EuroVA18

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https://diglib.eg.org/handle/10.2312/2632398

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Browsing EuroVA18 by Subject "Human"

Now showing 1 - 5 of 5
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    Combining the Automated Segmentation and Visual Analysis of Multivariate Time Series
    (The Eurographics Association, 2018) Bernard, Jürgen; Bors, Christian; Bögl, Markus; Eichner, Christian; Gschwandtner, Theresia; Miksch, Silvia; Schumann, Heidrun; Kohlhammer, Jörn; Christian Tominski and Tatiana von Landesberger
    For the automatic segmentation of multivariate time series domain experts at first need to consider a huge space of alternative configurations of algorithms and parameters. We assume that only a small subset of these configurations needs to be computed and analyzed to lead users to meaningful configurations. To expedite this search, we propose the conceptualization of a segmentation workflow. First, with an algorithmic segmentation pipeline, domain experts can calculate segmentation results with different parameter configurations. Second, in an interactive visual analysis step, domain experts can explore segmentation results to further adapt and improve segmentation pipeline in an informed way. In the interactive analysis approach influences of algorithms, parameters, and different types of uncertainty information are conveyed, which is decisive to trigger selective and purposeful re-calculations. The workflow is built upon reflections on collaborations with domain experts working in activity recognition, which also defines our usage scenario demonstrating the applicability of the workflow.
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    Guidance or No Guidance? A Decision Tree Can Help
    (The Eurographics Association, 2018) Ceneda, Davide; Gschwandtner, Theresia; May, Thorsten; Miksch, Silvia; Streit, Marc; Tominski, Christian; Christian Tominski and Tatiana von Landesberger
    Guidance methods have the potential of bringing considerable benefits to Visual Analytics (VA), alleviating the burden on the user and allowing a positive analysis outcome. However, the boundary between conventional VA approaches and guidance is not sharply defined. As a consequence, framing existing guidance methods is complicated and the development of new approaches is also compromised. In this paper, we try to bring these concepts in order, defining clearer boundaries between guidance and no-guidance. We summarize our findings in form of a decision tree that allows scientists and designers to easily frame their solutions. Finally, we demonstrate the usefulness of our findings by applying our guideline to a set of published approaches.
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    Personalized Visual-Interactive Music Classification
    (The Eurographics Association, 2018) Ritter, Christian; Altenhofen, Christian; Zeppelzauer, Matthias; Kuijper, Arjan; Schreck, Tobias; Bernard, Jürgen; Christian Tominski and Tatiana von Landesberger
    We present an interactive visual music classification tool that will allow users to automatically structure music collections in a personalized way. With our approach, users play an active role in an iterative process of building classification models, using different interactive interfaces for labeling songs. The interactive tool conflates interfaces for the detailed analysis at different granularities, i.e., audio features, music songs, as well as classification results at a glance. Interactive labeling is provided with three complementary interfaces, combining model-centered and human-centered labeling-support principles. A clean visual design of the individual interfaces depicts complex model characteristics for experts, and indicates our work-inprogress towards the abilities of non-experts. The result of a preliminary usage scenario shows that, with our system, hardly any knowledge about machine learning is needed to create classification models of high accuracy with less than 50 labels.
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    A Set-based Visual Analytics Approach to Analyze Retail Data
    (The Eurographics Association, 2018) Adnan, Muhammad; Ruddle, Roy A.; Christian Tominski and Tatiana von Landesberger
    This paper explores how a set-based visual analytics approach could be useful for analyzing customers' shopping behavior, and makes three main contributions. First, it describes the scale and characteristics of a real-world retail dataset from a major supermarket. Second, it presents a scalable visual analytics workflow to quickly identify patterns in shopping behavior. To assess the workflow, we conducted a case study that used data from four convenience stores and provides several insights about customers' shopping behavior. Third, from our experience with analyzing real-world retail data and comments made by our industry partner, we outline four research challenges for visual analytics to tackle large set intersection problems.
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    Towards Visual Cyber Security Analytics for the Masses
    (The Eurographics Association, 2018) Ulmer, Alex; Schufrin, Marija; Lücke-Tieke, Hendrik; Kannanayikkal, Clindo Devassy; Kohlhammer, Jörn; Christian Tominski and Tatiana von Landesberger
    Understanding network activity and cyber threats is of major concern these days, for business and private users alike. As more and more online applications assist us in our daily life, there is a growing potential vulnerability to cyber crime. With this paper, we want to share our vision of cyber security analytics becoming an accessible everyday task through visual analysis tools. We describe the context of this vision and our experience with the first achievements in this direction. With our new prototype, anyone can analyze their network traffic logs and get security-relevant information out of it, a task that was too difficult and sometimes too expensive in the past. We present an open, accessible and user-friendly visual network analyzer for PCAP (packet capture) files, critically discuss our first prototype, and give an outlook to anomaly detection supported by active learning in this context.