Browsing by Author "Splechtna, Rainer"

Now showing 1 - 3 of 3
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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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    Interactive Pattern Analysis of Multiple T-Maze Data
    (The Eurographics Association, 2019) Bechtold, Fabrizia; Abraham, Hrvoje; Splechtna, Rainer; Matkovic, Krešimir; Landesberger, Tatiana von and Turkay, Cagatay
    The Multiple T-Maze study is one of the standard methods used in ethology and behaviourism. In this paper we extend the current state of the art in analysis of Multiple T-Maze data for animal cohorts. We focus on pattern finding within animals' paths. We introduce the Sequence View which makes it possible to quickly spot patterns and to search for specific sub-paths in animal paths. Further, we also evaluate four different metrics for string comparison and two widely used embeddings to support interactive clustering. All views are fully integrated in a coordinated multiple views system and support active brushing. This research represents a step towards (semi)-automatic clustering for Multiple T-Maze cohort data, which will significantly improve the Multiple T-Maze data analysis.
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    Visual Exploratory Analysis for Multiple T-Maze Studies
    (The Eurographics Association, 2018) Bechtold, Fabrizia; Splechtna, Rainer; Matkovic, Kresimir; Puig Puig, Anna and Schultz, Thomas and Vilanova, Anna and Hotz, Ingrid and Kozlikova, Barbora and Vázquez, Pere-Pau
    Evaluation of spatial learning and memory in rodents is commonly carried out using different maze settings such as the Multiple T-Maze. State-of-the-art analysis is primarily based on statistics of quantitative measures stemming from animal trajectories in a maze, e.g. path length or correct decisions made. Currently trajectories themselves are analyzed and evaluated one at a time and comparison of multiple trajectories is a tedious task. The resulting findings may not fully answer complex questions that behavioral researchers encounter as well, e.g., why do animals behave in a certain way or can atypical behaviour be detected? This paper describes an innovative approach on how exploratory analysis for Multiple T-Maze studies can be enhanced through interactive visual analysis. We explain our solution for analyzing a whole ensemble of data at once and support the finding of orientational characteristics and migration patterns within the ensemble. We also abstract the analysis tasks for Multiple TMaze studies and, based on these tasks, we extend a coordinated multiple views system to support the solving of fundamental problems which behavioral researchers face. Besides views of standard charts we deploy a multi-resolution heat map and the Gate-O-Gon, which is a novel visual element. It gives clues on the animals' general movement orientation and distribution of revisited gates, as well as enhances the discovery of patterns in movement and identifying of irregular behavior. Finally we demonstrate the usefulness of the newly proposed approach using a real life data set consisting of 400 Multiple T-Maze runs.