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visual data exploration and analysis v

This volume 3298 of the conference proceedings contains 32 papers. Topics discussed include web based visualization, virtual reality and sound, multidimensional visualization, uncertainty visualization, timed-based vi... 详细信息

关键词： data reduction

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Contextualized visual analytics for multivariate events

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visual INFORMATICS 2025年第2期9卷

作者： Peng, Lei Lin, Ziyue Andrienko, Natalia Andrienko, Gennady Chen, Siming Fudan Univ Sch Data Sci Shanghai Peoples R China Fraunhofer IAIS St Augustin Germany City Univ London London England

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 visual analysis 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 visual analysis 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/).

关键词： visual analytics Event analysis Contextualized analysis Interactive exploration visualization design

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visual analysis of multi-subject association patterns in high-dimensional time-varying student performance data

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visual INFORMATICS 2025年第2期9卷

作者： Ji, Lianen Wang, Ziyi Qiu, Shirong Yang, Guang Zhang, Sufang China Univ Petr Dept Comp Sci & Technol Beijing 102249 Peoples R China China Univ Petr Beijing Key Lab Petr Data Min Beijing Peoples R China

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 visual analysis 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/).

关键词： Learning pattern Course grades Multi-subject Association pattern visual analysis

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EmotionLens: Interactive visual exploration of the circumplex emotion space in literary works via affective word clouds

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visual INFORMATICS 2025年第1期9卷 84-98页

作者： Wang, Bingyuan Shi, Qing Wang, Xiaohan Zhou, You Zeng, Wei Wang, Zeyu Hong Kong Univ Sci & Technol Guangzhou Guangzhou Peoples R China Hong Kong Univ Sci & Technol Hong Kong Peoples R China

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/).

关键词： Emotion visualization Word cloud Emotion analysis Literary text analysis Digital humanities

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Uncovering temporal patterns in visualizations of high-dimensional data

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MACHINE LEARNING 2025年第2期114卷 1-27页

作者： Policar, Pavlin G. Zupan, Blaz Univ Ljubljana Fac Comp & Informat Sci Vecna Pot 113 Ljubljana 1000 Slovenia Baylor Coll Med Dept Educ Innovat & Technol 1 Baylor Plz Houston TX 77030 USA

With the increasing availability of high-dimensional data, analysts often rely on exploratory data analysis 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 visual exploration. 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 data analysis.

关键词： Temporal-data visualization Dimensionality reduction data visualization

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visual comparative analytics of multimodal transportation

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visual INFORMATICS 2025年第1期9卷 18-30页

作者： Deng, Zikun Chen, Haoming Lu, Qing-Long Su, Zicheng Schreck, Tobias Bao, Jie Cai, Yi South China Univ Technol Sch Software Engn Guangzhou Peoples R China South China Univ Technol Key Lab Big Data & Intelligent Robot Guangzhou Peoples R China Natl Univ Singapore Dept Civil & Environm Engn Singapore Singapore Tongji Univ Key Lab Rd & Traff Engn Minist Educ Shanghai Peoples R China Graz Univ Technol Graz Austria JD Intelligent Cities Res Beijing Peoples R China

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 visual exploration. 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/).

关键词： Traffic visualization Multimodal transportation Comparative analysis

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vitessce: integrative visualization of multimodal and spatially resolved single-cell data

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NATURE METHODS 2025年第1期22卷 63-67页

作者： Keller, Mark S. Gold, Ilan McCallum, Chuck Manz, Trevor Kharchenko, Peter v. Gehlenborg, Nils Harvard Med Sch Dept Biomed Informat Boston MA 02115 USA Broad Inst MIT & Harvard Cambridge MA USA Altos Labs San Diego CA USA

Multiomics technologies with single-cell and spatial resolution make it possible to measure thousands of features across millions of cells. However, visual analysis 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.

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Decoding Learner Behavior in virtual Reality Education: Insights from Epistemic Network analysis and Differential Sequence Mining

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IEEE Transactions on visualization and Computer Graphics 2025年第5期31卷 2675-2683页

作者： Prakash, Antony Rajendran, Ramkumar Indian Institute of Technology Bombay India

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

关键词： virtual environments

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Small vision-Language Models: A Survey on Compact Architectures and Techniques

arXiv

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arXiv 2025年

作者： Patnaik, Nitesh Nayak, Navdeep Agrawal, Himani Bansal Khamaru, Moinak Chinmoy Bal, Gourav Panda, Saishree Smaranika Raj, Rishi Meena, vishal vadlamani, Kartheek Deemed to be University Odisha Bhubaneswar751024 India Information Systems Indian Institute of Management Visakhapatnam India Remodel AI

The emergence of small vision-language models (svLMs) marks a critical advancement in multimodal AI, enabling efficient processing of visual and textual data in resource-constrained environments. This survey offers a comprehensive exploration of svLM development, presenting a taxonomy of architectures—transformer-based, mamba-based, and hybrid—that highlight innovations in compact design and computational efficiency. Techniques such as knowledge distillation, lightweight attention mechanisms, and modality pre-fusion are discussed as enablers of high performance with reduced resource requirements. Through an in-depth analysis of models like TinyGPT-v, MiniGPT-4, and vL-Mamba, we identify trade-offs between accuracy, efficiency, and scalability. Persistent challenges, including data biases and generalization to complex tasks, are critically examined, with proposed pathways for addressing them. By consolidating advancements in svLMs, this work underscores their transformative potential for accessible AI, setting a foundation for future research into efficient multimodal systems. Copyright © 2025, The Authors. All rights reserved.

关键词： visual languages

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Network Narratives: data Tours for visual Network exploration and analysis 23

Network Narratives: Data Tours for Visual Network Exploratio...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Li, Wenchao Schottler, Sarah Scott-Brown, James Wang, Yun Chen, Siming Qu, Huamin Bach, Benjamin Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland Microsoft Res Asia Beijing Peoples R China Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781450394215

This paper introduces semi-automatic data tours to aid the exploration of complex networks. Exploring networks requires significant effort and expertise and can be time-consuming and challenging. Distinct from guidance and recommender systems for visual analytics, we provide a set of goal-oriented tours for network overview, ego-network analysis, community exploration, and other tasks. Based on interviews with five network analysts, we developed a user interface (NetworkNarratives) and 10 example tours. The interface allows analysts to navigate an interactive slideshow featuring facts about the network using visualizations and textual annotations. On each slide, an analyst can freely explore the network and specify nodes, links, or subgraphs as seed elements for follow-up tours. Two studies, comprising eight expert and 14 novice analysts, show that data tours reduce exploration effort, support learning about network exploration, and can aid the dissemination of analysis results. NetworkNarratives is available online, together with detailed illustrations for each tour.

关键词： Guided exploration network visualization

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