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检索条件"任意字段=VAST IEEE Symposium on Visual Analytics Science and Technology 2007"

共 428 条记录，以下是1-10 订阅

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vast Reviewers

VAST Reviewers

引用

ieee symposium on visual analytics science and technology (vast)

The conference offers a note of thanks and lists its reviewers.

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

The conference offers a note of thanks and lists its reviewers.

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来源：评论

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vast Program Committee

VAST Program Committee

引用

ieee symposium on visual analytics science and technology (vast)

Provides a listing of current committee members and society officers.

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

Provides a listing of current committee members and society officers.

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Preface to ieee vast 2020 Conference Track and vast Challenge

Preface to IEEE VAST 2020 Conference Track and VAST Challeng...

引用

ieee symposium on visual analytics science and technology (vast)

Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

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Diagnosing Concept Drift with visual analytics

Diagnosing Concept Drift with Visual Analytics

引用

ieee symposium on visual analytics science and technology (vast)

作者： Weikai Yang Zhen Li Mengchen Liu Yafeng Lu Kelei Cao Ross Maciejewski Shixia Liu School of Software BNRist Tsinghua University Microsoft Bloomberg L.P. Computer Science Arizona State University

The following topics are dealt with: data visualisation; data analysis; learning (artificial intelligence); complex networks; data mining; text analysis; pattern classification; weather forecasting; proteins; cancer.

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

关键词： Analytical models Data models Adaptation models Predictive models visual analytics Training Data visualization

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ConceptExplorer: visual Analysis of Concept Drifts in Multi-source Time-series Data

ConceptExplorer: Visual Analysis of Concept Drifts in Multi-...

引用

ieee symposium on visual analytics science and technology (vast)

作者： Xumeng Wang Wei Chen Jiazhi Xia Zexian Chen Dongshi Xu Xiangyang Wu Mingliang Xu Tobias Schreck State Key Lab of CAD&CG Zhejiang University School of Computer Science and Engineering Central South University College of Information Engineering China Jiliang University Institute of Graphics and Image Hangzhou Dianzi University Zhengzhou University Graz University of Technology

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

Time-series data is widely studied in various scenarios, like weather forecast, stock market, customer behavior analysis. To comprehensively learn about the dynamic environments, it is necessary to comprehend features from multiple data sources. This paper proposes a novel visual analysis approach for detecting and analyzing concept drifts from multi-sourced time-series. We propose a visual detection scheme for discovering concept drifts from multiple sourced time-series based on prediction models. We design a drift level index to depict the dynamics, and a consistency judgment model to justify whether the concept drifts from various sources are consistent. Our integrated visual interface, ConceptExplorer, facilitates visual exploration, extraction, understanding, and comparison of concepts and concept drifts from multi-source time-series data. We conduct three case studies and expert interviews to verify the effectiveness of our approach.

关键词： Predictive models Data visualization Data models Adaptation models Indexes Analytical models visual analytics

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iConViz: Interactive visual Exploration of the Default Contagion Risk of Networked-Guarantee Loans

iConViz: Interactive Visual Exploration of the Default Conta...

引用

ieee symposium on visual analytics science and technology (vast)

作者： Zhibin Niu Runlin Li Junqi Wu Dawei Cheng Jiawan Zhang College of Intelligence and Computing Tianjin University Shanghai Jiao Tong University

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

Groups of enterprises can serve as guarantees for one another and form complex networks when obtaining loans from commercial banks. During economic slowdowns, corporate default may spread like a virus and lead to large-scale defaults or even systemic financial crises. To help financial regulatory authorities and banks manage the risk associated with networked loans, we identified the default contagion risk, a pivotal issue in developing preventive measures, and established iConViz, an interactive visual analysis tool that facilitates the closed-loop analysis process. A novel financial metric, the contagion effect, was formulated to quantify the infectious consequences of guarantee chains in this type of network. Based on this metric, we designed and implemented a series of novel and coordinated views that address the analysis of financial problems. Experts evaluated the system using real-world financial data. The proposed approach grants practitioners the ability to avoid previous ad hoc analysis methodologies and extend coverage of the conventional Capital Accord to the banking industry.

关键词： Data visualization visual analytics Risk management Banking Monitoring Tools Task analysis

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STULL: Unbiased Online Sampling for visual Exploration of Large Spatiotemporal Data

STULL: Unbiased Online Sampling for Visual Exploration of La...

引用

ieee symposium on visual analytics science and technology (vast)

作者： Guizhen Wang Jingjing Guo Mingjie Tang José Florencio de Queiroz Neto Calvin Yau Anas Daghistani Morteza Karimzadeh Walid G. Aref David S. Ebert Purdue University Chinese Academy of Science Federal University of Ceara Umm Al-Qura University University of Colorado Boulder University of Oklahoma

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

Online sampling-supported visual analytics is increasingly important, as it allows users to explore large datasets with acceptable approximate answers at interactive rates. However, existing online spatiotemporal sampling techniques are often biased, as most researchers have primarily focused on reducing computational latency. Biased sampling approaches select data with unequal probabilities and produce results that do not match the exact data distribution, leading end users to incorrect interpretations. In this paper, we propose a novel approach to perform unbiased online sampling of large spatiotemporal data. The proposed approach ensures the same probability of selection to every point that qualifies the specifications of a user's multidimensional query. To achieve unbiased sampling for accurate representative interactive visualizations, we design a novel data index and an associated sample retrieval plan. Our proposed sampling approach is suitable for a wide variety of visual analytics tasks, e.g., tasks that run aggregate queries of spatiotemporal data. Extensive experiments confirm the superiority of our approach over a state-of-the-art spatial online sampling technique, demonstrating that within the same computational time, data samples generated in our approach are at least 50% more accurate in representing the actual spatial distribution of the data and enable approximate visualizations to present closer visual appearances to the exact ones.

关键词： Data visualization Spatiotemporal phenomena visual analytics Indexes Task analysis Spatial databases Time factors

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SilkViser: A visual Explorer of Blockchain-based Cryptocurrency Transaction Data

SilkViser: A Visual Explorer of Blockchain-based Cryptocurre...

引用

ieee symposium on visual analytics science and technology (vast)

作者： Zengsheng Zhong Shuirun Wei Yeting Xu Ying Zhao Fangfang Zhou Feng Luo Ronghua Shi School of Computer Sciences and Engineering Central South University China Chongqing Engineering Technology Research Center for Information Management in Development Chongqing Technology and Business University China

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

Many blockchain-based cryptocurrencies provide users with online blockchain explorers for viewing online transaction data. However, traditional blockchain explorers mostly present transaction information in textual and tabular forms. Such forms make understanding cryptocurrency transaction mechanisms difficult for novice users (NUsers). They are also insufficiently informative for experienced users (EUsers) to recognize advanced transaction information. This study introduces a new online cryptocurrency transaction data viewing tool called SilkViser. Guided by detailed scenario and requirement analyses, we create a series of appreciating visualization designs, such as paper ledger-inspired block and blockchain visualizations and ancient copper coin-inspired transaction visualizations, to help users understand cryptocurrency transaction mechanisms and recognize advanced transaction information. We also provide a set of lightweight interactions to facilitate easy and free data exploration. Moreover, a controlled user study is conducted to quantitatively evaluate the usability and effectiveness of SilkViser. Results indicate that SilkViser can satisfy the requirements of NUsers and EUsers. Our visualization designs can compensate for the inexperience of NUsers in data viewing and attract potential users to participate in cryptocurrency transactions.

关键词： Blockchain Bitcoin Data visualization Tools Data analysis visual analytics Data mining

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A visual analytics Framework for Contrastive Network Analysis

A Visual Analytics Framework for Contrastive Network Analysi...

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ieee symposium on visual analytics science and technology (vast)

作者： Takanori Fujiwara Jian Zhao Francine Chen Kwan-Liu Ma University of California Davis University of Waterloo FX Palo Alto Laboratory

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

A common network analysis task is comparison of two networks to identify unique characteristics in one network with respect to the other. For example, when comparing protein interaction networks derived from normal and cancer tissues, one essential task is to discover protein-protein interactions unique to cancer tissues. However, this task is challenging when the networks contain complex structural (and semantic) relations. To address this problem, we design ContraNA, a visual analytics framework leveraging both the power of machine learning for uncovering unique characteristics in networks and also the effectiveness of visualization for understanding such uniqueness. The basis of ContraNA is cNRL, which integrates two machine learning schemes, network representation learning (NRL) and contrastive learning (CL), to generate a low-dimensional embedding that reveals the uniqueness of one network when compared to another. ContraNA provides an interactive visualization interface to help analyze the uniqueness by relating embedding results and network structures as well as explaining the learned features by cNRL. We demonstrate the usefulness of ContraNA with two case studies using real-world datasets. We also evaluate ContraNA through a controlled user study with 12 participants on network comparison tasks. The results show that participants were able to both effectively identify unique characteristics from complex networks and interpret the results obtained from cNRL.

关键词： visual analytics Task analysis Semantics Computer architecture Two dimensional displays Proteins Principal component analysis

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visual analytics of Multivariate Event Sequence Data in Racquet Sports

Visual Analytics of Multivariate Event Sequence Data in Racq...

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ieee symposium on visual analytics science and technology (vast)

作者： Jiang Wu Ziyang Guo Zuobin Wang Qingyang Xu Yingcai Wu The State Key Lab of CAD&CG Zhejiang University

ISBN: (数字)9781728180090

ISBN: (纸本)9781728180106

In this work, we propose a generic visual analytics framework to support tactic analysis based on data collected from racquet sports (such as tennis and badminton). The proposed approach models each rally in a game as a sequence of hits (i.e., events) until one athlete scores a point. Each hit can be described with a set of attributes, such as the positions of the ball and the techniques used to hit the ball (such as drive and volley in tennis). Thus, the mentioned sequence of hits can be viewed as a multivariate event sequence. By detecting and analyzing the multivariate subsequences that frequently occur in the rallies (namely, tactical patterns), athletes can gain insights into the playing styles adopted by their opponents, and therefore help them identify systematic weaknesses of the opponents and develop counter strategies in matches. To support such analysis effectively, we propose a steerable multivariate sequential pattern mining algorithm with adjustable weights over event attributes, such that the domain expert can obtain frequent tactical patterns according to the attributes specified by himself. We also propose a re-configurable glyph design to help users simultaneously analyze multiple attributes of the hits. The framework further supports comparative analysis of the tactical patterns, e.g., for different athletes or the same athlete playing under different conditions. By applying the framework on two datasets collected in tennis and badminton matches, we demonstrate that the system is generic and effective for tactic analysis in sports and can help identify signature techniques used by individual athletes. Finally, we discuss the strengths and limitations of the proposed approach based on the feedback from the domain experts.

关键词： Sports Data visualization visual analytics Data mining Task analysis Games Data models

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