EuroVA2020

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https://diglib.eg.org/handle/10.2312/2632904

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Browsing EuroVA2020 by Subject "Human centered computing"

Now showing 1 - 7 of 7
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    Dual Radial Set
    (The Eurographics Association, 2020) Matkovic, Kresimir; Gracanin, Denis; Bardun, Matea; Splechtna, Rainer; Hauser, Helwig; Turkay, Cagatay and Vrotsou, Katerina
    Set-typed data visualizations require novel interactive representations, especially when visualizing multiple set-typed data attributes. The challenge is how to effectively analyze relations between data elements from different set-typed attributes. We build on Radial Set view to support simultaneous visualization of two set-typed attributes. The main contributions include: Dual Radial Set view that supports simultaneous visualization of two groups of sets; an extension of Radial Set view that can display empty sets; and two new view configurations, the equal sector size and the relative-size scaled sectors. The two new view configurations also can be applied to the original Radial Set view. We conducted an informal evaluation using a movies data set as a case study. The evaluation results demonstrate the advantages of the proposed approach.
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    Enhanced Attribute-Based Explanations of Multidimensional Projections
    (The Eurographics Association, 2020) Driel, Daan van; Zhai, Xiaorui; Tian, Zonglin; Telea, Alexandru; Turkay, Cagatay and Vrotsou, Katerina
    Multidimensional projections (MPs) are established tools for exploring the structure of high-dimensional datasets to reveal groups of similar observations. For optimal usage, MPs can be augmented with mechanisms that explain what such points have in common that makes them similar. We extend the set of such explanatory instruments by two new techniques. First, we compute and encode the local dimensionality of the data in the projection, thereby showing areas where the MP can be well explained by a few latent variables. Secondly, we compute and display local attribute correlations, thereby helping the user to discover alternative explanations for the underlying phenomenon. We implement our explanatory tools using an image-based approach, which is efficient to compute, scales well visually for large and dense MP scatterplots, and can handle any projection technique. We demonstrate our approach using several datasets.
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    An Exploratory Visual Analytics Tool for Multivariate Dynamic Networks
    (The Eurographics Association, 2020) Boz, Hasan Alp; Bahrami, Mohsen; Suhara, Yoshihiko; Bozkaya, Burcin; Balcisoy, Selim; Turkay, Cagatay and Vrotsou, Katerina
    Visualizing multivariate dynamic networks is a challenging task. The evolution of the dynamic network within the temporal axis must be depicted in conjunction with the associated multivariate attributes. In this paper, an exploratory visual analytics tool is proposed to display multivariate dynamic networks with spatial attributes. The proposed tool displays the distribution of multivariate temporal domain and network attributes in scattered views. Moreover, in order to expose the evolution of a single or a group of nodes in the dynamic network along the temporal axis, an egocentric approach is applied in which a node is represented with its neighborhood as an ego-network. This approach allows users to observe a node's surrounding environment along the temporal axis. On top of the traditional ego-network visualization methods, such as timelines, the proposed tool encodes ego-networks as feature vectors consisting of the domain and network attributes and projects them onto 2D views. As a result, the distance between projected ego-networks represents the dissimilarity across the temporal axis in a single view. The proposed tool is demonstrated with a real-world use case scenario on merchant networks obtained from a one-year-long credit card transactions.
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    Progressive Parameter Space Visualization for Task-Driven SAX Configuration
    (The Eurographics Association, 2020) Loeschcke, Sebastian; Hogräfer, Marius; Schulz, Hans-Jörg; Turkay, Cagatay and Vrotsou, Katerina
    As time series datasets are growing in size, data reduction approaches like PAA and SAX are used to keep them storable and analyzable. Yet, finding the right trade-off between data reduction and remaining utility of the data is a challenging problem. So far, it is either done in a user-driven way and offloaded to the analyst, or it is determined in a purely data-driven, automated way. None of these approaches take the analytic task to be performed on the reduced data into account. Hence, we propose a task-driven parametrization of PAA and SAX through a parameter space visualization that shows the difference of progressively running a given analytic computation on the original and on the reduced data for a representative set of data samples. We illustrate our approach in the context of climate analysis on weather data and exoplanet detection on light curve data.
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    Quality Metrics to Guide Visual Analysis of High Dimensional Genomics Data
    (The Eurographics Association, 2020) Fernstad, Sara Johansson; Macquisten, Alexander; Berrington, Janet; Embleton, Nicholas; Stewart, Christopher; Turkay, Cagatay and Vrotsou, Katerina
    Studies of genome sequenced data are increasingly common in many domains. Technological advances enable detection of hundreds of thousands of biological entities in samples, resulting in extremely high dimensional data. To enable exploration and understanding of such data, efficient visual analysis approaches are needed that take domain and data specific requirements into account. Based on a survey with bioscience experts, this paper suggests a categorisation and a set of quality metrics to identify patterns of interest, which can be used as guidance in visual analysis, as demonstrated in the paper.
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    SpatialRugs: Enhancing Spatial Awareness of Movement in Dense Pixel Visualizations
    (The Eurographics Association, 2020) Buchmüller, Juri F.; Schlegel, Udo; Cakmak, Eren; Keim, Daniel A.; Dimara, Evanthia; Turkay, Cagatay and Vrotsou, Katerina
    Compact visual summaries of spatio-temporal movement data often strive to express accurate positions of movers. We present SpatialRugs, a technique to enhance the spatial awareness of movements in dense pixel visualizations. SpatialRugs apply 2D colormaps to visualize location mapped to a juxtaposed display. We explore the effect of various colormaps discussing perceptual limitations and introduce a custom color-smoothing method to mitigate distorted patterns of collective movement behavior.
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    A Window-based Approach for Mining Long Duration Event-sequences
    (The Eurographics Association, 2020) Vrotsou, Katerina; Nordman, Aida; Turkay, Cagatay and Vrotsou, Katerina
    This paper presents an interactive sequence mining approach for exploring long duration event-sequences and identifying interesting patterns within them. The approach extends previous work on exploratory sequence mining by using a sliding window to split the sequence prior to mining. Patterns are interactively grown and visualized through a tree representation, while a set of accompanying views allows for identified patterns to be explored in the context in which they occur. The approach is motivated and exemplified in the domain of air traffic control and, in particular, air traffic controller training.