EuroRVVV18

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2632397

Browse

Browsing EuroRVVV18 by Title

Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    EuroRV3 2018: Frontmatter
    (The Eurographics Association, 2018) Kai Lawonn; Noeska Smit; Lars Linsen; Robert Kosara; Kai Lawonn and Noeska Smit and Lars Linsen and Robert Kosara
  • Thumbnail Image
    Item
    Toward Visualizing Subjective Uncertainty: A Conceptual Framework Addressing Perceived Uncertainty through Action Redundancy
    (The Eurographics Association, 2018) Li, Wei; Funk, Mathias; Brombacher, Aarnout C.; Kai Lawonn and Noeska Smit and Lars Linsen and Robert Kosara
    Uncertainty is usually technically defined with associated metrics by visualization researchers. Next to this rather objective description, there is a subjective notion to uncertainty considering human experiences eliciting a response to the perceived uncertainty. This article aims to complement the default technical notion with a subjective perspective of uncertainty as we experienced. As a starting point, we introduce a conceptual framework aiming to explain the consequential life-cycle of subjective uncertainty in relation with visualization methods. The framework is illustrated by a case in which the redundancy of logged game play behavior is visualized to assist the discovery of subjective uncertainty. Our preliminary results show that visualizing the Shannon entropy of categorical action labels can be a promising method to probe subjective uncertainty.
  • Thumbnail Image
    Item
    Uncertainty of Visualizations for SenseMaking in Criminal Intelligence Analysis
    (The Eurographics Association, 2018) Islam, M. Junayed; Xu, Kai; Wong, B. L. W.; Kai Lawonn and Noeska Smit and Lars Linsen and Robert Kosara
    Uncertainty in visualization is an inevitable issue for sensemaking in criminal intelligence. Accuracy and precision of adopted visualization techniques have got greater role in trustworthiness with the system while finding out insights from crime related dataset. In this paper, we have presented a case study to introduce concepts of uncertainty and provenance and their relevance to crime analysis. Our findings show how uncertainties of visualization pipeline influence cognitive biases, human awareness and trust-building during crime analysis and how provenance can enhance analysis processes that include uncertainties.
  • Thumbnail Image
    Item
    Uncertainty Visualization: Recent Developments and Future Challenges in Prostate Cancer Radiotherapy Planning
    (The Eurographics Association, 2018) Raidou, Renata G.; Kai Lawonn and Noeska Smit and Lars Linsen and Robert Kosara
    Radiotherapy is one of the most common treatment strategy for prostate cancer. Prior to radiotherapy, a complex process consisting of several steps is employed to create an optimal treatment plan. However, all these steps include several sources of uncertainty, which can be detrimental for the successful outcome of the treatment. In this work, we present a number of strategies from the field of Visual Analytics that have been recently designed and implemented, for the visualization of data, processes and uncertainties at each step of the planning pipeline. We additionally document our opinion on topics that have not been yet addressed, and could be interesting directions for future work.
  • Thumbnail Image
    Item
    Visual Analytics-enabled Bayesian Network Approach to Reasoning about Public Camera Data
    (The Eurographics Association, 2018) Chuprikova, Ekaterina; MacEachren, Alan M.; Cron, Juliane; Meng, Liqiu; Kai Lawonn and Noeska Smit and Lars Linsen and Robert Kosara
    The Visual Analytics (VA) approach has become an important tool for gaining insights on various data sets. Thus, significant research has been conducted to integrate statistical methods in the interactive environment of VA where data visualization provides support to analysts in understanding and exploring the data. However, much of the data explored with VA is inherently uncertain due to limits of our knowledge about a phenomenon, randomness and indeterminism, and vagueness. The Bayesian Network (BN) is a graphical model that provides techniques for reasoning under conditions of uncertainty in a consistent and mathematically rigorous manner. While several software tools for visualizing and editing BNs exist, they have an evident shortcoming when spatial data. In this study, we propose a Visual Analytics-enabled BN approach for reasoning under uncertainty. We describe the implementation procedure using an example of heterogeneous data that includes locations of security surveillance cameras installed in public places.
  • Thumbnail Image
    Item
    Visualizing Uncertainty in Cultural Heritage Collections
    (The Eurographics Association, 2018) Windhager, Florian; Filipov, Velitchko Andreev; Salisu, Saminu; Mayr, Eva; Kai Lawonn and Noeska Smit and Lars Linsen and Robert Kosara
    Visualizations of cultural heritage collections play an increasing role in supporting the sensemaking processes of visitors and researchers. While many visualization systems provide overview and exploration options for non-expert audiences or casual users, others support the in-depth analysis of collection experts like curators or art historians. These visualizations often rely on object metadata, which have been generated from historical catalogs or textual sources, each with their own standards of precision, certainty, and data quality. To make these levels of data quality transparent, the visualization of uncertainty poses a vital challenge for interfaces across the board. We introduce the PolyCube system as a visualization framework which provides four different perspectives on the geo-temporal origins of cultural collection data, including coordinated multiple views, animation, color coding, and a space-time cube representation. With specific regard to this multi-perspective framework we develop options to visualize spatio-temporal uncertainty and discuss ways and means of their coherent implementation.