Browsing by Author "Seebacher, Daniel"

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    CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling
    (The Eurographics Association and John Wiley & Sons Ltd., 2021) Fischer, Maximilian T.; Seebacher, Daniel; Sevastjanova, Rita; Keim, Daniel A.; El-Assady, Mennatallah; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
    Communication consists of both meta-information as well as content. Currently, the automated analysis of such data often focuses either on the network aspects via social network analysis or on the content, utilizing methods from text-mining. However, the first category of approaches does not leverage the rich content information, while the latter ignores the conversation environment and the temporal evolution, as evident in the meta-information. In contradiction to communication research, which stresses the importance of a holistic approach, both aspects are rarely applied simultaneously, and consequently, their combination has not yet received enough attention in automated analysis systems. In this work, we aim to address this challenge by discussing the difficulties and design decisions of such a path as well as contribute CommAID, a blueprint for a holistic strategy to communication analysis. It features an integrated visual analytics design to analyze communication networks through dynamics modeling, semantic pattern retrieval, and a user-adaptable and problem-specific machine learning-based retrieval system. An interactive multi-level matrix-based visualization facilitates a focused analysis of both network and content using inline visuals supporting cross-checks and reducing context switches. We evaluate our approach in both a case study and through formative evaluation with eight law enforcement experts using a real-world communication corpus. Results show that our solution surpasses existing techniques in terms of integration level and applicability. With this contribution, we aim to pave the path for a more holistic approach to communication analysis.
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    A Comprehensive Workflow for Effective Imitation and Reinforcement Learning with Visual Analytics
    (The Eurographics Association, 2022) Metz, Yannick; Schlegel, Udo; Seebacher, Daniel; El-Assady, Mennatallah; Keim, Daniel; Bernard, Jürgen; Angelini, Marco
    Multiple challenges hinder the application of reinforcement learning algorithms in experimental and real-world use cases even with recent successes in such areas. Such challenges occur at different stages of the development and deployment of such models. While reinforcement learning workflows share similarities with machine learning approaches, we argue that distinct challenges can be tackled and overcome using visual analytic concepts. Thus, we propose a comprehensive workflow for reinforcement learning and present an implementation of our workflow incorporating visual analytic concepts integrating tailored views and visualizations for different stages and tasks of the workflow.
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    Visual Analytics of Conversational Dynamics
    (The Eurographics Association, 2019) Seebacher, Daniel; Fischer, Maximilian T.; Sevastjanova, Rita; Keim, Daniel A.; El-Assady, Mennatallah; Landesberger, Tatiana von and Turkay, Cagatay
    Large-scale interaction networks of human communication are often modeled as complex graph structures, obscuring temporal patterns within individual conversations. To facilitate the understanding of such conversational dynamics, episodes with low or high communication activity as well as breaks in communication need to be detected to enable the identification of temporal interaction patterns. Traditional episode detection approaches are highly dependent on the choice of parameters, such as window-size or binning-resolution. In this paper, we present a novel technique for the identification of relevant episodes in bi-directional interaction sequences from abstract communication networks. We model communication as a continuous density function, allowing for a more robust segmentation into individual episodes and estimation of communication volume. Additionally, we define a tailored feature set to characterize conversational dynamics and enable a user-steered classification of communication behavior. We apply our technique to a real-world corpus of email data from a large European research institution. The results show that our technique allows users to effectively define, identify, and analyze relevant communication episodes.