EuroVA2023

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https://diglib.eg.org/handle/10.2312/2633347

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Browsing EuroVA2023 by Subject "Computing methodologies"

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    ChatKG: Visualizing Temporal Patterns as Knowledge Graph
    (The Eurographics Association, 2023) Christino, Leonardo; Paulovich, Fernando V.; Angelini, Marco; El-Assady, Mennatallah
    Line-chart visualizations of temporal data enable users to identify interesting patterns for the user to inquire about. Using oracles, such as chat AIs, Visual Analytic tools can automatically uncover explicit knowledge related information to said patterns. Yet, visualizing the association of data, patterns, and knowledge is not straightforward. In this paper, we present ChatKG, a novel visualization strategy that allows exploratory data analysis of a Knowledge Graph which associates a dataset of temporal sequences, the patterns found in each sequence, the temporal overlap between patterns, and related explicit knowledge to each given pattern. We exemplify and informally evaluate ChatKG by analyzing the world's life expectancy. For this, we implement an oracle that automatically extracts relevant or interesting patterns, inquires chatGPT for related information, and populates the Knowledge Graph which is visualized. Our tests and an interview conducted showed that ChatKG is well suited for temporal analysis of temporal patterns and their related knowledge when applied to history studies.
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    Nonparametric Dimensionality Reduction Quality Assessment based on Sortedness of Unrestricted Neighborhood
    (The Eurographics Association, 2023) Pereira-Santos, Davi; Neves, Tácito Trindade Araújo Tiburtino; Carvalho, André C. P. L. F. de; Paulovich, Fernando V.; Angelini, Marco; El-Assady, Mennatallah
    High-dimensional data are known to be challenging to explore visually. Dimensionality Reduction (DR) techniques are good options for making high-dimensional data sets more interpretable and computationally tractable. An inherent question regarding their use is how much relevant information is lost during the layout generation process. In this study, we aim to provide means to quantify the quality of a DR layout according to the intuitive notion of sortedness of the data points. For such, we propose a straightforward measure with Kendall t at its core to provide values in a standard and meaningful interval. We present sortedness and pairwise sortedness as suitable replacements over, respectively, trustworthiness and stress when assessing projection quality. The formulation, its rationale and scope, and experimental results show their strength compared to the state-of-the-art.