EuroVA: International Workshop on Visual Analytics
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Browsing EuroVA: International Workshop on Visual Analytics by Author "Behrisch, Michael"
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Item Multivariate Time Series Retrieval with Symbolic Aggregate Approximation, Regular Expression, and Query Expansion(The Eurographics Association, 2022) Yu, Yuncong; Becker, Tim; Behrisch, Michael; Bernard, Jürgen; Angelini, MarcoWe present SAXRegEx, a method for pattern search in multivariate time series in the presence of various distortions, such as duration variation, warping, and time delay between signals. For example, in the automotive industry, calibration engineers spontaneously search for event-induced patterns in fresh measurements under time pressure. Current methods do not sufficiently address duration (horizontal along the time axis) scaling and inter-track time delay. One reason is that it can be overwhelmingly complex to consider scaling and warping jointly and analyze temporal dynamics and attribute interrelation simultaneously. SAXRegEx meets this challenge with a novel symbolic representation modeling adapted to handle time series with multiple tracks. We employ methods from text retrieval, i.e., regular expression matching, to perform a pattern retrieval and develop a novel query expansion algorithm to deal flexibly with pattern distortions. Experiments show the effectiveness of our approach, especially in the presence of such distortions, and its efficiency surpassing the state-of-the-art methods. While we design the method primarily for automotive data, it is well transferable to other domains.Item ShaRP: Shape-Regularized Multidimensional Projections(The Eurographics Association, 2023) Machado, Alister; Telea, Alexandru; Behrisch, Michael; Angelini, Marco; El-Assady, MennatallahProjections, or dimensionality reduction methods, are techniques of choice for the visual exploration of high-dimensional data. Many such techniques exist, each one of them having a distinct visual signature - i.e., a recognizable way to arrange points in the resulting scatterplot. Such signatures are implicit consequences of algorithm design, such as whether the method focuses on local vs global data pattern preservation; optimization techniques; and hyperparameter settings. We present a novel projection technique - ShaRP - that provides users explicit control over the visual signature of the created scatterplot, which can cater better to interactive visualization scenarios. ShaRP scales well with dimensionality and dataset size, generically handles any quantitative dataset, and provides this extended functionality of controlling projection shapes at a small, user-controllable cost in terms of quality metrics.