Browsing by Author "Shao, Lin"

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    Recognizing User Behavior from Interactions for Adaptive Consumer Information Systems
    (The Eurographics Association, 2023) Lengauer, Stefan; Bedek, Michael A.; Kupfer, Cordula; Shao, Lin; Albert, Dietrich; Schreck, Tobias; Pelechano, Nuria; Liarokapis, Fotis; Rohmer, Damien; Asadipour, Ali
    Consumer Information Systems, which experience widespread application, benefit substantially from adapting the conveyed information to specific user needs, by addressing various impairments such as color blindness, deficient preknowledge, and/or graph illiteracy. Ideally, to allow for an unperturbed exploration process, the system automatically recognizes and responds to the need for adaptation. While it has been shown that users' interactions with a system can be leveraged to this end, there exists no generalized taxonomy covering all possible interactions/processes and how they relate to each other. This paper garners different interactions, defined in the literature, and classifies them regarding complexity and inter-dependencies in a 'processes landscape'. Using this landscape, we outline a concept how low-level interactions (e.g., 'Clicking', 'Typing') can be combined with context-sensitive ones (e.g., 'Hovering') to estimate high-level behavior such as 'Reading' or 'Exploring'. Knowledge of the latter allows a system to intervene and adapt in a reasonably manner.
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    Reordering Sets of Parallel Coordinates Plots to Highlight Differences in Clusters
    (The Eurographics Association, 2022) Koh, Elliot; Blumenschein, Michael; Shao, Lin; Schreck, Tobias; Bernard, Jürgen; Angelini, Marco
    Visualizing high-dimensional (HD) data is a key challenge for data scientists. The importance of this challenge is to properly map data properties, e.g., patterns, outliers, and correlations, from a HD data space onto a visualization. Parallel coordinate plots (PCPs) are a common way to do this. However, a PCP visualization can be arranged in several ways by reordering its axes, which may lead to different visual representations. Many methods have been developed with the aim of evaluating the quality of reorderings of given PCP view. A high-dimensional data set can be divided into multiple classes, and being able to identify differences between the classes is important. Then, besides overlaying the groups in a single PCP, we can show the different groups in individual PCPs in a small multiple fashion. This raises the problem of jointly reordering sets of PCPs to create meaningful reorderings of the set of plots. We propose a joint reordering strategy, based on maximizing the pairwise visual difference in PCPs, such as to support their contrastive comparison. We present an implementation and an evaluation of the reordering strategy to assess the effectiveness of the method. The approach shows feasible in bringing out pairwise difference in PCP plots and hence support comparison of grouped data.