2017 IEEE Conference on Visual Analytics Science and Technology (VAST)最新文献

Understanding Hidden Memories of Recurrent Neural Networks 理解递归神经网络的隐藏记忆

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-30 DOI: 10.1109/VAST.2017.8585721

Yao Ming, Shaozu Cao, Ruixiang Zhang, Z. Li, Yuanzhe Chen, Yangqiu Song, Huamin Qu

引用次数: 173

VAST Mini-Challenge 1 浩瀚迷你挑战1

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585677

Ayushi Gupta, Veera Raghavendra Chikka, K. Karlapalem

引用次数: 0

Visualizing chemicals detection VAST 2017 Mini Challenge 2 可视化化学品检测VAST 2017迷你挑战2

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585723

Jiaqi Zhang, Xintian Liu, Hongjun Qian, T. Kam

引用次数: 0

Temporal and Spatial Analysis of VAST 2017’s Mini-Challenge 1 VAST 2017迷你挑战赛的时空分析

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585475

Chris Muller, Kevin McGurgan, Stephanie Kane

引用次数: 0

The “y” of it Matters, Even for Storyline Visualization 它的“y”很重要，即使对故事情节可视化也是如此

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585487

Dustin L. Arendt, M. Pirrung

{"title":"The “y” of it Matters, Even for Storyline Visualization","authors":"Dustin L. Arendt, M. Pirrung","doi":"10.1109/VAST.2017.8585487","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585487","url":null,"abstract":"Storylines are adept at communicating complex change by encoding time on the x-axis and using the proximity of lines in the y direction to represent interaction between entities. The original definition of a storyline visualization requires data defined in terms of explicit interaction groups. Relaxing this definition allows storyline visualization to be applied more generally, but this creates questions about how the y-coordinate should encode interactions when this is tied to a particular place or state. To answer this question, we conducted a design study where we considered two layout algorithm design alternatives within a geo-temporal analysis tool written to solve part of the VAST Challenge 2014. We measured the performance of users at overview and detail oriented tasks between two storyline layout algorithms. To the best of our knowledge, this paper is the first work to question the design principles for storyline visualization, and what we found surprised us. For overview tasks with the alternative layout, which has a consistent encoding for the y-coordinate, users performed moderately better (${p}lt.05$) than the storyline layout based on existing design constraints and aesthetic criteria. Our empirical findings were also supported by first-hand accounts taken from interviews with multiple expert analysts, who suggested that the inconsistent meaning of the y-axis was misleading. These findings led us to design a new storyline layout algorithm that is a “best of both” where the y-axis has a consistent meaning but aesthetic criteria (e.g., line crossings) are considered.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132840328","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11

ODIX: A Rapid Hypotheses Testing System for Origin-Destination Data IEEE VAST Challenge Award for Excellence in Spatio-temporal Graph Analytics ODIX:出发地-目的地数据的快速假设测试系统IEEE VAST挑战时空图分析卓越奖

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585686

J. Buchmüller, Wolfgang Jentner, Dirk Streeb, D. Keim

引用次数: 2

CrystalBall: A Visual Analytic System for Future Event Discovery and Analysis from Social Media Data 水晶球:一个从社会媒体数据中发现和分析未来事件的可视化分析系统

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585658

Isaac Cho, Ryan Wesslen, Svitlana Volkova, W. Ribarsky, Wenwen Dou

{"title":"CrystalBall: A Visual Analytic System for Future Event Discovery and Analysis from Social Media Data","authors":"Isaac Cho, Ryan Wesslen, Svitlana Volkova, W. Ribarsky, Wenwen Dou","doi":"10.1109/VAST.2017.8585658","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585658","url":null,"abstract":"Social media data bear valuable insights regarding events that occur around the world. Events are inherently temporal and spatial. Existing visual text analysis systems have focused on detecting and analyzing past and ongoing events. Few have leveraged social media information to look for events that may occur in the future. In this paper, we present an interactive visual analytic system, CrystalBall, that automatically identifies and ranks future events from Twitter streams. CrystalBall integrates new methods to discover events with interactive visualizations that permit sensemaking of the identified future events. Our computational methods integrate seven different measures to identify and characterize future events, leveraging information regarding time, location, social networks, and the informativeness of the messages. A visual interface is tightly coupled with the computational methods to present a concise summary of the possible future events. A novel connection graph and glyphs are designed to visualize the characteristics of the future events. To demonstrate the efficacy of CrystalBall in identifying future events and supporting interactive analysis, we present multiple case studies and validation studies on analyzing events derived from Twitter data.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115360725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Multi-Spectral Satellite Image Analysis for Feature Identification and Change Detection VAST Challenge 2017: Honorable Mention for Good Facilitation of Single Image Analysis 面向特征识别和变化检测的多光谱卫星图像分析VAST挑战赛2017:单幅图像分析便捷性优秀奖

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585482

S. Malla, Anwesh Tuladhar, Ghulam Jilani Quadri, P. Rosen

引用次数: 1

Spatiotemporal identification of anomalies in a wildlife preserve VAST Grand Challenge 2017 Award: Clear Presentation of Hypotheses and Supporting Evidence 野生动物保护区异常的时空识别VAST 2017年大挑战奖:明确提出假设和支持证据

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585493

Bharadwaj S. Kishan, Ong Guan Jie Jason, Yanrong Zhang, Kam Tin Seong

引用次数: 0

The Role of Explicit Knowledge: A Conceptual Model of Knowledge-Assisted Visual Analytics 显性知识的作用:知识辅助视觉分析的概念模型

2017 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2017-10-01 DOI: 10.1109/VAST.2017.8585498

P. Federico, Markus Wagner, A. Rind, Albert Amor-Amoros, S. Miksch, W. Aigner

{"title":"The Role of Explicit Knowledge: A Conceptual Model of Knowledge-Assisted Visual Analytics","authors":"P. Federico, Markus Wagner, A. Rind, Albert Amor-Amoros, S. Miksch, W. Aigner","doi":"10.1109/VAST.2017.8585498","DOIUrl":"https://doi.org/10.1109/VAST.2017.8585498","url":null,"abstract":"Visual Analytics (VA) aims to combine the strengths of humans and computers for effective data analysis. In this endeavor, humans’ tacit knowledge from prior experience is an important asset that can be leveraged by both human and computer to improve the analytic process. While VA environments are starting to include features to formalize, store, and utilize such knowledge, the mechanisms and degree in which these environments integrate explicit knowledge varies widely. Additionally, this important class of VA environments has never been elaborated on by existing work on VA theory. This paper proposes a conceptual model of Knowledge-assisted VA conceptually grounded on the visualization model by van Wijk. We apply the model to describe various examples of knowledge-assisted VA from the literature and elaborate on three of them in finer detail. Moreover, we illustrate the utilization of the model to compare different design alternatives and to evaluate existing approaches with respect to their use of knowledge. Finally, the model can inspire designers to generate novel VA environments using explicit knowledge effectively.","PeriodicalId":149607,"journal":{"name":"2017 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127213855","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 51