Hochschule für Architektur, Bau und Geomatik FHNW

Dauerhafte URI für den Bereichhttps://irf.fhnw.ch/handle/11654/6

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  • Vorschaubild
    Publikation
    Toward flexible visual analytics augmented through smooth display transitions
    (Elsevier, 2021) Tominski, Christian; Andrienko, Gennady; Andrienko, Natalia; Bleisch, Susanne; Fabrikant, Sara Irina; Mayr, Eva; Miksch, Silvia; Pohl, Margit; Skupin, André
    Visualizing big and complex multivariate data is challenging. To address this challenge, we propose flexible visual analytics (FVA) with the aim to mitigate visual complexity and interaction complexity challenges in visual analytics, while maintaining the strengths of multiple perspectives on the studied data. At the heart of our proposed approach are transitions that fluidly transform data between user-relevant views to offer various perspectives and insights into the data. While smooth display transitions have been already proposed, there has not yet been an interdisciplinary discussion to systematically conceptualize and formalize these ideas. As a call to further action, we argue that future research is necessary to develop a conceptual framework for flexible visual analytics. We discuss preliminary ideas for prioritizing multi-aspect visual representations and multi-aspect transitions between them, and consider the display user for whom such depictions are produced and made available for visual analytics. With this contribution we aim to further facilitate visual analytics on complex data sets for varying data exploration tasks and purposes based on different user characteristics and data use contexts.
    01A - Beitrag in wissenschaftlicher Zeitschrift
  • Vorschaubild
    Publikation
    Visual feature engineering
    (Institut Geomatik, Hochschule für Architektur, Bau und Geomatik FHNW, 2018) Bleisch, Susanne
    Feature engineering is a key concept in machine learning describing the process of defining the characteristics of an observed phenomenon in a way that makes it usable by an algorithm (e.g., [3]). This process often includes domain knowledge to make the features, as well as the results of the algorithms, meaningful in the respective application area. In data analysis generally, including visual data analysis, the obtained results or insights are often dependent on the employed analysis method as well as the parameters and their imensions used. A simple but well-known example is the modifiable area unit problem [5]. Depending on the size and form of the spatial units chosen to aggregate the data, different visualizations and potentially interpretations of the information may result. In some cases, the chosen methods or algorithms and their parameters can be argued to be the right ones to support a specific analysis task, in other cases a sensitivity analysis may be helpful in determining the optimal values. Additionally, visual analytics, allowing tight integration of the interaction with the methods and parameters and the visualizations, has the potential to support the evaluation of the right or sensible analysis method and its parameters as well as to provide provenance information for the finally employed approach.
    05 - Forschungs- oder Arbeitsbericht