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检索条件"任意字段=Conference on Visual Data Exploration and Analysis"

共 1754 条记录，以下是1-10 订阅

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

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

引用

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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A Tool for visual exploration and analysis of Solar Photovoltaic Module data

A Tool for Visual Exploration and Analysis of Solar Photovol...

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2023 Workshops of the EDBT/ICDT Joint conference, EDBT/ICDT-WS 2023

作者： Stamatopoulos, Vassilis Maroulis, Stavros Kozanis, Konstantinos Psarros, Ioannis Papastefanatos, George Giannopoulos, Giorgos Terrovitis, Manolis ATHENA Research Center Greece

Solar photovoltaic (PV) modules are a popular source of clean energy, and effective monitoring and optimization of their performance requires the ability to explore and analyze the measurement data from their sensors. In this work, we introduce a tool for the visual analysis of such data stored in raw measurement files, offering efficient interactive visualization directly on the files. The tool includes a soiling detection module, and novel UI modules that combine time series data visualization with advanced solar panel analytics presentation. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CEUR Workshop Proceedings (***)

关键词： Solar energy

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Uncovering Patterns in Design Spaces: An exploration Tool for the analysis of Faceted Interface Collections 24

Uncovering Patterns in Design Spaces: An Exploration Tool fo...

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17th International conference on Advanced visual Interfaces (AVI)

作者： Keck, Mandy Freller, Lisa-Maria Neumayr, Thomas Augstein, Mirjam Univ Appl Sci Upper Austria Hagenberg Campus Hagenberg Austria

ISBN: (纸本)9798400717642

Design spaces are powerful conceptual frameworks for analyzing solutions within a problem domain. We provide an exploration and analysis tool to examine the underlying data collection, compare different solutions and analyze commonly used building blocks. The web-based tool is based on javascript and *** and is avaibable online at: https://***

关键词： Design Space Faceted Browser analysis Tool Building Blocks

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visual analysis of Spinels with General Line Coordinates 28

Visual Analysis of Spinels with General Line Coordinates

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28th International conference on Information visualisation (IV)

作者： Luque, Leandro Lujan Ganuza, Maria Bjerg, Ernesto Kovalerchuk, Boris UNS Inst Comp Sci & Engn Dept Comp Sci & Engn CONICET Bahia Blanca Argentina UNS INGEOSUR CONICET Dept Geol Bahia Blanca Argentina Cent Washington Univ Dept Comp Sci Ellensburg WA USA

ISBN: (纸本)9798350380170;9798350380163

In the geological context, the analysis of multidimensional data is a very common task and requires visualization techniques for exploration. General Line Coordinates (GLC) is a technique especially intended for lossless representation among the various techniques available for the visualization of multidimensional data. In this study, the application of GLC for pattern analysis and identification in mineral datasets is investigated. Furthermore, we develop a web-based tool to explore the possibility of employing GLC to leverage these approaches to derive visual discovery rules for pattern recognition.

关键词： General Line Coordinates Multidimensional data Geochemical data

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DoodleTunes: Interactive visual analysis of Music-Inspired Children Doodles with Automated Feature Annotation 24

DoodleTunes: Interactive Visual Analysis of Music-Inspired C...

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CHI conference on Human Factors in Computing Sytems (CHI)

作者： Liu, Shuqi Bu, Jia Ye, Huayuan Chen, Juntong Jiang, Shiqi Tao, Mingtian Guo, Liping Wang, Changbo Li, Chenhui East China Normal Univ Sch Comp Sci & Technol Shanghai Peoples R China East China Normal Univ Fac Educ Shanghai Peoples R China

ISBN: (纸本)9798400703300

Music and visual arts are essential in children's arts education, and their integration has garnered signifcant attention. Existing data analysis methods for exploring audio-visual correlations are limited. Yet, relevant research is necessary for innovating and promoting arts integration courses. In our work, we collected substantial volumes of music-inspired doodles created by children and interviewed education experts to comprehend the challenges they encountered in the relevant analysis. Based on the insights we obtained, we designed and constructed an interactive visualization system DoodleTunes. DoodleTunes integrates deep learning-driven methods for automatically annotating several types of data features. The visual designs of the system are based on a four-level analysis structure to construct a progressive workfow, facilitating data exploration and insight discovery between doodle images and corresponding music pieces. We evaluated the accuracy of our feature prediction results and collected usage feedback on DoodleTunes from fve domain experts.

关键词： Multimodal data visualization data annotation user interface audio data visual analysis painting

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Enabling Tabular data exploration for Blind and Low-Vision Users

Enabling Tabular Data Exploration for Blind and Low-Vision U...

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2024 ACM Designing Interactive Systems conference, DIS 2024

作者： Wang, Yanan Srinivasan, Arjun Kim, Yea-Seul Department of Computer Sciences University of Wisconsin-Madison MadisonWI United States Databricks Inc. SeattleWA United States

ISBN: (纸本)9798400705830

In a data-driven society, being able to examine data on one’s own terms is crucial for various aspects of well-being. However, current data exploration paradigms, such as Exploratory data analysis (EDA), heavily rely on visualizations to unveil patterns and insights. This visual-centric approach poses signifcant challenges for blind and low-vision (BLV) individuals. To address this gap, we built a prototype that supports non-visual data exploration and conducted an observational user study involving 18 BLV participants. Participants were asked to conduct various analytical tasks, as well as free exploration of provided datasets. The study fndings provide insights into the factors infuencing inefcient data exploration and characterizations of BLV participants’ analytical behaviors. We conclude by highlighting future avenues of research for the design of data exploration tools for BLV users. © 2024 Copyright held by the owner/author(s).

关键词： Human engineering

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Dash: A Bimodal data exploration Tool for Interactive Text and visualizations

Dash: A Bimodal Data Exploration Tool for Interactive Text a...

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2024 IEEE visualization and visual Analytics conference, VIS 2024

作者： Bromley, Dennis Setlur, Vidya Tableau Research United States

ISBN: (纸本)9798350354850

Integrating textual content, such as titles, annotations, and captions, with visualizations facilitates comprehension and takeaways during data exploration. Yet current tools often lack mechanisms for integrating meaningful long-form prose with visual data. This paper introduces DASH, a bimodal data exploration tool that supports integrating semantic levels into the interactive process of visualization and text-based analysis. DASH operationalizes a modified version of Lundgard et al.'s semantic hierarchy model that catego-rizes data descriptions into four levels ranging from basic encodings to high-level insights. By leveraging this structured semantic level framework and a large language model's text generation capabilities, DASH enables the creation of data-driven narratives via drag-and-drop user interaction. Through a preliminary user evaluation, we discuss the utility of DASH's text and chart integration capabilities when participants perform data exploration with the tool. © 2024 IEEE.

关键词： Spatio-temporal data

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A Personalized AI Assistant For Intuition-Driven visual explorations 20

A Personalized AI Assistant For Intuition-Driven Visual Expl...

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20th IEEE International conference on E-Science (E-Science)

作者： Hammer, James Hobson, Tanner Pugmire, David Klasky, Scott Moreland, Kenneth Huang, Jian Univ Tennessee Dept EECS Knoxville TN USA Oak Ridge Natl Lab CSMD Div Oak Ridge TN USA

ISBN: (纸本)9798350365627;9798350365610

Understanding the time-varying magnetic field within a fusion device is critical for the successful design and construction of clean-burning fusion power plants. Poincare analysis provides a powerful method for the analysis and visualization of magnetic field lines in fusion devices. Current state-of-the-art relies on manually and iteratively generating Poincare plots from simulation data. Using Poincare plots in deep analysis is very time consuming because Poincare plots can be very computationally expensive, especially for a time-varying simulation with thousands of time steps. Further, the visualization results are so complex that only expert users know how to explore, interpret, and control. In addition, collaboration is hampered due to the high barrier to entry. To this end, we contribute Fugent, a reinforcement learning-based agent capable of recommending and evaluating the importance of exploration regions based on training data captured from historic expert user usage. Using Fugent, we show that important regions can be identified and recommended for further exploration. Fugent is open source.

关键词： visual exploration Reinforcement Learning Recommendation System

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A Conceptual Framework for Immersive Behavioral data exploration Assisted by a Deep Learned Representation of data 7

A Conceptual Framework for Immersive Behavioral Data Explora...

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7th IEEE International conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Duvivier, Victor Da Silva, Matthieu Perreira Prié, Yannick Nantes Université École Centrale Nantes Cnrs LS2N Umr 6004 Nantes44300 France

ISBN: (纸本)9798331521578

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, including event logs, motion trajectories, eye-tracking data, or physiological signals. However, analyzing such multimodal data presents considerable challenges due to their inherent complexity, the varied structures they imply, as well as the necessity to use exploratory approaches. There is therefore a need to design visual analytics tools for the exploration of immersive behavioral data without prior knowledge. Our idea is to integrate deep learned computational models -which leverage advanced techniques to extract valuable high-level features from unlabeled data- into exploratory visual Analytics tools. We introduce a conceptual framework for integrating deep learning models into the visual Analytics process of immersive behavioral data analysis, also focusing on the services that such systems should provide to the analysts. © 2025 IEEE.

关键词： visual analytics

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Towards a visual Perception-Based analysis of Clustering Quality Metrics

Towards a Visual Perception-Based Analysis of Clustering Qua...

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2024 conference on visualization in data Science

作者： Blasilli, Graziano Kerrigan, Daniel Bertini, Enrico Santucci, Giuseppe Sapienza Univ Roma Rome Italy Northeastern Univ Boston MA USA

ISBN: (纸本)9798331528423;9798331528430

Clustering is an essential technique across various domains, such as data science, machine learning, and explainable artificial intelligence. Information visualization and visual analytics techniques have been proven to effectively support human involvement in the visual exploration of clustered data to enhance the understanding and refinement of cluster assignments. To support the human involvement, several perceptual studies and visual quality metrics have already been proposed. However, the visual perception of clustering quality metrics, also known as Cluster Validity Indexes (CVIs), still remains to be further explored. This paper presents the first attempt of a deep and exhaustive evaluation of the perceptive aspects of clustering quality metrics, focusing on the Davies-Bouldin Index, Dunn Index, Calinski-Harabasz Index, and Silhouette Score. Our research is centered around two main objectives: a) assessing the human perception of common CVIs in 2D scatterplots and b) exploring the potential of Large Multimodal Models, in particular GPT-4o, to emulate the assessed human perception. To this end, we conducted two systematic data studies and a user study covering a broad collection of datasets. By discussing the obtained results, highlighting limitations, and areas for further exploration, this paper aims to propose a foundation for future research activities.

关键词： Clustering Clustering Quality Metrics Clustering Validity Indexes visual Perception Large Multimodal Models LMM

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