Browsing by Author "Paulovich, Fernando V."

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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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    A Generic Model for Projection Alignment Applied to Neural Network Visualization
    (The Eurographics Association, 2020) Cantareira, Gabriel Dias; Paulovich, Fernando V.; Turkay, Cagatay and Vrotsou, Katerina
    Dimensionality reduction techniques are popular tools for the visualization of neural network models due to their ability to display hidden layer activations and aiding the understanding of how abstract representations are being formed. However, many techniques render poor results when used to compare multiple projections resulted from different feature sets, such as the outputs of different hidden layers or the outputs from different models processing the same data. This problem occurs due to the lack of an alignment factor to ensure that visual differences represent actual differences between the feature sets and not artifacts generated by the technique. In this paper, we propose a generic model to align multiple projections when visualizing different feature sets that can be applied to any gradient descent-based dimensionality reduction technique. We employ this model to generate a variant of the UMAP method and show the results of its application.
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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.
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    Progressive Multidimensional Projections: A Process Model based on Vector Quantization
    (The Eurographics Association, 2020) Ventocilla, Elio Alejandro; Martins, Rafael M.; Paulovich, Fernando V.; Riveiro, Maria; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
    As large datasets become more common, so becomes the necessity for exploratory approaches that allow iterative, trial-anderror analysis. Without such solutions, hypothesis testing and exploratory data analysis may become cumbersome due to long waiting times for feedback from computationally-intensive algorithms. This work presents a process model for progressive multidimensional projections (P-MDPs) that enables early feedback and user involvement in the process, complementing previous work by providing a lower level of abstraction and describing the specific elements that can be used to provide early system feedback, and those which can be enabled for user interaction. Additionally, we outline a set of design constraints that must be taken into account to ensure the usability of a solution regarding feedback time, visual cluttering, and the interactivity of the view. To address these constraints, we propose the use of incremental vector quantization (iVQ) as a core step within the process. To illustrate the feasibility of the model, and the usefulness of the proposed iVQ-based solution, we present a prototype that demonstrates how the different usability constraints can be accounted for, regardless of the size of a dataset.