Publications Search Results

Now showing 1 - 10 of 154
  • Publication
    Interactive Input and Visualization for Planning with Temporal Uncertainty
    ( 2022)
    Höhn, Markus
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    Wunderlich, Marcel
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    Ballweg, Kathrin
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    Landesberger, Tatiana von
    Data with temporal uncertainty is ubiquitous in everyone's life. Popular examples are holiday planning or train trips. There are several approaches to visualize temporal uncertainty, but common research usually does not take uncertainty into account, neither as input nor output. We propose a new approach that provides both an interactive drawing for data with temporal uncertainty and their respective visualizations. The user can draw both variable and fixed activities and also has the possibility to set probability distributions and enter indefinite activities. A quantitative user study shows the need and suitability of our new approach.
  • Publication
    NetVisGame: Mobile Gamified Information Visualization of Home Network Traffic Data
    The awareness of everyday internet users for cyber security becomes ever more important considering the ubiquity of the Internet in everyday life. However, people usually lack the necessary understanding of this topic or the motivation to pay attention to the problem and its possible consequences. In this paper, we present the novel idea of combining visualization of one's own personal data related to cyber-security literacy with a casual gaming approach. We therefore introduce our prototype, NetVisGame, in which we have implemented the idea for personal network traffic data based on a preliminary user study. The evaluation results of the first iteration of the user-centered design process supports the assumption that this approach is feasible to raise interest for and foster understanding of network traffic data and therefore could be a promising approach for data and technologies related to cyber-security literacy.
  • Publication
    The Role of Interactive Visualization in Fostering Trust in AI
    ( 2021)
    Beauxis-Aussalet, Emma
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    Behrisch, Michael
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    Borgo, Rita
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    Chau, Duen Horng
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    Collins, Christopher
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    Ebert, David
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    El-Assady, Mennatallah
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    Endert, Alex
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    Keim, Daniel A.
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    Oelke, Daniela
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    Peltonen, Jaakko
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    Riveiro, Maria
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    Schreck, Tobias
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    Strobelt, Hendrik
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    Wijk, Jarke J. van
    The increasing use of artificial intelligence (AI) technologies across application domains has prompted our society to pay closer attention to AI's trustworthiness, fairness, interpretability, and accountability. In order to foster trust in AI, it is important to consider the potential of interactive visualization, and how such visualizations help build trust in AI systems. This manifesto discusses the relevance of interactive visualizations and makes the following four claims: i) trust is not a technical problem, ii) trust is dynamic, iii) visualization cannot address all aspects of trust, and iv) visualization is crucial for human agency in AI.
  • Publication
    Towards the Detection and Visual Analysis of COVID-19 Infection Clusters
    A major challenge for departments of public health (DPHs) in dealing with the ongoing COVID-19 pandemic is tracing contacts in exponentially growing SARS-CoV2 infection clusters. Prevention of further disease spread requires a comprehensive registration of the connections between individuals and clusters. Due to the high number of infections with unknown origin, the healthcare analysts need to identify connected cases and clusters through accumulated epidemiological knowledge and the metadata of the infections in their database. Here we contribute a visual analytics framework to identify, assess and visualize clusters in COVID-19 contact tracing networks. Additionally, we demonstrate how graph-based machine learning methods can be used to find missing links between infection clusters and thus support the mission to get a comprehensive view on infection events. This work was developed through close collaboration with DPHs in Germany. We argue how our systems supports the identification of clusters by public health experts and discuss ongoing developments and possible extensions.
  • Publication
    ProBGP: Progressive Visual Analytics of Live BGP Updates
    The global routing network is the backbone of the Internet. However, it is quite vulnerable to attacks that cause major disruptions or routing manipulations. Prior related works have visualized routing path changes with node link diagrams, but it requires strong domain expertise to understand if a routing change between autonomous systems is suspicious. Geographic visualization has an advantage over conventional node-link diagrams by helping uncover such suspicious routes as the user can immediately see if a path is the shortest path to the target or an unreasonable detour. In this paper, we present ProBGP, a web-based progressive approach to visually analyze BGP update routes. We created a novel progressive data processing algorithm for the geographic approximation of autonomous systems and combined it with a progressively updating visualization. While the newest log data is continuously loaded, our approach also allows querying the entire log recordings since 1999. We present the usefulness of our approach with a real use case of a major route leak from June 2019. We report on multiple interviews with domain experts throughout the development. Finally, we evaluated our algorithm quantitatively against a public peering database and qualitatively against AS network maps.
  • Publication
    Towards a Comprehensive Cohort Visualization of Patients with Inflammatory Bowel Disease
    This paper reports on a joint project with medical experts on inflammatory bowel disease (IBD). Patients suffering from IBD, e.g. Crohn's disease or ulcerative colitis, do not have a reduced life expectancy and disease progressions easily span several decades. We designed a visualization to highlight information that is vital for comparing patients and progressions, especially with respect to the treatments administered over the years. Medical experts can interactively determine the amount of information displayed and can synchronize the progressions to the beginning of certain treatments and medications. While the visualization was designed in close collaboration with IBD experts, we additionally evaluated our approach with 35 participants to ensure good usability and accessibility. The paper also highlights the future work on similarity definition and additional visual features in this on-going project.
  • Publication
    The Effect of Alignment on Peoples Ability to Judge Event Sequence Similarity
    ( 2021)
    Ruddle, Roy A.
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    Bernard, Jürgen
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    Event sequences are central to the analysis of data in domains that range from biology and health, to logfile analysis and people's everyday behavior. Many visualization tools have been created for such data, but people are error-prone when asked to judge the similarity of event sequences with basic presentation methods. This paper describes an experiment that investigates whether local and global alignment techniques improve people's performance when judging sequence similarity. Participants were divided into three groups (basic vs. local vs. global alignment), and each participant judged the similarity of 180 sets of pseudo-randomly generated sequences. Each set comprised a target, a correct choice and a wrong choice. After training, the global alignment group was more accurate than the local alignment group (98% vs. 93% correct), with the basic group getting 95% correct. Participants' response times were primarily affected by the number of event types, the similarity of sequences (measured by the Levenshtein distance) and the edit types (nine combinations of deletion, insertion and substitution). In summary, global alignment is superior and people's performance could be further improved by choosing alignment parameters that explicitly penalize sequence mismatches.
  • Publication
    A Visualization Interface to Improve the Transparency of Collected Personal Data on the Internet
    Online services are used for all kinds of activities, like news, entertainment, publishing content or connecting with others. But information technology enables new threats to privacy by means of global mass surveillance, vast databases and fast distribution networks. Current news are full of misuses and data leakages. In most cases, users are powerless in such situations and develop an attitude of neglect for their online behaviour. On the other hand, the GDPR (General Data Protection Regulation) gives users the right to request a copy of all their personal data stored by a particular service, but the received data is hard to understand or analyze by the common internet user. This paper presents Transparency Vis - a web-based interface to support the visual and interactive exploration of data exports from different online services. With this approach, we aim at increasing the awareness of personal data stored by such online services and the effects of online behaviour. This design study provides an online accessible prototype and a best practice to unify data exports from different sources.
  • Publication
    User-Centered Design of Visualizations for Software Vulnerability Reports
    Today's software systems are created by software development processes that naturally include mistakes, some of which can be exploited by attackers and are therefore called vulnerabilities. Automatic software scanners enable developers to analyze their applications to detect vulnerabilities and alert them of their presence. But often these reports are hard to understand, include false positives or overwhelm users due to the sheer number of alerts, since a report may contain hundreds to thousands of vulnerabilities. Developers must undergo a process called vulnerability triage to find the relevant vulnerabilities to fix. This paper presents two interactive visualizations for developers and security experts to gain an overview of the security state of their application. Users can see the distribution of vulnerabilities, find the most relevant ones, and compare differences between application versions. Our visualization design is inspired by an initial preliminary study and has been evaluated by domain experts to investigate the usability and appropriateness.
  • Publication
    PrivInferVis: Towards Enhancing Transparency over Attribute Inference in Online Social Networks
    The European GDPR calls, besides other things, for innovative tools to empower online social networks (OSN) users with transparency over risks of attribute inferences. In this work, we propose a novel transparency-enhancing framework for OSN, PrivInferVis, to help people assess and visualize their individual risks of attribute inference derived from public details from their social graphs in different OSN domains. We propose a weighted Bayesian model as the underlying method for attribute inference. A preliminary evaluation shows that our proposal outperforms baseline algorithms on several evaluation metrics significantly. PrivInferVis provides visual interfaces that allow users to explore details about their (inferred and self-disclosed) data and to understand how inference estimates and related scores are derived.