VIS) since 2004. Our analysis of research topics in visualization can serve as a starting point to (a) help create a common vocabulary to improve communication among different visualization sub-groups, (b) facilitate the process of understanding differences and commonalities of the various research sub-fields in visualization, (c) provide an understanding of emerging new research trends, (d) facilitate the crucial step of finding the right reviewers for research submissions, and (e) it can eventually lead to a comprehensive taxonomy of visualization research. One additional tangible outcome of our work is an application that allows visualization researchers to easily browse the 2600+ keywords used for IEEE VIS papers during the past 10 years, aiming at more informed and, hence, more effective keyword selections for future visualization publications.

Illustrative depictions have been playing an essential role in the communication of knowledge for centuries. Despite being limited to two ``colors,'' black-and-white illustrations can convey shape material, and illumination. Such blackand-white illustrations often have a certain clarity and crispness and are very suitable for use in print. Our goal: illustrative black-and-white visualization of dense line data with the same clarity and crispness.

International audience; Immersive Analytics is a new research initiative that aims to remove barriers between people, their data and the tools they use for analysis and decision making. Here we clarify the aims of immersive analytics research, its opportunities and historical context, as well as providing a broad research agenda for the field. In addition, we review how the term immersion has been used to refer to both technological and psychological immersion, both of which are central to immersive analytics research.

Digital tabletop displays and other large interactive displays have recently become more affordable and commonplace. Due to their benefits for supporting collaborative work—when compared to current desktop-based setups—they will likely be integrated in tomorrow’s work and learning environments. In these environments the exploration of information is a common task. We describe design considerations that focus on digital tabletop collaborative visualization environments. We focus on two types of interfaces: those for information exploration and data analysis in the context of workplaces, and those for more casual information exploration in public settings such as museums. We contrast design considerations for both environments and outline differences and commonalities between them. Postprint

Traditionally, scientific visualization research concentrates on the development and improvement of interactive techniques to support expert data analysis. While many scientific visualization tools have been developed for desktop environments and individual use, scenarios that go beyond mouse and keyboard interaction have received considerably less attention. We present a study that investigates how large-display direct-touch interaction affects data exploration and insight generation among groups of nonexperts exploring 2D vector data. In this study, pairs of participants used interaction techniques to customize and explore 2D vector visualizations and collaboratively discussed the process to develop their own understanding of the data sets. Postprint