A Survey on Cross‐Virtuality Analytics

Fröhler, Bernhard; Anthes, Christoph; Pointecker, Fabian; Friedl, Judith; Schwajda, Daniel; Riegler, Andreas; Tripathi, Shailesh; Holzmann, Clemens; Brunner, Manuel; Jodlbauer, Herbert; Jetter, Hans Christian; Heinzl, Christoph

doi:10.1111/cgf.14447

Cited by 39 publications

(19 citation statements)

References 203 publications

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“…The preliminary results indicate positive findings regarding the collaborative analysis and the flexible usage of the tool. The variety of supported devices is a stepping stone towards cross-virtuality analytics [20,27,28].…”

Section: Discussionmentioning

confidence: 99%

NDTFlix: Collaborative Remote Analysis of X-ray Computed Tomography Datasets

Fröhler¹,

Weinberger²,

Gall³

et al. 2023

eJNDT

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When analysing data collected from non-destructive testing, several areas of expertise are required, such as on imaging modalities, data analysis and visualisation, or the domain background in terms of the inspected material. Typically, this requires a team of experts, who are not necessarily situated at the same working place. We therefore propose a system supporting collaborative remote analysis facilitating easy access to a common workspace. The system supports a collaborative analysis of datasets by connecting interactions in a lightweight web-based viewer with those in an advanced analysis software package on a desktop PC. Our system supports both co-located and distributed collaboration on large complex datasets and allows for a simple spontaneous cross-device access. This allows a wide range of devices and immersive systems to be supported regardless of their capabilities. Viewpoints and annotations can be shared as visual cues for the analysis of collaborators. The proposed system will be presented using a case study of analysing an adaptive manufactured aeronautics component. The preliminary findings showed positive feedback of the experts regarding the creation of a flexible and fast collaborative analysis set-up.

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Section: Discussionmentioning

confidence: 99%

NDTFlix: Collaborative Remote Analysis of X-ray Computed Tomography Datasets

Fröhler¹,

Weinberger²,

Gall³

et al. 2023

eJNDT

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“…Examples of existing literature exploring this area include work utilizing a mixture of mobile and large-scale displays [27], AR and tabletop displays [49], and VR and desktop displays [28]. Fröhler et al [15] survey and categorize these works as cross-virtuality analytics (XVA)-or more generally cross-virtuality (XV) [2]. The authors define XVA as "systems for data visualization and analysis that seamlessly integrate different visual metaphors and devices along the entire RVC to support multiple users with transitional and collaborative interfaces.…”

Section: Cross-platform Visualizationmentioning

confidence: 99%

“…This gap in common ground, a central design consideration for collaborative visualization [24], widens as the platforms diverge in their physical form factor, display mode, and interaction model. It is particularly problematic when collaborating across the reality-virtuality continuum (RVC) [15]-such as across desktop and virtual reality (VR) head-mounted devices. At the same, engaging multiple device platforms in collaboration is often beneficial [27], especially when combining traditional 2D views with immersive 3D views [28,59].…”

Section: Introductionmentioning

confidence: 99%

Through Their Eyes and In Their Shoes: Providing Group Awareness During Collaboration Across Virtual Reality and Desktop Platforms

Saffo¹,

Batch²,

Dunne³

et al. 2023

Preprint

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Many collaborative data analysis situations benefit from collaborators utilizing different platforms. However, maintaining group awareness between team members using diverging devices is difficult, not least because common ground diminishes. A person using head-mounted VR cannot physically see a user on a desktop computer even while co-located, and the desktop user cannot easily relate to the VR user’s 3D workspace. To address this, we propose the “eyes-and-shoes” principles for group awareness and abstract them into four levels of techniques. Furthermore, we evaluate these principles with a qualitative user study of 6 participant pairs synchronously collaborating across distributed desktop and VR head-mounted devices. In this study, we vary the group awareness techniques between participants and explore two visualization contexts within participants. The results of this study indicate that the more visual metaphors and views of participants diverge, the greater the level of group awareness is needed.

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“…The advertisement text was posted on multiple platforms dedicated to experiments with online recruitment, such as Immersive Experiences Working Group (2022), Kruusimagi et al (2022), and Prolific (2022). As these platforms are not necessarily popular among VR users, we also advertised our experiment on channels dedicated to open exchanges between VR users, such as the Facebook oculus channel (Meta, 2004) and Reddit (Advance Publications, 2005).…”

Section: Advertising Experimentsmentioning

confidence: 99%

Mitigation strategies for participant non-attendance in VR remote collaborative experiments

Bovo

Giunchi

Costanza

et al. 2022

Front. Comput. Sci.

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COVID-19 led to the temporary closure of many HCI research facilities disrupting many ongoing user studies. While some studies could easily move online, this has proven problematic for virtual reality (VR) studies. The main challenge of remote VR study is the recruitment of participants who have access to specialized hardware such as head-mounted displays. This challenge is exacerbated in collaborative VR studies, where multiple participants need to be available and remotely connect to the study simultaneously. We identify the latter as the worst-case scenario regarding resource wastage and frustration. Across two collaborative user studies, we identified the personal connection between the experimenter and the participant as a critical factor in reducing non-attendance. We compare three recruitment strategies that we have iteratively developed based on our recent experiences. We introduce a metric to quantify the cost for each recruitment strategy, and we show that our final strategy achieves the best metric score. Our work is valuable for HCI researchers recruiting participants for collaborative VR remote studies, but it can be easily extended to every remote experiment scenario.

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A Survey on Cross‐Virtuality Analytics

Cited by 39 publications

References 203 publications

NDTFlix: Collaborative Remote Analysis of X-ray Computed Tomography Datasets

NDTFlix: Collaborative Remote Analysis of X-ray Computed Tomography Datasets

Through Their Eyes and In Their Shoes: Providing Group Awareness During Collaboration Across Virtual Reality and Desktop Platforms

Mitigation strategies for participant non-attendance in VR remote collaborative experiments

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