For event analysis, the information from both before and after the event can be crucial in certain scenarios. By incorporating a contextualized perspective in event analysis, analysts can gain deeper insights from the...
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For event analysis, the information from both before and after the event can be crucial in certain scenarios. By incorporating a contextualized perspective in event analysis, analysts can gain deeper insights from the events. We propose a contextualized visualanalysis framework which enables the identification and interpretation of temporal patterns within and across multivariate events. The framework consists of a design of visual representation for multivariate event contexts, a data processing workflow to support the visualization, and a context-centered visualanalysis system to facilitate the interactive exploration of temporal patterns. To demonstrate the applicability and effectiveness of our framework, we present case studies using real-world datasets from two different domains and an expert study conducted with experienced data analysts. (c) 2025 The Authors. Published by Elsevier B.v. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
Exploring the association patterns of student performance in depth can help administrators and teachers optimize the curriculum structure and teaching plans more specifically to improve teaching effectiveness in a col...
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Exploring the association patterns of student performance in depth can help administrators and teachers optimize the curriculum structure and teaching plans more specifically to improve teaching effectiveness in a college undergraduate major. However, these high-dimensional time-varying student performance data involve multiple associated subjects, such as student, course, and teacher, which exhibit complex interrelationships in academic semesters, knowledge categories, and student groups. This makes it challenging to conduct a comprehensive analysis of association patterns. To this end, we construct a visualanalysis framework, called MAPvis, to support multi-method and multilevel interactive exploration of the association patterns in student performance. MAPvis consists of two stages: in the first stage, we extract students' learning patterns and further introduce mutual information to explore the distribution of learning patterns;in the second stage, various learning patterns and subject attributes are integrated based on a hierarchical apriori algorithm to achieve a multi-subject interactive exploration of the association patterns among students, courses, and teachers. Finally, we conduct a case study using real student performance data to verify the applicability and effectiveness of MAPvis. (c) 2025 The Authors. Published by Elsevier B.v. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
Emotion (e.g., valence and arousal) is an important factor in literature (e.g., poetry and prose), and has rich values for plotting the life and knowledge of historical figures and appreciating the aesthetics of liter...
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Emotion (e.g., valence and arousal) is an important factor in literature (e.g., poetry and prose), and has rich values for plotting the life and knowledge of historical figures and appreciating the aesthetics of literary works. Currently, digital humanities and computational literature apply data statistics extensively in emotion analysis but lack visual analytics for efficient exploration. To fill the gap, we propose a user-centric approach that integrates advanced machine learning models and intuitive visualization for emotion analysis in literature. We make three main contributions. First, we consolidate a new emotion dataset of literary works in different periods, literary genres, and language contexts, augmented with fine-grained valence and arousal labels. Next, we design an interactive visual analytic system named EmotionLens, which allows users to perform multi-granularity (e.g., individual, group, society) and multi-faceted (e.g., distribution, chronology, correlation) analyses of literary emotions, supporting both exploratory and confirmatory approaches in digital humanities. Specifically, we introduce a novel affective word cloud with augmented word weight, position, and color, to facilitate literary text analysis from an emotional perspective. To validate the usability and effectiveness of EmotionLens, we provide two consecutive case studies, two user studies, and interviews with experts from different domains. Our results show that EmotionLens bridges literary text, emotion, and various other attributes, enables efficient knowledge discovery in massive data, and facilitates raising and validating domain-specific hypotheses in literature. (c) 2025 The Authors. Published by Elsevier B.v. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).
With the increasing availability of high-dimensional data, analysts often rely on exploratory dataanalysis to understand complex data sets. A key approach to exploring such data is dimensionality reduction, which emb...
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With the increasing availability of high-dimensional data, analysts often rely on exploratory dataanalysis to understand complex data sets. A key approach to exploring such data is dimensionality reduction, which embeds high-dimensional data in two dimensions to enable visualexploration. However, popular embedding techniques, such as t-SNE and UMAP, typically assume that data points are independent. When this assumption is violated, as in time-series data, the resulting visualizations may fail to reveal important temporal patterns and trends. To address this, we propose a formal extension to existing dimensionality reduction methods that incorporates two temporal loss terms that explicitly highlight temporal progression in the embedded visualizations. Through a series of experiments on both synthetic and real-world datasets, we demonstrate that our approach effectively uncovers temporal patterns and improves the interpretability of the visualizations. Furthermore, the method improves temporal coherence while preserving the fidelity of the embeddings, providing a robust tool for dynamic dataanalysis.
By allowing to conduct experiments involving eco-logically valid tasks within controlled environments, virtual Real-ity (vR) offers novel opportunities for studying human behavior. Several modalities can be leveraged,...
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Users often begin exploratory visualanalysis (EvA) without clear analysis goals but iteratively refine them as they learn more about their data. As an essential step in data science, researchers want to aid EvA by de...
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Contemporary urban transportation systems frequently depend on a variety of modes to provide residents with travel services. Understanding a multimodal transportation system is pivotal for devising well-informed plann...
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Contemporary urban transportation systems frequently depend on a variety of modes to provide residents with travel services. Understanding a multimodal transportation system is pivotal for devising well-informed planning;however, it is also inherently challenging for traffic analysts and planners. This challenge stems from the necessity of evaluating and contrasting the quality of transportation services across multiple modes. Existing methods are constrained in offering comprehensive insights into the system, primarily due to the inadequacy of multimodal traffic data necessary for fair comparisons and their inability to equip analysts and planners with the means for exploration and reasoned analysis within the urban spatial context. To this end, we first acquire sufficient multimodal trips leveraging well-established navigation platforms that can estimate the routes with the least travel time given an origin and a destination (an OD pair). We also propose TraDyssey, a visual analytics system that enables analysts and planners to evaluate and compare multiple modes by exploring acquired massive multimodal trips. TraDyssey follows a streamlined query-and-explore workflow supported by user-friendly and effective interactive visualizations. Specifically, a revisited difference-aware parallel coordinate plot (PCP) is designed for overall mode comparisons based on multimodal trips. Trip groups can be flexibly queried on the PCP based on differential features across modes. The queried trips are then organized and presented on a geographic map by OD pairs, forming a group-OD-trip hierarchy of visualexploration. Domain experts gained valuable insights into transportation planning through real-world case studies using TraDyssey. (c) 2025 The Authors. Published by Elsevier B.v. on behalf of Zhejiang University and Zhejiang University Press Co. Ltd. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).
Multiomics technologies with single-cell and spatial resolution make it possible to measure thousands of features across millions of cells. However, visualanalysis of high-dimensional transcriptomic, proteomic, genom...
Multiomics technologies with single-cell and spatial resolution make it possible to measure thousands of features across millions of cells. However, visualanalysis of high-dimensional transcriptomic, proteomic, genome-mapped and imaging data types simultaneously remains a challenge. Here we describe vitessce, an interactive web-based visualization framework for exploration of multimodal and spatially resolved single-cell data. We demonstrate integrative visualization of millions of data points, including cell-type annotations, gene expression quantities, spatially resolved transcripts and cell segmentations, across multiple coordinated views. The open-source software is available at http://***. vitessce is a robust and versatile web-based framework for interactive visualization of large-scale multiomics and spatial data at the single-cell level.
The paper presents a recommender algorithm for visualanalysis based on data field Schema and Aggregation, and developed an automated dataanalysis solution recommendation system (AutoEDA) in conjunction with the Expl...
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The integration of immersive virtual Reality (I-vR) technology in education has emerged as a promising approach for enhancing learning experiences. There is a handful of research done to study the impact of I-vR on le...
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The integration of immersive virtual Reality (I-vR) technology in education has emerged as a promising approach for enhancing learning experiences. There is a handful of research done to study the impact of I-vR on learning outcomes, comparison of learning using I-vR and other traditional learning methods, and the impact of values such as haptic sensation, and verbal and non-verbal cues on the learning outcomes. However, there is a dearth of research on understanding how learning is happening from the perspective of the behavior of the learners in the virtual Reality Learning Environment (vRLE). To address this gap, we developed an Interaction Behavioral data (IBD) logging mechanism to log all the interaction traces that constitute the behavior of the learners in a virtual Reality Learning Environment (vRLE). We deployed the IBD logging mechanism in a vRLE used to learn electromagnetic induction concepts and conducted a study with 30 undergraduate computer science students. We extract the learners' actions from the logged data and contextualize them based on the action features such as duration (Long and Short), and frequency of occurrence (First and Repeated occurrence). In this paper, we investigate the actions extracted from logged interaction trace data to understand the behaviors that lead to high and low performance in the vRLE. Using Epistemic Network analysis (ENA), we identify differences in prominent actions and co-occurring actions between high and low performers. Additionally, we apply Differential Sequence Mining (DSM) to uncover significant action patterns, involving multiple actions, that are differentially frequent between these two groups. Our findings demonstrate that high performers engage in structured, iterative patterns of experimentation and evaluation, while low performers exhibit less focused exploration patterns. The insights gained from ENA and DSM highlight the behavioral variations between high and low performers in the vRLE, providing v
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