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Video visualization and Visual Analytics: A Task-Based and Application-Driven Investigation

ieee TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第11期34卷 11316-11339页

作者： Xia, Wang Sun, Guodao Li, Tong Chang, Baofeng Tang, Jingwei Zhang, Gefei Liang, Ronghua Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Peoples R China

Video data refers to digital information in the form of a series of frames or images representing continuous motion captured by a video recording device. In various domains such as security, sports, education, and entertainment, a significant amount of video data is generated and stored daily. However, analyzing these videos manually is challenging due to their intrinsic characteristics, including large-scale, redundancy, contextual dependencies, and multimodality. Consequently, researchers have extensively explored visualization techniques to address these complexities. In this investigation, we review the state-of-the-art techniques in video visualization and visual analysis. Initially, we provide an overview of the design space for video visualization and visual analysis techniques. Subsequently, we organize and classify these techniques based on visual analysis tasks and application scenarios, providing detailed descriptions within each category. Drawing upon a comprehensive review of existing research, we provide a critical evaluation and propose potential opportunities for future research. Additionally, we have developed a web-based survey browser for convenient exploration of our created classification framework and the associated scholarly articles (https://***/VOVideo/).

关键词： Video visualization video analysis visual analytics video data survey

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A Customized Validator Recommender System for PoS Networks Using Similarity-Based Circular visualization 14

A Customized Validator Recommender System for PoS Networks U...

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14th symposium on large data analysis and visualization

作者： Lee, Jaeuk Kim, Jisu Han, Hyunwoo Lee, Kyungwon Ajou Univ Dept Digital Media Suwon South Korea Ajou Univ Dept Business Intelligence Suwon South Korea Stamper Co Ltd Seoul South Korea

ISBN: (纸本)9798331516932;9798331516925

This study analyzes the impact of validator behavior on investor rewards in proof-of-stake (PoS) based blockchain networks and proposes a visualization system to assist investors in selecting appropriate validators. This system enables personalized evaluations through five adjustable indicators tailored to the investor's preferences. By utilizing similarity-based circular visualizations and radar charts, it facilitates the selection and comparison of validators. Additionally, it provides time-series data-based line graphs and raw data-based table views to support detailed comparative analysis among validators. The introduction of such a multifaceted evaluation methodology in staking investments is expected to contribute to the formation of user-customized portfolios and the establishment of optimized investment strategies.

关键词： Circular visualization Blockchain Networks Similarity-Based analysis Customized Recommendations

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Standardized data-Parallel Rendering Using ANARI 14

Standardized Data-Parallel Rendering Using ANARI

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14th symposium on large data analysis and visualization

作者： Wald, Ingo Zellmann, Stefan Amstutz, Jefferson Wu, Qi Griffin, Kevin Jaros, Milan Wesner, Stefan NVIDIA Santa Clara CA 95051 USA Univ Cologne Cologne Germany Univ Calif Davis Davis CA USA VSB Tech Univ Ostrava IT4Innovat Ostrava Czech Republic

ISBN: (纸本)9798331516932;9798331516925

We propose and discuss a paradigm that allows for expressing data-parallel rendering with the classically non-parallel ANARI API. We propose this as a new standard for data-parallel rendering, describe two different implementations of this paradigm, and use multiple sample integrations into existing applications to show how easy it is to adopt, and what can be gained from doing so.

关键词： anari distributed rendering ray tracing sci-vis

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Graphical Representation through a User Interface for In Situ Scientific visualization with Ascent 14

Graphical Representation through a User Interface for In Sit...

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14th symposium on large data analysis and visualization

作者： Heinemann, Colleen Amstutz, Jefferson Insley, Joseph A. Mateevitsi, Victor A. Papka, Michael E. Rizzi, Silvio Univ Illinois Urbana IL 61801 USA Argonne Natl Lab Argonne IL 60439 USA NVIDIA Santa Clara CA USA Univ Illinois Chicago IL USA Northern Illinois Univ De Kalb IL 60115 USA

ISBN: (纸本)9798331516932;9798331516925

This work presents a graphical user interface (GUI) designed to allow users to interactively create scientific visualizations and workflows with the in situ visualization software, Ascent [5]. Traditionally, in situ pipelines are configured through a YAML file, which can be cumbersome. Our GUI offers an alternative by automating the conversion of user inputs into a format that Ascent can process, eliminating the need for manual file editing. This interactive capability increases the potential for a more diverse group of users to leverage Ascent's powerful visualization tools.

关键词： In situ visualization graphical user interface (GUI) HPC simulations scientific visualization Ascent

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Visual Diagnostics of Parallel Performance in Training large-scale DNN Models

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第7期30卷 3915-3929页

作者： Wei, Yating Wang, Zhiyong Wang, Zhongwei Dai, Yong Ou, Gongchang Gao, Han Yang, Haitao Wang, Yue Cao, Caleb Chen Weng, Luoxuan Lu, Jiaying Zhu, Rongchen Chen, Wei Zhejiang Univ State Key Lab CAD & CG Hangzhou 310058 Zhejiang Peoples R China Huawei Technol Co Ltd Distributed Data Lab Shenzhen 518129 Peoples R China

Diagnosing the cluster-based performance of large-scale deep neural network (DNN) models during training is essential for improving training efficiency and reducing resource consumption. However, it remains challenging due to the incomprehensibility of the parallelization strategy and the sheer volume of complex data generated in the training processes. Prior works visually analyze performance profiles and timeline traces to identify anomalies from the perspective of individual devices in the cluster, which is not amenable for studying the root cause of anomalies. In this article, we present a visual analytics approach that empowers analysts to visually explore the parallel training process of a DNN model and interactively diagnose the root cause of a performance issue. A set of design requirements is gathered through discussions with domain experts. We propose an enhanced execution flow of model operators for illustrating parallelization strategies within the computational graph layout. We design and implement an enhanced Marey's graph representation, which introduces the concept of time-span and a banded visual metaphor to convey training dynamics and help experts identify inefficient training processes. We also propose a visual aggregation technique to improve visualization efficiency. We evaluate our approach using case studies, a user study and expert interviews on two large-scale models run in a cluster, namely, the PanGu-alpha 13B model (40 layers), and the Resnet model (50 layers).

关键词： Training data visualization Computational modeling Solid modeling Parallel processing Performance evaluation data models Deep neural network model training parallel performance visual analysis

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Progressive Tree-Based Compression of large-scale Particle data

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第7期30卷 4321-4338页

作者： Hoang, Duong Bhatia, Harsh Lindstrom, Peter Pascucci, Valerio Univ Utah Sci Comp & Imaging Inst Salt Lake City UT 84112 USA Lawrence Livermore Natl Lab Ctr Appl Sci Comp Livermore CA 94550 USA

Scientific simulations and observations using particles have been creating large datasets that require effective and efficient data reduction to store, transfer, and analyze. However, current approaches either compress only small data well while being inefficient for large data, or handle large data but with insufficient compression. Toward effective and scalable compression/decompression of particle positions, we introduce new kinds of particle hierarchies and corresponding traversal orders that quickly reduce reconstruction error while being fast and low in memory footprint. Our solution to compression of large-scale particle data is a flexible block-based hierarchy that supports progressive, random-access, and error-driven decoding, where error estimation heuristics can be supplied by the user. For low-level node encoding, we introduce new schemes that effectively compress both uniform and densely structured particle distributions. Our proposed methods thus target all three phases of a tree-based particle compression pipeline, namely tree construction, tree traversal, and node encoding. The improved efficacy and flexibility of these methods over existing compressors are demonstrated through extensive experimentation, using a wide range of scientific particle datasets.

关键词： Encoding Decoding Image reconstruction data models Computational modeling Compressors Task analysis Coarse approximation compression (coding) data compaction and compression hierarchical multiresolution particle datasets progressive decompression tree traversal visualization

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CommentVis: Unveiling Comment Insights Through Interactive visualization Tool 17

CommentVis: Unveiling Comment Insights Through Interactive V...

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17th Pacific visualization Conference (PacificVis)

作者： Yan, Guangjing Jang, Jinhwa Seo, Jinwook Seoul Natl Univ Seoul South Korea Samsung Elect Suwon South Korea

ISBN: (纸本)9798350393811;9798350393804

Recently, online forums have emerged as a stage for consumers to comment and share their reviews. These user comments serve as a valuable data source for marketing professionals and analysts. Nevertheless, conventional user interfaces often present an overwhelming volume of comments in a linear-structured list, significantly impeding the efficiency of marketing professionals in analyzing the feedback. In response to this challenge, we introduce CommentVis, an interactive visualization tool that helps users grasp the skeleton of comments' semantic distribution. Using the tool, analysts gain detailed information about comments with profound insights. The tool leverages state-of-the-art large-scale language models, optimizing the speed and depth of analysis of substantial text data that may take a long time for marketers. To illustrate the practical application of CommentVis, we present a usage scenario that demonstrates its effectiveness in real-world marketing analysis. The tool's impact and utility were further validated through a user study involving three marketing professionals in a global manufacturing company.

关键词： Human-centered computing-visualization-visualization systems and tools- Computing methodologies-Artificial intelligence-Natural language processing-

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Provenance for Longitudinal analysis in large scale Networks 16th

Provenance for Longitudinal Analysis in Large Scale Networks

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16th International Conference on Social Networks analysis and Mining

作者： Stoica, Andrei Riveni, Mirela Univ Groningen Groningen Netherlands Univ Groningen Informat Syst Grp Groningen Netherlands

ISBN: (纸本)9783031785375;9783031785382

Concerns related to the veracity and originality of the content on social networks are at an ongoing rise. Considerable work has been done on information spreading, and tools have been built, while approaches with provenance-based analysis are rare. We are of the opinion that provenance-based analysis and visualization tools can make (mis-)information spreading analysis more efficient. Thus, we study provenance, and present a provenance pipeline for data analytics, where users are able to interact with multiple network analysis modules through a graphical user interface, and describe a proof-of-concept system. Although provenance visualization can suffice in capturing all the necessary metadata, integration with other network visualization modules suited to the same data enhanced our results analysis and conclusions. Having designed distinct provenance models, we captured and analysed lineage of information on community dynamics. We tested our proposed prototype with a real-world dataset comprising of more than 10 million filtered tweets, focused on COVID-19 vaccinations, and conducted an analysis on community dynamics with network science metrics and NLP.

关键词： large scale Networks Network Science data analysis Misinformation NLP Provenance

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Quantivine: A visualization Approach for large-scale Quantum Circuit Representation and analysis

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第1期30卷 573-583页

作者： Wen, Zhen Liu, Yihan Tan, Siwei Chen, Jieyi Zhu, Minfeng Han, Dongming Yin, Jianwei Xu, Mingliang Chen, Wei Zhejiang Univ State Key Lab CAD&CG Hangzhou Peoples R China Zhejiang Univ Adv Comp & Syst Lab Hangzhou Peoples R China Zhejiang Univ Hangzhou Peoples R China Hithink Royal Flush Informat Network Co Ltd Hangzhou Zhejiang Peoples R China Zhengzhou Univ Zhengzhou Peoples R China

Quantum computing is a rapidly evolving field that enables exponential speed-up over classical algorithms. At the heart of this revolutionary technology are quantum circuits, which serve as vital tools for implementing, analyzing, and optimizing quantum algorithms. Recent advancements in quantum computing and the increasing capability of quantum devices have led to the development of more complex quantum circuits. However, traditional quantum circuit diagrams suffer from scalability and readability issues, which limit the efficiency of analysis and optimization processes. In this research, we propose a novel visualization approach for large-scale quantum circuits by adopting semantic analysis to facilitate the comprehension of quantum circuits. We first exploit meta-data and semantic information extracted from the underlying code of quantum circuits to create component segmentations and pattern abstractions, allowing for easier wrangling of massive circuit diagrams. We then develop Quantivine, an interactive system for exploring and understanding quantum circuits. A series of novel circuit visualizations is designed to uncover contextual details such as qubit provenance, parallelism, and entanglement. The effectiveness of Quantivine is demonstrated through two usage scenarios of quantum circuits with up to 100 qubits and a formal user evaluation with quantum experts. A free copy of this paper and all supplemental materials are available at https://***/2m9yh/?view_only=0aa1618c97244f5093cd7ce15f1431f9.

关键词： Quantum circuit semantic analysis visual abstraction context visualization

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Submerse: Visualizing Storm Surge Flooding Simulations in Immersive Display Ecologies

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ieee TRANSACTIONS ON visualization AND COMPUTER GRAPHICS 2024年第9期30卷 6365-6377页

作者： Boorboor, Saeed Kim, Yoonsang Hu, Ping Moses, Josef M. Colle, Brian A. Kaufman, Arie E. SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA SUNY Stony Brook Sch Marine & Atmospher Sci Stony Brook NY 11794 USA

We present Submerse, an end-to-end framework for visualizing flooding scenarios on large and immersive display ecologies. Specifically, we reconstruct a surface mesh from input flood simulation data and generate a to-scale 3D virtual scene by incorporating geographical data such as terrain, textures, buildings, and additional scene objects. To optimize computation and memory performance for large simulation datasets, we discretize the data on an adaptive grid using dynamic quadtrees and support level-of-detail based rendering. Moreover, to provide a perception of flooding direction for a time instance, we animate the surface mesh by synthesizing water waves. As interaction is key for effective decision-making and analysis, we introduce two novel techniques for flood visualization in immersive systems: (1) an automatic scene-navigation method using optimal camera viewpoints generated for marked points-of-interest based on the display layout, and (2) an AR-based focus+context technique using an aux display system. Submerse is developed in collaboration between computer scientists and atmospheric scientists. We evaluate the effectiveness of our system and application by conducting workshops with emergency managers, domain experts, and concerned stakeholders in the Stony Brook Reality Deck, an immersive gigapixel facility, to visualize a superstorm flooding scenario in New York City.

关键词： Floods data visualization Three-dimensional displays Cameras Rendering (computer graphics) data models Solid modeling Camera navigation flooding simulation immersive visualization mixed reality

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