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ieee conference on visual analytics science and technology (vast)

作者： Zhou, Zheng Wang, Sijin Wu, Wenjie Huang, Aijun Niu, Yafeng Tang, Hui Chen, Yingjie Victor Qian, Zhenyu Cheryl Purdue Univ Dept Comp Graph Technol W Lafayette IN 47907 USA Purdue Univ Dept Ind & Interact Design W Lafayette IN 47907 USA Southeast Univ Sch Mech Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538631638

The visual analytics system ClockPetals aims to reveal the spatio-temporal and sequential patterns of a large traffic record dataset. The system features appealing interactive web graphics that fast illustrate traffic patterns and allow users to locate unusual, anomalous traffic events from multiple demographical and temporal dimensions. ClockPetals also provides the interactive exploration of different vehicle batches via common sequential characteristic clustering. This paper presents the system's architecture and the benefits of its adoption.

关键词： [Human-centered computing]-visual analytics Information visualization User interface design [Information systems]-Spatial-temporal systems

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visual Analytic Design for Detecting Airborne Pollution Sources vast Challenge 2017 Award: Comprehensive Mini-Challenge 2 Answer

Visual Analytic Design for Detecting Airborne Pollution Sour...

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2017 ieee conference on visual analytics science and technology, vast 2017

作者： Wood, Jo GiCentre University of London United Kingdom

ISBN: (纸本)9781538631638

Using the vast Challenge 2017 dataset as illustration, the design choices of a visual analytic system for predicting the source of air pollution is described. Probabilistic Source Cones are visual symbols representing the probability of source direction of a pollution event. Using transparency to indicate probability, multiple cones may be overlaid in order to provide a fuzzy triangulation of likely sources. This enabled the correct prediction and elimination of pollution sources at a precision far in excess of the spatial density of the sensors themselves. © 2017 ieee.

关键词： visualization

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CRICTO: Supporting Sensemaking through Crowdsourced Information Schematization

CRICTO: Supporting Sensemaking through Crowdsourced Informat...

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ieee conference on visual analytics science and technology (vast)

作者： Chung, Haeyong Dasari, Sai Prashanth Nandhakumar, Santhosh Andrews, Christopher Univ Alabama Huntsville AL 35899 USA Middlebury Coll Middlebury VT 05753 USA

ISBN: (纸本)9781538631638

We present CRICTO, a new crowdsourcing visual analytics environment for making sense of and analyzing text data, whereby multiple crowdworkers are able to parallelize the simple information schematization tasks of relating and connecting entities across documents. The diverse links from these schematization tasks are then automatically combined and the system visualizes them based on the semantic types of the linkages. CRICTO also includes several tools that allow analysts to interactively explore and refine crowdworkers' results to better support their own sensemaking processes. We evaluated CRICTO's techniques and analysis workflow with deployments of CRICTO using Amazon Mechanical Turk and a user study that assess the effect of crowdsourced schematization in sensemaking tasks. The results of our evaluation show that CRICTO's crowdsourcing approaches and workflow help analysts explore diverse aspects of datasets, and uncover more accurate hidden stories embedded in the text datasets.

关键词： visual text analytics sensemaking crowdsourcing

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Warning, Bias May Occur: A Proposed Approach to Detecting Cognitive Bias in Interactive visual analytics

Warning, Bias May Occur: A Proposed Approach to Detecting Co...

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ieee conference on visual analytics science and technology (vast)

作者： Wall, Emily Blaha, Leslie M. Franklin, Lyndsey Endert, Alex Georgia Tech Atlanta GA 30332 USA Pacific Northwest Natl Lab Richland WA 99352 USA

ISBN: (纸本)9781538631638

visual analytic tools combine the complementary strengths of humans and machines in human-in-the-loop systems. Humans provide invaluable domain expertise and sensemaking capabilities to this discourse with analytic models;however, little consideration has yet been given to the ways inherent human biases might shape the visual analytic process. In this paper, we establish a conceptual framework for considering bias assessment through human-in-the-loop systems and lay the theoretical foundations for bias measurement. We propose six preliminary metrics to systematically detect and quantify bias from user interactions and demonstrate how the metrics might be implemented in an existing visual analytic system, InterAxis. We discuss how our proposed metrics could be used by visual analytic systems to mitigate the negative effects of cognitive biases by making users aware of biased processes throughout their analyses.

关键词： cognitive bias visual analytics human-in-the-loop mixed initiative user interaction

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The Anchoring Effect in Decision-Making with visual analytics

The Anchoring Effect in Decision-Making with Visual Analytic...

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ieee conference on visual analytics science and technology (vast)

作者： Cho, Isaac Wesslen, Ryan Karduni, Alireza Santhanam, Sashank Shaikh, Samira Dou, Wenwen Univ North Carolina Charlotte Charlotte NC 28223 USA

ISBN: (纸本)9781538631638

Anchoring effect is the tendency to focus too heavily on one piece of information when making decisions. In this paper, we present a novel, systematic study and resulting analyses that investigate the effects of anchoring effect on human decision-making using visual analytic systems. visual analytics interfaces typically contain multiple views that present various aspects of information such as spatial, temporal, and categorical. These views are designed to present complex, heterogeneous data in accessible forms that aid decision-making. However, human decision-making is often hindered by the use of heuristics, or cognitive biases, such as anchoring effect. Anchoring effect can be triggered by the order in which information is presented or the magnitude of information presented. Through carefully designed laboratory experiments, we present evidence of anchoring effect in analysis with visual analytics interfaces when users are primed by representation of different pieces of information. We also describe detailed analyses of users' interaction logs which reveal the impact of anchoring bias on the visual representation preferred and paths of analysis. We discuss implications for future research to possibly detect and alleviate anchoring bias.

关键词： visual analytics Anchoring Effect Sense Making Cognitive Bias Interaction Log Analysis

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Understanding Hidden Memories of Recurrent Neural Networks

Understanding Hidden Memories of Recurrent Neural Networks

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ieee conference on visual analytics science and technology (vast)

作者： Ming, Yao Cao, Shaozu Zhang, Ruixiang Li, Zhen Chen, Yuanzhe Song, Yangqiu Qu, Huamin Hong Kong Univ Sci & Technol Hong Kong Peoples R China

ISBN: (纸本)9781538631638

Recurrent neural networks (RNNs) have been successfully applied to various natural language processing (NLP) tasks and achieved better results than conventional methods. However, the lack of understanding of the mechanisms behind their effectiveness limits further improvements on their architectures. In this paper, we present a visual analytics method for understanding and comparing RNN models for NLP tasks. We propose a technique to explain the function of individual hidden state units based on their expected response to input texts. We then co-cluster hidden state units and words based on the expected response and visualize co-clustering results as memory chips and word clouds to provide more structured knowledge on RNNs' hidden states. We also propose a glyph-based sequence visualization based on aggregate information to analyze the behavior of an RNN's hidden state at the sentence-level. The usability and effectiveness of our method are demonstrated through case studies and reviews from domain experts.

关键词： recurrent neural networks visual analytics understanding neural model co-clustering

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ODIX: A Rapid Hypotheses Testing System for Origin-Destination Data ieee vast Challenge Award for Excellence in Spatio-temporal Graph analytics

ODIX: A Rapid Hypotheses Testing System for Origin-Destinati...

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2017 ieee conference on visual analytics science and technology, vast 2017

作者： Buchmuller, Juri Jentner, Wolfgang Streeb, Dirk Keim, Daniel A. Universität Konstanz Germany

ISBN: (纸本)9781538631638

In this paper, we present our solution to the vast Challenge 2017 Mini Challenge 1. We discuss challenges posed by data set and tasks and introduce ODIX, a custom rapid hypotheses testing system tailored to origin-destination data as provided by the challenge. We show findings made with ODIX and illustrate how we apply sequential pattern mining to explore common traffic patterns. © 2017 ieee.

关键词： Information systems

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visual analytics science and technology INTRODUCTION

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INFORMATION visualIZATION 2008年第1期7卷 1-2页

作者： Ribarsky, Bill Dill, John

It is an honor and a pleasure to present in this theme issue extended versions of the best papers from the recent visual analytics science and technology Symposium (vast 07), held in conjunction with ieee visualization 2007 (October 28-November 1, 2007 in Sacramento, CA). Now in its second year, vast is the premier international conference in visual analytics. vast 07 had 24 papers accepted, presented, and published in the proceedings. The number and quality of papers is noteworthy since there has been increased competition from journal special issues devoted to visual analytics and several conferences where visual analytics papers are encouraged. The overall picture is of a strong and growing field. The seven papers selected span important visual analytics topics such as investigative analysis, emergency response, collaboration, text and media analyses, social analytics, and interaction. These are not only rich research topics, they also involve techniques of importance to those who need to apply visual analytics tools and methods. Several of these papers and also other papers from vast 07 reflect growing collaborations between visualization scientists and those who do analytics, either in mathematical and statistical methods, social analyses, financial analytics, or other areas. We expect that these collaborations will grow and in the future there will be leadership from the analytics side. Based on ongoing research in the visual analytics community and interests expressed in discussions at workshops and other venues, in the future we also expect to see more papers in the important areas of analytical reasoning, cognitive models, the fundamental properties of interaction, and presentation visual analytics for decision makers and others.

关键词： visual analytics Pride

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Big Data Exploration through visual analytics

Big Data Exploration through Visual Analytics

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ieee conference on visual analytics science and technology (vast)

作者： Abousalh-Neto, Nascif A. Kazgan, Sumeyye SAS Institute United States

ISBN: (纸本)9781467347532

SAS (R) visual analytics Explorer is an advanced data visualization and exploratory data analysis application that is a component of the SAS visual analytics solution. It excels at handling big data problems like the vast challenge. With a wide range of visual analytics features and the ability to scale to massive datasets, SAS visual analytics Explorer enables analysts to find patterns and relationships quickly and easily, no matter the size of their data. In this summary paper, we explain how we used SAS visual analytics Explorer to solve the vast Challenge 2012 mini-challenge 1.

关键词： visual analytics big data data visualization exploratory data analysis

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vast Challenge 2017: Mystery at the Wildlife Preserve

VAST Challenge 2017: Mystery at the Wildlife Preserve

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ieee conference on visual analytics science and technology (vast)

作者： Whiting, Mark A. Cook, Kris Crouser, R. Jordan Fallon, John Grinstein, Georges Haack, Jereme Henderson, Cindy Liggett, Kristen Staheli, Diane Strasburg, Jana Tagestad, Jerry Varley, Carrie Pacific Northwest Natl Lab Richland WA 99352 USA Smith Coll Northampton MA 01063 USA Univ Mass Amherst Amherst MA USA Air Force Res Lab Wright Patterson AFB OH USA MIT Lincoln Lab Cambridge MA 02139 USA

ISBN: (纸本)9781538631638

The vast Challenge 2017 offered three mini-challenges and a grand challenge dealing with environmental problems potentially caused by human patterns of life and potentially harmful chemically laden effluent plumes being emitted from factory smokestacks. The data provided included traffic patterns, sensor data though a Preserve, information about the Preserve, multispectral imagery and a map to help an ornithology graduate student particularly concerned with the population decrease of the Rose-Crested Blue Pipit determine who and what might be responsible. Mini-Challenge 1 focused on analysis of vehicles passing through the Preserve over time. Mini-Challenge 2 looked at data collected by air sampling monitors surrounding nearby factories, along with meteorological readings, to understand potential impacts they may be having on the Pipit. Mini-Challenge 3 required investigation into several months of multi-spectral imagery over the area to understand the Preserve's general health. The Grand Challenge asked participants to synthesize across all three mini-challenges to create hypotheses of what is happening and what sensible next steps could be. This year's challenge received 58 submissions and recorded over 1100 unique downloads from 20 countries prior to the submission deadline.

关键词： visual analytics human information interaction patterns of life short events analytics multi-spectral imagery evaluation contest

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