263,903 research outputs found

    The self-organizing map as a visual neighbor retrieval method

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    We have recently introduced rigorous goodness criteria for information visualization by posing it as a visual neighbor retrieval problem, where the task is to find proximate high-dimensional data based only on a low-dimensional display. Standard information retrieval criteria such as precision and recall can then be used for information visualization. We introduced an algorithm, Neighbor Retrieval Visualizer (NeRV), to optimize the total cost of retrieval errors. NeRV was shown to outperform alternative methods, but the SOM was not included in the comparison. In empirical experiments of this paper the SOM turns out to be comparable to the best methods in terms of (smoothed) precision but not on recall. On a related measure called trustworthiness, the SOM outperforms all others. Finally, we suggest that for information visualization tasks the free parameters of the SOM could be optimized for information visualization with cross-validation

    An Information-Theoretic Framework for Evaluating Edge Bundling Visualization

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    Edge bundling is a promising graph visualization approach to simplifying the visual result of a graph drawing. Plenty of edge bundling methods have been developed to generate diverse graph layouts. However, it is difficult to defend an edge bundling method with its resulting layout against other edge bundling methods as a clear theoretic evaluation framework is absent in the literature. In this paper, we propose an information-theoretic framework to evaluate the visual results of edge bundling techniques. We first illustrate the advantage of edge bundling visualizations for large graphs, and pinpoint the ambiguity resulting from drawing results. Second, we define and quantify the amount of information delivered by edge bundling visualization from the underlying network using information theory. Third, we propose a new algorithm to evaluate the resulting layouts of edge bundling using the amount of the mutual information between a raw network dataset and its edge bundling visualization. Comparison examples based on the proposed framework between different edge bundling techniques are presented

    ์‹œ๊ฐํ™” ์ดˆ์‹ฌ์ž์—๊ฒŒ ์‹œ๊ฐ์  ๋น„๊ต๋ฅผ ๋•๋Š” ์ •๋ณด ์‹œ๊ฐํ™” ๊ธฐ์ˆ ์˜ ๋””์ž์ธ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2020. 2. ์„œ์ง„์šฑ.The visual comparison is one of the fundamental tasks in information visualization (InfoVis) that enables people to organize, evaluate, and combine information fragmented in visualizations. For example, people perform visual comparison tasks to compare data over time, from different sources, or with different analytic models. While the InfoVis community has focused on understanding the effectiveness of different visualization designs for supporting visual comparison tasks, it is still unclear how to design effective comparative visualizations due to several limitations: (1) Empirical findings and practical implications from those studies are fragmented, and (2) we lack user studies that directly investigated the effectiveness of different visualization designs for visual comparison. In this dissertation, we present the results of three studies to build our knowledge on how to support effective visual comparison to InfoVis novicesโ โ€”general people who are not familiar with visual representations and visual data exploration process. Identifying the major stages in the visualization construction process where novices confront challenges with visual comparison tasks, we explored two high-level comparison tasks with actual users: comparing visual mapping (encoding barrier) and comparing information (interpretation barrier) in visualizations. First, we conducted a systematical literature review on research papers (N = 104) that focused on supporting visual comparison tasks to gather and organize the practical insights that researchers gained in the wild. From this study, we offered implications for designing comparative visualizations, such as actionable guidelines, as well as the lucid categorization of comparative designs which can help researchers explore the design space. In the second study, we performed a qualitative user study (N = 24) to investigate how novices compare and understand visual mapping suggested in a visual-encoding recommendation interface. Based on the study, we present novices' main challenges in using visual encoding recommendations and design implications as remedies. In the third study, we conducted a design study in the area on bioinformatics to design and implement a visual analytics tool, XCluSim, that helps users to compare multiple clustering results. Case studies with a bioinformatician showed that our system enables analysts to easily evaluate the quality of a large number of clustering results. Based on the results of three studies in this dissertation, we suggest a future research agenda, such as designing recommendations for visual comparison and distinguishing InfoVis novices from experts.์‹œ๊ฐ์  ๋น„๊ต๋Š” ์ •๋ณด ์‹œ๊ฐํ™”๋ฅผ ์ด์šฉํ•œ ํ•ต์‹ฌ์ ์ธ ๋ฐ์ดํ„ฐ ๋ถ„์„ ๊ณผ์ • ์ค‘ ํ•˜๋‚˜๋กœ์จ, ๋ถ„์‚ฐ๋˜์–ด ์žˆ๋Š” ์ •๋ณด๋“ค์„ ์‚ฌ๋žŒ๋“ค์ด ์„œ๋กœ ์ •๋ฆฌ, ํ‰๊ฐ€, ๋ณ‘ํ•ฉํ•  ์ˆ˜ ์žˆ๋„๋ก ๋•๋Š”๋‹ค. ์˜ˆ๋ฅผ ๋“ค์–ด, ์‚ฌ๋žŒ๋“ค์€ ์‹œ๊ฐ„์˜ ํ๋ฆ„์— ๋”ฐ๋ฅธ ๋ฐ์ดํ„ฐ์˜ ๋ณ€ํ™”๋ฅผ ๋ณด๊ฑฐ๋‚˜, ์„œ๋กœ ๋‹ค๋ฅธ ์ถœ์ฒ˜์˜ ๋ฐ์ดํ„ฐ๋ฅผ ๋น„๊ตํ•˜๊ฑฐ๋‚˜, ๊ฐ™์€ ๋ฐ์ดํ„ฐ๋ฅผ ์—ฌ๋Ÿฌ ๋ถ„์„ ๋ชจ๋ธ๋“ค์„ ์ด์šฉํ•ด ํ‰๊ฐ€ํ•˜๊ธฐ ์œ„ํ•ด ์‹œ๊ฐ์  ๋น„๊ต ๊ณผ์—…์„ ํ”ํžˆ ์ˆ˜ํ–‰ํ•˜๊ฒŒ ๋œ๋‹ค. ํšจ๊ณผ์ ์ธ ์‹œ๊ฐํ™” ๋””์ž์ธ์„ ์œ„ํ•œ ์—ฌ๋Ÿฌ ์—ฐ๊ตฌ๊ฐ€ ์ •๋ณด ์‹œ๊ฐํ™” ๋ถ„์•ผ์—์„œ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋Š” ๋ฐ˜๋ฉด, ์–ด๋–ค ๋””์ž์ธ์„ ํ†ตํ•ด ํšจ๊ณผ์ ์œผ๋กœ ์‹œ๊ฐ์  ๋น„๊ต๋ฅผ ์ง€์›ํ•  ์ˆ˜ ์žˆ๋Š”์ง€์— ๋Œ€ํ•œ ์ดํ•ด๋Š” ๋‹ค์Œ์˜ ์ œ์•ฝ๋“ค๋กœ ์ธํ•ด ์•„์ง๊นŒ์ง€ ๋ถˆ๋ถ„๋ช…ํ•˜๋‹ค. (1) ๊ฒฝํ—˜์  ํ†ต์ฐฐ๋“ค๊ณผ ์‹ค์šฉ์  ์„ค๊ณ„ ์ง€์นจ๋“ค์ด ํŒŒํŽธํ™”๋˜์–ด ์žˆ์œผ๋ฉฐ (2) ๋น„๊ต ์‹œ๊ฐํ™”๋ฅผ ์ง€์›ํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•œ ์‚ฌ์šฉ์ž ์‹คํ—˜์˜ ์ˆ˜๊ฐ€ ์—ฌ์ „ํžˆ ์ œํ•œ์ ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์‹œ๊ฐํ™” ์ดˆ์‹ฌ์ž๋“ค์—๊ฒŒ ํšจ๊ณผ์ ์œผ๋กœ ์‹œ๊ฐ์  ๋น„๊ต๋ฅผ ์ง€์›ํ•˜๊ธฐ ์œ„ํ•œ ์ •๋ณด ์‹œ๊ฐํ™” ๋””์ž์ธ ๋ฐฉ๋ฒ•์„ ๋” ๊นŠ์ด ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•ด์„œ ์ผ๋ จ์˜ ์„ธ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜๊ณ  ์ด์— ๋Œ€ํ•œ ๊ฒฐ๊ณผ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ํŠน๋ณ„ํžˆ, ์‹œ๊ฐํ™” ์ดˆ์‹ฌ์ž๋“ค์ด ์‹œ๊ฐ์  ๋น„๊ต๋ฅผ ํ•  ๋•Œ ์–ด๋ ค์›€์„ ๊ฒฝํ—˜ํ•  ์ˆ˜ ์žˆ๋Š” ๋‘ ์ฃผ์š” ์‹œ๊ฐํ™” ๋‹จ๊ณ„๋ฅผ ํ™•์ธํ•จ์œผ๋กœ์จ, ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์‹œ๊ฐ์  ์ธ์ฝ”๋”ฉ ๋น„๊ต (์ธ์ฝ”๋”ฉ ์žฅ๋ฒฝ) ๋ฐ ์ •๋ณด ๋น„๊ต (ํ•ด์„ ์žฅ๋ฒฝ) ๊ณผ์—…๋“ค์— ์ดˆ์ ์„ ๋งž์ถ˜๋‹ค. ์ฒซ์งธ, ๋น„๊ต ์‹œ๊ฐํ™” ๋””์ž์ธ์„ ์ œ์‹œํ•œ ๋ฌธํ—Œ๋“ค(N = 104)์„ ์ฒด๊ณ„์ ์œผ๋กœ ์กฐ์‚ฌ ๋ฐ ๋ถ„์„ํ•จ์œผ๋กœ์จ ์‹œ๊ฐํ™” ์—ฐ๊ตฌ์ž๋“ค์ด ์‚ฌ์šฉ์ž ์‹คํ—˜๊ณผ ์‹œ๊ฐํ™” ์„ค๊ณ„ ๊ณผ์ •์„ ํ†ตํ•ด ์–ป์€ ์‹ค์šฉ์  ํ†ต์ฐฐ๋“ค์„ ์ •๋ฆฌํ•˜์˜€๋‹ค. ์ด ๋ฌธํ—Œ์กฐ์‚ฌ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ๋น„๊ต ์‹œ๊ฐํ™” ์„ค๊ณ„์— ๋Œ€ํ•œ ์ง€์นจ๋“ค์„ ์ •๋ฆฝํ•˜๊ณ , ๋น„๊ต ์‹œ๊ฐํ™”๋ฅผ ์œ„ํ•œ ๋””์ž์ธ ๊ณต๊ฐ„์„ ๋” ๊นŠ์ด ์ดํ•ดํ•˜๊ณ  ํƒ์ƒ‰ํ•˜๋Š” ๋ฐ ๋„์›€์„ ์ค„ ์ˆ˜ ์žˆ๋Š” ์‹œ๊ฐํ™” ๋ถ„๋ฅ˜ ๋ฐ ์˜ˆ์‹œ๋“ค์„ ์ œ๊ณตํ•œ๋‹ค. ๋‘˜์งธ, ์ดˆ์‹ฌ์ž๋“ค์ด ์‹œ๊ฐํ™” ์ถ”์ฒœ ์ธํ„ฐํŽ˜์ด์Šค์—์„œ ์–ด๋–ป๊ฒŒ ์ƒˆ๋กœ์šด ์‹œ๊ฐ์  ์ธ์ฝ”๋”ฉ๋“ค์„ ์„œ๋กœ ๋น„๊ตํ•˜๊ณ  ์‚ฌ์šฉํ•˜๋Š”์ง€์— ๋Œ€ํ•œ ์ดํ•ด๋ฅผ ๋•๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ์ž ์‹คํ—˜(N = 24)์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์ด ์‹คํ—˜์˜ ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ, ์ดˆ์‹ฌ์ž๋“ค์˜ ์ฃผ์š” ์–ด๋ ค์›€๋“ค๊ณผ ์ด๋“ค์„ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•œ ๋””์ž์ธ ์ง€์นจ๋“ค์„ ์ œ์‹œํ•œ๋‹ค. ์…‹์งธ, ์ƒ๋ช…์ •๋ณดํ•™์ž๊ฐ€ ์‹œ๊ฐ์ ์œผ๋กœ ๋‹ค์ˆ˜ ๊ฐœ์˜ ํด๋Ÿฌ์Šคํ„ฐ๋ง ๊ฒฐ๊ณผ๋“ค์„ ๋น„๊ต ๋ฐ ๋ถ„์„ํ•  ์ˆ˜ ์žˆ๋„๋ก ๋„์™€์ฃผ๋Š” ์‹œ๊ฐํ™” ์‹œ์Šคํ…œ, XCluSim์„ ๋””์ž์ธํ•˜๊ณ  ๊ตฌํ˜„ํ•˜๋Š” ๋””์ž์ธ ์Šคํ„ฐ๋””๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์‚ฌ๋ก€ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์‹ค์ œ๋กœ ์ƒ๋ช…์ •๋ณดํ•™์ž๊ฐ€ XCluSim์„ ์ด์šฉํ•˜์—ฌ ๋งŽ์€ ํด๋Ÿฌ์Šคํ„ฐ๋ง ๊ฒฐ๊ณผ๋“ค์„ ์‰ฝ๊ฒŒ ๋น„๊ต ๋ฐ ํ‰๊ฐ€ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ์ด ์„ธ ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋“ค์„ ๊ธฐ๋ฐ˜์œผ๋กœ ๋น„๊ต ์‹œ๊ฐํ™” ๋ถ„์•ผ์—์„œ ์œ ๋งํ•œ ํ–ฅํ›„ ์—ฐ๊ตฌ๋“ค์„ ์ œ์‹œํ•œ๋‹ค.CHAPTER 1. Introduction 1 1.1 Background and Motivation 1 1.2 Research Questions and Approaches 4 1.2.1 Revisiting Comparative Layouts: Design Space, Guidelines, and Future Directions 5 1.2.2 Understanding How InfoVis Novices Compare Visual Encoding Recommendation 6 1.2.3 Designing XCluSim: a Visual Analytics System for Comparing Multiple Clustering Results 7 1.3 Dissertation Outline 8 CHAPTER 2. Related Work 9 2.1 Visual Comparison Tasks 9 2.2 Visualization Designs for Comparison 10 2.2.1 Gleicher et al.s Comparative Layout 11 2.3 Understanding InfoVis Novices 12 2.4 Visualization Recommendation Interfaces 13 2.5 Comparative Visualizations for Cluster Analysis 14 CHAPTER 3. Comparative Layouts Revisited: Design Space, Guidelines, and Future Directions 19 3.1 Introduction 19 3.2 Literature Review 21 3.2.1 Method 22 3.3 Comparative Layouts in The Wild 23 3.3.1 Classifying Comparison Tasks in User Studies 25 3.3.2 Same LayoutIs Called Differently 26 3.3.3 Lucid Classification of Comparative Layouts 28 3.3.4 Advantages and Concerns of Using Each Layout 30 3.3.5 Trade-offs between Comparative Layouts 36 3.3.6 Approaches to Overcome the Concerns 38 3.3.7 Comparative Layout Explorer 42 3.4 Discussion 42 3.4.1 Guidelines for Comparative Layouts 44 3.4.2 Promising Directions for Future Research 48 3.5 Summary 49 CHAPTER 4. Understanding How InfoVis Novices Compare Visual Encoding Recommendation 51 4.1 Motivation 51 4.2 Interface 53 4.2.1 Visualization Goals 53 4.2.2 Recommendations 54 4.2.3 Representation Methods for Recommendations 54 4.2.4 Interface 58 4.2.5 Pilot Study 61 4.3 User Study 62 4.3.1 Participants 62 4.3.2 Interface 62 4.3.3 Tasks and Datasets 65 4.3.4 Procedure. 65 4.4 Findings 68 4.4.1 Poor Design Decisions 68 4.4.2 Role of Preview, Animated Transition, and Text 69 4.4.3 Challenges For Understanding Recommendations 70 4.4.4 Learning By Doing 71 4.4.5 Effects of Recommendation Order 71 4.4.6 Personal Criteria for Selecting Recommendations 72 4.5 Discussion 73 4.5.1 Design Implications 73 4.5.2 Limitations and FutureWork 75 4.6 Summary 77 CHAPTER 5. Designing XCluSim: a Visual Analytics System for Comparing Multiple Clustering Results 78 5.1 Motivation 78 5.2 Task Analysis and Design Goals 79 5.3 XCluSim 80 5.3.1 Color Encoding of Clusters Using Tree Colors 82 5.3.2 Overview of All Clustering Results 83 5.3.3 Visualization for Comparing Selected Clustering Results 86 5.3.4 Visualization for Individual Clustering Results 92 5.3.5 Implementation 100 5.4 CaseStudy 100 5.4.1 Elucidating the Role of Ferroxidase in Cryptococcus Neoformans Var. Grubii H99 (CaseStudy 1) 100 5.4.2 Finding a Clustering Result that Clearly Represents Biological Relations (CaseStudy 2) 103 5.5 Discussion 106 5.5.1 Limitations and FutureWork 108 5.6 Summary 108 CHAPTER 6. Future Research Agenda 110 6.0.1 Recommendation for Visual Comparison 110 6.0.2 Understanding the Perception of Subtle Difference 111 6.0.3 Distinguishing InfoVis Novices from Experts 112 CHAPTER 7. Conclusion. 113 Abstract (Korean) 129 Acknowledgments (Korean) 131Docto

    Integrated web visualizations for protein-protein interaction databases

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    BACKGROUND: Understanding living systems is crucial for curing diseases. To achieve this task we have to understand biological networks based on protein-protein interactions. Bioinformatics has come up with a great amount of databases and tools that support analysts in exploring protein-protein interactions on an integrated level for knowledge discovery. They provide predictions and correlations, indicate possibilities for future experimental research and fill the gaps to complete the picture of biochemical processes. There are numerous and huge databases of protein-protein interactions used to gain insights into answering some of the many questions of systems biology. Many computational resources integrate interaction data with additional information on molecular background. However, the vast number of diverse Bioinformatics resources poses an obstacle to the goal of understanding. We present a survey of databases that enable the visual analysis of protein networks. RESULTS: We selected M =10 out of N =53 resources supporting visualization, and we tested against the following set of criteria: interoperability, data integration, quantity of possible interactions, data visualization quality and data coverage. The study reveals differences in usability, visualization features and quality as well as the quantity of interactions. StringDB is the recommended first choice. CPDB presents a comprehensive dataset and IntAct lets the user change the network layout. A comprehensive comparison table is available via web. The supplementary table can be accessed on http://tinyurl.com/PPI-DB-Comparison-2015. CONCLUSIONS: Only some web resources featuring graph visualization can be successfully applied to interactive visual analysis of protein-protein interaction. Study results underline the necessity for further enhancements of visualization integration in biochemical analysis tools. Identified challenges are data comprehensiveness, confidence, interactive feature and visualization maturing

    Dostrzec i zrozumieฤ‡. Porรณwnanie wybranych metod wizualizacji danych ALS wykorzystywanych w archeologii

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    Application of airborne laser scanning (ALS) for archaeological purposes allows for identification of relief features. Unless the detection is automated, the recognition of archaeological objects in the observed dataset is bounded by the interaction between human mind, eye and visual phenomena that are displayed on the screen. To improve effectiveness of ALS interpretation several visualization techniques have been developed. However, due to their complexity the spatial information produced by these algorithms differs. The aim of the paper is to present the discrepancies between the most popular visualization techniques used for archaeological purposes. Unlike previous attempts, the presented comparison is based on the vector outputs of the interpretative mapping. Therefore, we demonstrate in detail the differences in the morphology as well as quantity of identified archaeological features due to the use of various visualization techniques

    Rainbow boxes: a technique for visualizing overlapping sets and an application to the comparison of drugs properties

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    International audienceOverlapping set visualization is a well-known problem in information visualization. This problem considers elements and sets containing all or part of the elements, a given element possibly belonging to more than one set. A typical example is the properties of the 20 amino-acids. A more complex application is the visual comparison of the contraindications or the adverse effects of several similar drugs. The knowledge involved is voluminous, each drug has many contraindications and adverse effects, some of them are shared with other drugs.In this paper, we present rainbow boxes, a novel technique for visualizing overlapping sets, and its application to the properties of amino-acids and to the comparison of drug properties. We also describe a user study comparing rainbow boxes to tables and showing that the former allowed physicians to find information significantly faster. We finally discuss the limits and the perspectives of rainbow boxes

    The fish-eye visualization of foreign currency exchange data streams

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    In a foreign currency exchange market, there are highdensity data streams. The present approaches for visualization of this type of data cannot show us a figure with targeted both local details and global trend information. In this paper, on the basis of features and attributes of foreign currency exchange trading streams, we discuss and compare multiple approaches including interactive zooming, multiform sampling with combination of attribute of large foreign currency exchange data, and fish-eye view embedded visualization for visual display of high-density foreign currency exchange transactions. By comparison, Fish-eye-based visualization is the best option, which can display regional records in details without losing global movement trend in the market in a limited display window. We used Fish-eye technology for output visualization of foreign currency exchange trading strategies in our trading support system linking to realtime foreign currency market closing data. ยฉ 2005, Australian Computer Society, Inc
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