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visual data analysis with Task-Based Recommendations

data SCIENCE AND ENGINEERING 2022年第4期7卷 354-369页

作者： Shen, Leixian Shen, Enya Tai, Zhiwei Xu, Yihao Dong, Jiaxiang Wang, Jianmin Tsinghua Univ Beijing Peoples R China

General visualization recommendation systems typically make design decisions for the dataset automatically. However, most of them can only prune meaningless visualizations but fail to recommend targeted results. This paper contributes TaskVis, a task-oriented visualization recommendation system that allows users to select their tasks precisely on the interface. We first summarize a task base with 18 classical analytic tasks by a survey both in academia and industry. On this basis, we maintain a rule base, which extends empirical wisdom with our targeted modeling of the analytic tasks. Then, our rule-based approach enumerates all the candidate visualizations through answer set programming. After that, the generated charts can be ranked by four ranking schemes. Furthermore, we introduce a task-based combination recommendation strategy, leveraging a set of visualizations to give a brief view of the dataset collaboratively. Finally, we evaluate TaskVis through a series of use cases and a user study.

关键词： visual data analysis visualization recommendation Analytic task Answer set programming

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Spatio-temporal changes in racial segregation and diversity in large US cities from 1990 to 2020: a visual data analysis

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EPJ data SCIENCE 2023年第1期12卷 30页

作者： Dmowska, Anna Stepinski, Tomasz F. Adam Mickiewicz Univ Inst Geoecol & Geoinformat Bogumila Krygowskiego 10 PL-61680 Poznan Poland Univ Cincinnati Dept Geog & GIS Space Informat Lab 215 Braunstein Hall Cincinnati OH 45221 USA

Urban populations in large US cities exhibit racial and ethnic diversity, yet they remain residentially segregated. The examination of temporal trends in segregation and diversity is crucial for sociologists and urban planners. In this study, we investigate the spatio-temporal changes in segregation and diversity across 61 major US cities, utilizing data from four US Censuses conducted between 1990 and 2020. Unlike previous studies, our approach relies on visual data analysis, enabling us to capture the overarching changes in racial coresidence during this period. We employ four distinct perspectives - geographical, temporal, groups evolution, and desegregation scale limit - to visualize and analyze the data. Geographical analysis uncovers a decrease in regional disparities in urban diversity and segregation since 1990, as urban racial integration extends beyond West Coast and Southwestern cities to encompass the entire US. Through temporal analysis, we observe a general trend of rapidly increasing diversity and gradual reduction in segregation, albeit with varying rates across different cities. Groups evolution analysis reveals that cities grouped based on their diversity and segregation metrics in 1990 follow the overall trend toward larger diversity and smaller segregation while preserving group's coherence but not their distinctiveness. Finally, the desegregation scale limit perspective suggests that, on average, over the 1990 to 2020 period, the desegregation scale has started to subceed the lower limit of the census block. By employing these diverse analytical perspectives, our study provides a comprehensive understanding of the changes in racial segregation and diversity within US cities over the past three decades.

关键词： Racial segregation US Census data Racial dynamics visual data analysis

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XNLI: Explaining and Diagnosing NLI-Based visual data analysis

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2024年第7期30卷 3813-3827页

作者： Feng, Yingchaojie Wang, Xingbo Pan, Bo Wong, Kam Kwai Ren, Yi Liu, Shi Yan, Zihan Ma, Yuxin Qu, Huamin Chen, Wei Zhejiang Univ State Key Lab CAD & CG Hangzhou 310027 Zhejiang Peoples R China Hong Kong Univ Sci & Technol Hong Kong 999077 Peoples R China MIT Media Lab Cambridge MA 02139 USA Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518055 Guangdong Peoples R China Zhejiang Univ Lab Art & Archaeol Image Minist Educ Hangzhou 310027 Zhejiang Peoples R China

Natural language interfaces (NLIs) enable users to flexibly specify analytical intentions in data visualization. However, diagnosing the visualization results without understanding the underlying generation process is challenging. Our research explores how to provide explanations for NLIs to help users locate the problems and further revise the queries. We present XNLI, an explainable NLI system for visual data analysis. The system introduces a Provenance Generator to reveal the detailed process of visual transformations, a suite of interactive widgets to support error adjustments, and a Hint Generator to provide query revision hints based on the analysis of user queries and interactions. Two usage scenarios of XNLI and a user study verify the effectiveness and usability of the system. Results suggest that XNLI can significantly enhance task accuracy without interrupting the NLI-based analysis process.

关键词： data visualization visualization Task analysis Encoding data analysis Motion pictures Prototypes Natural language interface visual data analysis explainability

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Biomedical visual data analysis to build an intelligent diagnostic decision support system in medical genetics

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ARTIFICIAL INTELLIGENCE IN MEDICINE 2014年第2期62卷 105-118页

作者： Kuru, Kaya Niranjan, Mahesan Tunca, Yusuf Osvank, Erhan Azim, Tayyaba Gulhane Mil Med Acad Dept Commun Elect & Informat Syst TR-06010 Ankara Turkey Univ Southampton Sch Elect & Comp Sci Southampton SO17 1BF Hants England Gulhane Mil Med Acad Dept Med Genet TR-06010 Ankara Turkey Middle E Tech Univ Inst Informat TR-06531 Ankara Turkey

Background: In general, medical geneticists aim to pre-diagnose underlying syndromes based on facial features before performing cytological or molecular analyses where a genotype-phenotype interrelation is possible. However, determining correct genotype-phenotype interrelationships among many syndromes is tedious and labor-intensive, especially for extremely rare syndromes. Thus, a computer-aided system for pre-diagnosis can facilitate effective and efficient decision support, particularly when few similar cases are available, or in remote rural districts where diagnostic knowledge of syndromes is not readily available. Methods: The proposed methodology, visual diagnostic decision support system (visual diagnostic DSS), employs machine learning (ML) algorithms and digital image processing techniques in a hybrid approach for automated diagnosis in medical genetics. This approach uses facial features in reference images of disorders to identify visual genotype-phenotype interrelationships. Our statistical method describes facial image data as principal component features and diagnoses syndromes using these features. Results: The proposed system was trained using a real dataset of previously published face images of subjects with syndrcimes, which provided accurate diagnostic information. The method was tested using a leave-one-out cross-validation scheme with 15 different syndromes, each of comprised 5-9 cases, i.e., 92 cases in total. An accuracy rate of 83% was achieved using this automated diagnosis technique, which was statistically significant (p < 0.01). Furthermore, the sensitivity and specificity values were 0.857 and 0.870, respectively. Conclusion: Our results show that the accurate classification of syndromes is feasible using ML techniques. Thus, a large number of syndromes with characteristic facial anomaly patterns could be diagnosed with similar diagnostic DSSs to that described in the present study, i.e., visual diagnostic DSS, thereby demonstrating th

关键词： Decision support system Machine learning visual data analysis Principal components analysis Medical genetics Dysmorphology Facial genotype-phenotype

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Lumos: Increasing Awareness of Analytic Behavior during visual data analysis

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2022年第1期28卷 1009-1018页

作者： Narechania, Arpit Coscia, Adam Wall, Emily Endert, Alex Georgia Inst Technol Atlanta GA 30332 USA Emory Univ Atlanta GA 30322 USA Northwestern Univ Evanston IL 60208 USA

visual data analysis tools provide people with the agency and flexibility to explore data using a variety of interactive functionalities. However, this flexibility may introduce potential consequences in situations where users unknowingly overemphasize or underemphasize specific subsets of the data or attribute space they are analyzing. For example, users may overemphasize specific attributes and/or their values (e.g., Gender is always encoded on the X axis), underemphasize others (e.g., Religion is never encoded), ignore a subset of the data (e.g., older people are filtered out), etc. In response, we present Lumos, a visual data analysis tool that captures and shows the interaction history with data to increase awareness of such analytic behaviors. Using in-situ (at the place of interaction) and ex-situ (in an external view) visualization techniques, Lumos provides real-time feedback to users for them to reflect on their activities. For example, Lumos highlights datapoints that have been previously examined in the same visualization (in-situ) and also overlays them on the underlying data distribution (i.e., baseline distribution) in a separate visualization (ex-situ). Through a user study with 24 participants, we investigate how Lumos helps users' data exploration and decision-making processes. We found that Lumos increases users' awareness of visual data analysis practices in real-time, promoting reflection upon and acknowledgement of their intentions and potentially influencing subsequent interactions.

关键词： data visualization visualization data analysis Measurement History Hidden Markov models data models visual data analysis interaction traces analytic provenance awareness human bias

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SafetyLens: visual data analysis of Functional Safety of Vehicles

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2021年第2期27卷 1688-1697页

作者： Narechania, Arpit Qamar, Ahsan Endert, Alex Georgia Inst Technol Atlanta GA 30332 USA Ford Motor Co Detroit MI 48126 USA

Modern automobiles have evolved from just being mechanical machines to having full-fledged electronics systems that enhance vehicle dynamics and driver experience. However, these complex hardware and software systems, if not properly designed, can experience failures that can compromise the safety of the vehicle, its occupants, and the surrounding environment. For example, a system to activate the brakes to avoid a collision saves lives when it functions properly, but could lead to tragic outcomes if the brakes were applied in a way that's inconsistent with the design. Broadly speaking, the analysis performed to minimize such risks falls into a systems engineering domain called Functional Safety. In this paper, we present SafetyLens, a visual data analysis tool to assist engineers and analysts in analyzing automotive Functional Safety datasets. SafetyLens combines techniques including network exploration and visual comparison to help analysts perform domain-specific tasks. This paper presents the design study with domain experts that resulted in the design guidelines, the tool, and user feedback.

关键词： visual data analysis Design study Network visualization Functional safety Automotive engineering

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Optical illusions from visual data analysis: Example of the New Zealand asthma mortality epidemic

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JOURNAL OF CLINICAL EPIDEMIOLOGY 1997年第10期50卷 1079-1088页

作者： Suissa, S Ernst, P ROYAL VICTORIA HOSP DEPT MED MONTREAL PQ H3A 1A1 CANADA ROYAL VICTORIA HOSP DEPT EPIDEMIOL & BIOSTAT MCGILL PHARMACOEPIDEMIOL RES UNIT MONTREAL PQ H3A 1A1 CANADA MCGILL UNIV MONTREAL GEN HOSP MONTREAL PQ H3G 1A4 CANADA

The abundance of health related statistics routinely collected worldwide invites their misuse from haphazard associations between secular trends of these data. This misuse is often compounded by assessing these associations simply on the basis of a visual inspection of the data. The visual approach to data analysis, known to have several pitfalls, is particularly tempting in the context of asthma where it has often been used. For example, the epidemic of asthma deaths that occurred in New Zealand during the last two decades has been imputed to fenoterol, a medication for asthma, on the basis of a visual assessment of ecological data. The simultaneity of time trends in the asthma death rate and fenoterol market share in that country formed an important part of the statistical basis of the evidence. We verified whether the results of such visual analyses are corroborated by more objective quantitative statistical methods of analysis. We reanalyzed these same data, namely the time trend data of New Zealand asthma death rates, fenoterol market share, sales of beta-agonists and inhaled corticosteroids, measured yearly for the 16-year span 1976-1991, using Poisson weighted Ioglinear regression. We found that the protective effect of inhaled corticosteroids (rate ratio 0.5 per canister per month;95% confidence interval 0.4 to 0.7;p = 0.0001) was more closely associated with changes in asthma mortality than either fenoterol (RR 2.7 per canister per month;95% CI: 0.9 to 7.5;p = 0.06) or all beta-agonists combined (RR 1.6;95% CI: 0.8 to 3.0;p = .19). We conclude from this quantitative analysis that these ecological asthma mortality data provide evidence of a stronger association with inhaled corticosteroids, little used in New Zealand at the onset of the epidemic but used abundantly at its termination, than with fenoterol. This conclusion is diametrically opposite to that found by the visual approach. The quantitative analysis demonstrates that the visual approach to the anal

关键词： asthma mortality beta(2)-agonists ecological studies epidemiologic methods fenoterol graphical data inhaled corticosteroids statistics visual data analysis

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visual data analysis with computational intelligence methods

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BULLETIN OF THE POLISH ACADEMY OF SCIENCES-TECHNICAL SCIENCES 2010年第3期58卷 393-401页

作者： Kruse, R. Steinbrecher, M. Otto VonGuericke Univ Magdegurg Fac Comp Sci Dept Knowledge & Language Engn D-39016 Magdeburg Germany

visual data analysis is an appealing and increasing field of application We present two related visual analysis approaches that allow for the visualization of graphical model parameters and time dependent association rules When the graphical model is defined over purely nominal attributes its local structure can be interpreted as an association rule Such association rules comprise one of the most prominent and wide spread analysis techniques for pattern detection however there are only few visualization methods We introduce an alternative visual representation that also incorporates time since patterns are likely to change over time when the underlying data was collected from real world processes We apply the technique to both an artificial and a complex real life dataset and show that the combined automatic and visual approach gives more and faster insight Into the data than a fully automatic approach only Thus our proposed method is capable of reducing considerably the analysis time

关键词： visual data analysis computational intelligence methods

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Composites: A Tangible Interaction Paradigm for visual data analysis in Design Practice 2022

Composites: A Tangible Interaction Paradigm for Visual Data ...

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16th Working Conference on Advanced visual Interfaces (ACM AVI)

作者： Subramonyam, Hariharan Adar, Eytan Drucker, Steven M. Stanford Univ Stanford CA 94305 USA Univ Michigan Ann Arbor MI USA Microsoft Res Redmond WA USA

ISBN: (纸本)9781450397193

Conventional tools for visual analytics emphasize a linear production workflow and lack organic "work surfaces." A better surface would simultaneously support collaborative visualization construction, data and design exploration, and reasoning. To facilitate datadriven design within existing design tools such as card sorting, we introduce Composites, a tangible, augmented reality interface for constructing visualizations on large surfaces. In response to the placement of physical sticky-notes, Composites projects visualizations and data onto large surfaces. Our spatial grammar allows the designer to flexibly construct visualizations through the use of the notes. Similar to affinity-diagramming, the designer can "connect" the physical notes to data, operations, and visualizations which can then be re-arranged based on creative needs. We develop mechanisms (sticky interactions, visual hinting, etc.) to provide guiding feedback to the end-user. By leveraging low-cost technology, Composites extends a working surface to support a broad range of workflows without limiting creative design thinking.

关键词： visual data analysis data-Driven-Design Augmented Reality

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BiasBuzz: Combining visual Guidance with Haptic Feedback to Increase Awareness of Analytic Behavior during visual data analysis

BiasBuzz: Combining Visual Guidance with Haptic Feedback to ...

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CHI Conference on Human Factors in Computing Sytems (CHI)

作者： Paden, Jamal Narechania, Arpit Endert, Alex Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9798400703317

During visual data analysis, users may inadvertently focus more on certain aspects of data, affecting analysis outcome(s). Existing tools primarily rely on visual cues (e.g., highlight already visited data) to increase user awareness of such analytic behaviors. We believe this single, visual modality is a passive form of guidance that adds to users' cognitive load already engaged in analysis. We investigate how a dual modality (visual guidance and haptic feedback) can capture users' attention and more actively guide them in their pursuits. We interface an existing visual data analysis tool with a gaming mouse. This enhanced system tracks user interactions and communicates biases by vibrating the mouse (haptic) and simultaneously displaying contextual information in the tool (visual). A formative study with nine users revealed that this dual modality increased analytical awareness in some cases but some users found the haptic mouse vibrations to be distracting and disturbing, informing the design of future multimodal user interfaces.

关键词： haptic visualization guidance visual data analysis

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