EuroVA2020

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

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Browsing EuroVA2020 by Subject "Visual analytics"

Now showing 1 - 4 of 4
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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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    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.