Browsing by Author "Streit, Marc"

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    Guide Me in Analysis: A Framework for Guidance Designers
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Ceneda, Davide; Andrienko, Natalia; Andrienko, Gennady; Gschwandtner, Theresia; Miksch, Silvia; Piccolotto, Nikolaus; Schreck, Tobias; Streit, Marc; Suschnigg, Josef; Tominski, Christian; Benes, Bedrich and Hauser, Helwig
    Guidance is an emerging topic in the field of visual analytics. Guidance can support users in pursuing their analytical goals more efficiently and help in making the analysis successful. However, it is not clear how guidance approaches should be designed and what specific factors should be considered for effective support. In this paper, we approach this problem from the perspective of guidance designers. We present a framework comprising requirements and a set of specific phases designers should go through when designing guidance for visual analytics. We relate this process with a set of quality criteria we aim to support with our framework, that are necessary for obtaining a suitable and effective guidance solution. To demonstrate the practical usability of our methodology, we apply our framework to the design of guidance in three analysis scenarios and a design walk‐through session. Moreover, we list the emerging challenges and report how the framework can be used to design guidance solutions that mitigate these issues.
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    ParaDime: A Framework for Parametric Dimensionality Reduction
    (The Eurographics Association and John Wiley & Sons Ltd., 2023) Hinterreiter, Andreas; Humer, Christina; Kainz, Bernhard; Streit, Marc; Bujack, Roxana; Archambault, Daniel; Schreck, Tobias
    ParaDime is a framework for parametric dimensionality reduction (DR). In parametric DR, neural networks are trained to embed high-dimensional data items in a low-dimensional space while minimizing an objective function. ParaDime builds on the idea that the objective functions of several modern DR techniques result from transformed inter-item relationships. It provides a common interface for specifying these relations and transformations and for defining how they are used within the losses that govern the training process. Through this interface, ParaDime unifies parametric versions of DR techniques such as metric MDS, t-SNE, and UMAP. It allows users to fully customize all aspects of the DR process.We show how this ease of customization makes ParaDime suitable for experimenting with interesting techniques such as hybrid classification/embedding models and supervised DR. This way, ParaDime opens up new possibilities for visualizing high-dimensional data.