EuroVA19

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https://diglib.eg.org/handle/10.2312/2632784

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Browsing EuroVA19 by Subject "centered computing"

Now showing 1 - 4 of 4
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    Moving Together: Towards a Formalization of Collective Movement
    (The Eurographics Association, 2019) Buchmüller, Juri; Cakmak, Eren; Andrienko, Natalia; Andrienko, Gennady; Jolles, Jolle W.; Keim, Daniel A.; Landesberger, Tatiana von and Turkay, Cagatay
    While conventional applications for spatiotemporal datasets mostly focus on the relation between movers and environment, research questions in the analysis of collective movement typically focus more on relationships and dynamics between the moving entities themselves. Instead of concentrating on origin, destination and the way in between, this inter-mover perspective on spatiotemporal data allows to explain how moving groups are coordinating. Yet, only few visualization and Visual Analytics approaches focus on the relationships between movers. To illuminate this research gap, we propose initial steps towards a comprehensive formalization of coordination in collective movement based on temporal autocorrelation of distance matrices derived from basic movement characteristics. We exemplify how patterns can be encoded using autocorrelation cubes and outline the next steps towards an exhaustive formalization of coordination patterns.
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    On Quality Indicators for Progressive Visual Analytics
    (The Eurographics Association, 2019) Angelini, Marco; May, Thorsten; Santucci, Giuseppe; Schulz, Hans-Jörg; Landesberger, Tatiana von and Turkay, Cagatay
    A key component in using Progressive Visual Analytics (PVA) is to be able to gauge the quality of intermediate analysis outcomes. This is necessary in order to decide whether a current partial outcome is already good enough to cut a long-running computation short and to proceed. To aid in this process, we propose ten fundamental quality indicators that can be computed and displayed to gain a better understanding of the progress of the progression and of the stability and certainty of an intermediate outcome. We further highlight the use of these fundamental indicators to derive other quality indicators, and we show how to apply the indicators in two use cases.
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    Quantifying Uncertainty in Multivariate Time Series Pre-Processing
    (The Eurographics Association, 2019) Bors, Christian; Bernard, Jürgen; Bögl, Markus; Gschwandtner, Theresia; Kohlhammer, Jörn; Miksch, Silvia; Landesberger, Tatiana von and Turkay, Cagatay
    In multivariate time series analysis, pre-processing is integral for enabling analysis, but inevitably introduces uncertainty into the data. Enabling the assessment of the uncertainty and allowing uncertainty-aware analysis, the uncertainty needs to be quantified initially. We address this challenge by formalizing the quantification of uncertainty for multivariate time series preprocessing. To tackle the large design space, we elaborate key considerations for quantifying and aggregating uncertainty. We provide an example how the quantified uncertainty is used in a multivariate time series pre-processing application to assess the effectiveness of pre-processing steps and adjust the pipeline to minimize the introduction of uncertainty.
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    Visual Analytics of Event Data using Multiple Mining Methods
    (The Eurographics Association, 2019) Adnan, Muhammad; Nguyen, Phong; Ruddle, Roy; Turkay, Cagatay; Landesberger, Tatiana von and Turkay, Cagatay
    Most researchers use a single method of mining to analyze event data. This paper uses case studies from two very different domains (electronic health records and cybersecurity) to investigate how researchers can gain breakthrough insights by combining multiple event mining methods in a visual analytics workflow. The aim of the health case study was to identify patterns of missing values, which was daunting because the 615 million missing values occurred in 43,219 combinations of fields. However, a workflow that involved exclusive set intersections (ESI), frequent itemset mining (FIM) and then two more ESI steps allowed us to identify that 82% of the missing values were from just 244 combinations. The cybersecurity case study's aim was to understand users' behavior from logs that contained 300 types of action, gathered from 15,000 sessions and 1,400 users. Sequential frequent pattern mining (SFPM) and ESI highlighted some patterns in common, and others that were not. For the latter, SFPM stood out for its ability to action sequences that were buried within otherwise different sessions, and ESI detected subtle signals that were missed by SFPM. In summary, this paper demonstrates the importance of using multiple perspectives, complementary set mining methods and a diverse workflow when using visual analytics to analyze complex event data.