Browsing by Author "Tytarenko, Mariia"

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    GUIDÆTA - A Versatile Interactions Dataset with extensive Context Information and Metadata
    (The Eurographics Association, 2025) Lengauer, Stefan; Götz, Sarah Annabelle von; Hoesch, Marie-Therese; Steinwidder, Florian; Tytarenko, Mariia; Bedek, Michael A.; Schreck, Tobias; Comino Trinidad, Marc; Mancinelli, Claudio; Maggioli, Filippo; Romanengo, Chiara; Cabiddu, Daniela; Giorgi, Daniela
    Interaction data is widely used in multiple domains such as cognitive science, visualization, human computer interaction, and cybersecurity, among others. Applications range from cognitive analyses over user/behavior modeling, adaptation, recommendations, to (user/bot) identification/verification. That is, research on these applications - in particular those relying on learned models - require copious amounts of structured data for both training and evaluation. Different application domains thereby impose different requirements. I.e., for some purposes it is vital that the data is based on a guided interaction process, meaning that monitored subjects pursued a given task, while other purposes require additional context information, such as widget interactions or metadata. Unfortunately, the amount of publicly available datasets is small and their respective applicability for specific purposes limited. We present GUIDEd Interaction DATA (GUIDÆTA) - a new dataset, collected from a large-scale guided user study with more than 250 users, each working on three pre-defined information retrieval tasks using a custom-built consumer information system. Besides being larger than most comparable datasets - with 716 completed tasks, 2.39 million mouse and keyboard events (2.35 million and 40 thousand, respectively) and a total observation period of almost 50 hours - its interactions exhibit encompassing context information in the form of widget information, triggered (system) events and associated displayed content. Combined with extensive metadata such as sociodemographic user data and answers to explicit feedback questionnaires (regarding perceived usability, experienced cognitive load, pre-knowledge on the information system's topic), GUIDÆTA constitutes a versatile dataset, applicable for various research domains and purposes. Alongside the data itself, we publish the software tools we use for handling and analyzing the dataset.
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    Hierarchical Topic Maps for Visual Exploration and Comparison of Documents
    (The Eurographics Association, 2024) Tytarenko, Mariia; Shao, Lin; Rutar, Tobias Walter; Bedek, Michael A.; Krenn, Cornelia; Lengauer, Stefan; Schreck, Tobias; El-Assady, Mennatallah; Schulz, Hans-Jörg
    Information visualization nowadays provides a large amount of different text visualization techniques that help to summarize and present textual information in an intuitive and comprehensible manner. Despite many advancements, there remains a gap in effectively illustrating the thematic and structural distinction between similar documents in a hierarchical and interactive manner. We present the Hierarchical Topic Maps (HTM), an innovative approach, inspired by Tile Bars, that addresses this gap by illustrating the content distribution across a document hierarchically. Our model incorporates a multi-resolution display feature, enabling users, in particular curators of large document collections, with the need to quickly obtain text document structure, to delve deeper and draw more meaningful conclusions, to assess thematic similarities at multiple levels of detail, as well as facilitate nuanced comparison of textual documents. We demonstrate the effectiveness of both our approach's document exploration and document comparison potential by two exemplary use case scenarios. Our findings suggest that HTM not only simplifies the document overview process but also provides a practical solution for comparing thematic structures, thereby offering contributions to the field of text visualization and visualization analytics.