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voyager: Exploratory analysis via Faceted Browsing of visualization Recommendations

IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2016年第1期22卷 649-658页

作者： Wongsuphasawat, Kanit Moritz, Dominik Anand, Anushka Mackinlay, Jock Howe, Bill Heer, Jeffrey Univ Washington Seattle WA 98195 USA

General visualization tools typically require manual specification of views: analysts must select data variables and then choose which transformations and visual encodings to apply. These decisions often involve both domain and visualization design expertise, and may impose a tedious specification process that impedes exploration. In this paper, we seek to complement manual chart construction with interactive navigation of a gallery of automatically-generated visualizations. We contribute voyager, a mixed-initiative system that supports faceted browsing of recommended charts chosen according to statistical and perceptual measures. We describe voyager's architecture, motivating design principles, and methods for generating and interacting with visualization recommendations. In a study comparing voyager to a manual visualization specification tool, we find that voyager facilitates exploration of previously unseen data and leads to increased data variable coverage. We then distill design implications for visualization tools, in particular the need to balance rapid exploration and targeted question-answering.

关键词： User interfaces information visualization exploratory analysis visualization recommendation mixed-initiative systems

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Network Narratives: data Tours for visual Network exploration and analysis 23

Network Narratives: Data Tours for Visual Network Exploratio...

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CHI conference on Human Factors in Computing Systems (CHI)

作者： Li, Wenchao Schottler, Sarah Scott-Brown, James Wang, Yun Chen, Siming Qu, Huamin Bach, Benjamin Hong Kong Univ Sci & Technol Hong Kong Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland Microsoft Res Asia Beijing Peoples R China Fudan Univ Shanghai Peoples R China

ISBN: (纸本)9781450394215

This paper introduces semi-automatic data tours to aid the exploration of complex networks. Exploring networks requires significant effort and expertise and can be time-consuming and challenging. Distinct from guidance and recommender systems for visual analytics, we provide a set of goal-oriented tours for network overview, ego-network analysis, community exploration, and other tasks. Based on interviews with five network analysts, we developed a user interface (NetworkNarratives) and 10 example tours. The interface allows analysts to navigate an interactive slideshow featuring facts about the network using visualizations and textual annotations. On each slide, an analyst can freely explore the network and specify nodes, links, or subgraphs as seed elements for follow-up tours. Two studies, comprising eight expert and 14 novice analysts, show that data tours reduce exploration effort, support learning about network exploration, and can aid the dissemination of analysis results. NetworkNarratives is available online, together with detailed illustrations for each tour.

关键词： Guided exploration network visualization

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MTDES: Multi-dimensional Temporal data exploration System;Strong Support for Exploratory analysis Award in vAST 2018, Mini-Challenge 2 13

<i>MTDES</i>: Multi-dimensional Temporal Data Exploration Sy...

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13th IEEE conference on visual Analytics Science and Technology (vAST)

作者： Pham, vung v. Dang, Tommy Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

ISBN: (纸本)9781538668610

This work proposes a visual analytic solution which is well-designed to provide investigative functions with fluent interactions to analyze multi-dimensional temporal data. The solution allows users to view different dimensions of the data at different levels of details with a well-designed mixture of different visualizations and smooth interactions. At the general/overview level, various aggregation strategies are used to reduce data to be visualized, and different sorting procedures are used to cluster correlated data together to help discover patterns. Detail views are provided to explore and confirm/reject the identified patterns. Interaction and smooth transition between views are implemented to enable natural actions while performing analysis tasks. This work also presents the result of applying the solution to the vAST 2018 - Mini-Challenge (MC) 2 dataset, which led to the Strong Support for Exploratory analysis award for the challenge.

关键词： Multi-dimensional, temporal data analysis-Heat-map-Line-graph-Interaction Design vAST Challenge 2018-Mini-Challenge 2-Award: Strong Support for Exploratory analysis

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From visual exploration to Storytelling and Back Again

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COMPUTER GRAPHICS FORUM 2016年第3期35卷 491-500页

作者： Gratzl, S. Lex, A. Gehlenborg, N. Cosgrove, N. Streit, M. Johannes Kepler Univ Linz Linz Austria Univ Utah Salt Lake City UT 84112 USA Harvard Med Sch Boston MA USA

The primary goal of visual data exploration tools is to enable the discovery of new insights. To justify and reproduce insights, the discovery process needs to be documented and communicated. A common approach to documenting and presenting findings is to capture visualizations as images or videos. Images, however, are insufficient for telling the story of a visual discovery, as they lack full provenance information and context. videos are difficult to produce and edit, particularly due to the non-linear nature of the exploratory process. Most importantly, however, neither approach provides the opportunity to return to any point in the exploration in order to review the state of the visualization in detail or to conduct additional analyses. In this paper we present CLUE (Capture, Label, Understand, Explain), a model that tightly integrates data exploration and presentation of discoveries. Based on provenance data captured during the exploration process, users can extract key steps, add annotations, and author vistories, visual stories based on the history of the exploration. These vistories can be shared for others to view, but also to retrace and extend the original analysis. We discuss how the CLUE approach can be integrated into visualization tools and provide a prototype implementation. Finally, we demonstrate the general applicability of the model in two usage scenarios: a Gapminder-inspired visualization to explore public health data and an example from molecular biology that illustrates how vistories could be used in scientific journals.

关键词： Categories and Subject Descriptors (according to ACM CCS): H.5.2 [Information Systems]: Information Interfaces and Presentation&mdash User Interfaces-Graphical user interfaces

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3D visual data mining -: goals and experiences

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COMPUTATIONAL STATISTICS & data analysis 2003年第4期43卷 445-469页

作者： Böhlen, M Bukauskas, L Eriksen, PS Lauritzen, SL Mazeika, A Musaeus, P Mylov, P Aalborg Univ Dept Comp Sci DK-9220 Aalborg Denmark

The visual exploration of large databases raises a number of unresolved inference problems and calls for new interaction patterns between multiple disciplines-both at the conceptual and technical level. We present an approach that is based on the interaction of four disciplines: database systems, statistical analyses, perceptual and cognitive psychology, and scientific visualization. At the conceptual level we offer perceptual and cognitive insights to guide the information visualization process. We then choose cluster surfaces to exemplify the data mining process, to discuss the tasks involved, and to work out the interaction patterns. (C) 2003 Elsevier B.v. All rights reserved.

关键词： visual data mining immersive data exploration observer relative data extraction perception of space and objects nested density surfaces

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CyteGuide: visual Guidance for Hierarchical Single-Cell analysis

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2018年第1期24卷 739-748页

作者： Hollt, Thomas Pezzotti, Nicola van Unen, vincent Koning, Frits Lelieveldt, Boudewijn P. F. vilanova, Anna Delft Univ Technol Comp Graph & Visualizat Grp Delft Netherlands Leiden Univ Med Ctr Computat Biol Ctr Leiden Netherlands Leiden Univ Med Ctr Dept Immunohematol & Blood Transfus Leiden Netherlands Leiden Univ Med Ctr Div Image Proc Dept Radiol Leiden Netherlands Delft Univ Technol Pattern Recognit & Bioinformat Grp Delft Netherlands

Single-cell analysis through mass cytometry has become an increasingly important tool for immunologists to study the immune system in health and disease. Mass cytometry creates a high-dimensional description vector for single cells by time-of-flight measurement. Recently, t-Distributed Stochastic Neighborhood Embedding (t-SNE) has emerged as one of the state-of-the-art techniques for the visualization and exploration of single-cell data. Ever increasing amounts of data lead to the adoption of Hierarchical Stochastic Neighborhood Embedding (HSNE), enabling the hierarchical representation of the data. Here, the hierarchy is explored selectively by the analyst, who can request more and more detail in areas of interest. Such hierarchies are usually explored by visualizing disconnected plots of selections in different levels of the hierarchy. This poses problems for navigation, by imposing a high cognitive load on the analyst. In this work. we present an interactive summary-visualization to tackle this problem. CyteGuide guides the analyst through the exploration of hierarchically represented single-cell data, and provides a complete overview of the current state of the analysis. We conducted a two-phase user study with domain experts that use HSNE for data exploration. We first studied their problems with their current workflow using HSNE and the requirements to ease this workflow in a field study. These requirements have been the basis for our visual design. In the second phase, we verified our proposed solution in a user evaluation.

关键词： Hierarchical data HSNE Single-Cell analysis visual Guidance

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visual exploration of rating datasets and user groups

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 105卷 547-561页

作者： Zegarra, Fabian Colque Carbajal Ipenza, Juan C. Omidvar-Tehrani, Behrooz Moreira, viviane P. Amer-Yahia, Sihem Comba, Joao L. D. Univ Fed Rio Grande do Sul Porto Alegre RS Brazil NAVER LABS Europe Meylan France Univ Grenoble Alpes Grenoble France CNRS Paris France

The increasing availability of rating datasets (i.e., datasets containing user evaluations on items such as products and services) constitutes a new opportunity in various applications ranging from behavioral analytics to recommendations. In this paper, we describe the design of vUGA, a visual enabler for the exploration of rating data and user groups. vUGA helps analysts, be they novice analysts or domain experts, acquire an understanding of their data through a seamless integration between exploring users and exploring their collective behavior via group analysis. vUGA is data-driven and does not require analysts to know the value distributions in their data. While automated systems can identify and suggest potentially interesting groups, they can do that for well-specified needs (e.g., through SQL QUERIES or constrained mining). vUGA helps analysts filter and refine their exploration as they discover what lies in the data. vUGA enables analysts to easily acquire statistics about their data, form groups, and find similar and dissimilar groups. While most visual analytics systems are data-dependent, vUGA relies on a data model that captures user data in such a way that a variety of group formation and exploration approaches can be used. We describe the architecture of vUGA and illustrate its use via tasks and a user study. We conclude with a discussion on future work enabled by vUGA. (C) 2019 Elsevier B.v. All rights reserved.

关键词： User data exploration User group exploration visual analytics User data Dimensionality reduction

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A Framework for Exploring Multidimensional data with 3D Projections

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COMPUTER GRAPHICS FORUM 2011年第3期30卷 1111-1120页

作者： Poco, J. Etemadpour, R. Paulovich, F. v. Long, T. v. Rosenthal, P. Oliveira, M. C. F. Linsen, L. Minghim, R. Univ Sao Paulo Sao Carlos SP Brazil Univ Utah Salt Lake City UT 84112 USA Jacobs Univ Bremen Germany Univ Transport & Commun Hanoi Hanoi Vietnam Tech Univ Chemnitz Chemnitz Germany

visualization of high-dimensional data requires a mapping to a visual space. Whenever the goal is to preserve similarity relations a frequent strategy is to use 2D projections, which afford intuitive interactive exploration, e. g., by users locating and selecting groups and gradually drilling down to individual objects. In this paper, we propose a framework for projecting high-dimensional data to 3D visual spaces, based on a generalization of the Least-Square Projection (LSP). We compare projections to 2D and 3D visual spaces both quantitatively and through a user study considering certain exploration tasks. The quantitative analysis confirms that 3D projections outperform 2D projections in terms of precision. The user study indicates that certain tasks can be more reliably and confidently answered with 3D projections. Nonetheless, as 3D projections are displayed on 2D screens, interaction is more difficult. Therefore, we incorporate suitable interaction functionalities into a framework that supports 3D transformations, predefined optimal 2D views, coordinated 2D and 3D views, and hierarchical 3D cluster definition and exploration. For visually encoding data clusters in a 3D setup, we employ color coding of projected data points as well as four types of surface renderings. A second user study evaluates the suitability of these visual encodings. Several examples illustrate the framework's applicability for both visual exploration of multidimensional abstract (non-spatial) data as well as the feature space of multi-variate spatial data.

关键词： MULTIDIMENSIONAL databases INFORMATION visualization LEAST squares THREE-dimensional imaging IMAGE analysis

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ActiviTree: Interactive visual exploration of Sequences in Event-Based data Using Graph Similarity

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2009年第6期15卷 945-952页

作者： vrotsou, Katerina Johansson, Jimmy Cooper, Matthew Linkoping Univ S-58183 Linkoping Sweden

The identification of significant sequences in large and complex event-based temporal data is a challenging problem with applications in many areas of today's information intensive society. Pure visual representations can be used for the analysis, but are constrained to small data sets. Algorithmic search mechanisms used for larger data sets become expensive as the data size increases and typically focus on frequency of occurrence to reduce the computational complexity, often overlooking important infrequent sequences and outliers. In this paper we introduce an interactive visual data mining approach based on an adaptation of techniques developed for web searching, combined with an intuitive visual interface, to facilitate user-centred exploration of the data and identification of sequences significant to that user. The search algorithm used in the exploration executes in negligible time, even for large data, and so no pre-processing of the selected data is required, making this a completely interactive experience for the user. Our particular application area is social science diary data but the technique is applicable across many other disciplines.

关键词： Interactive visual exploration event-based data sequence identification graph similarity node similarity

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Geographically weighted visualization: Interactive graphics for scale-varying exploratory analysis

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IEEE TRANSACTIONS ON visualIZATION AND COMPUTER GRAPHICS 2007年第6期13卷 1161-1168页

作者： Dykes, Jason Brunsdon, Chris City Univ London Dept Informat Sci London EC1V 0HB England Univ Leicester Dept Geog Leicester LE1 7RH Leics England

We introduce a series of geographically weighted (GW) interactive graphics, or geowigs, and use them to explore spatial relationships at a range of scales. We visually encode information about geographic and statistical proximity and variation in novel ways through gw-choropleth maps, multivariate gw-boxplots, gw-shading and scalograms. The new graphic types reveal information about GW statistics at several scales concurrently. We impernent these views in prototype software containing dynamic links and GW interactions that encourage exploration and refine them to consider directional geographies. An informal evaluation uses interactive GW techniques to consider Guerry's clataset of 'moral statistics', casting doubt on correlations originally proposed through visual analysis, revealing new local anomalies and suggesting multivariate geographic relationships. Few attempts at visually synthesising geography with multivariate statistical values at multiple scales have been reported. The geowigs proposed here provide informative representations of multivariate local variation, particularly when combined with interactions that coordinate views and result in gw-shading. We argue that they are widely applicable to area and point-based geographic data and provide a set of methods to support visual analysis using GW statistics through which the effects of geography can be explored at multiple scales.

关键词： geographical weighting exploratory data analysis scale multivariate directional interaction coordinated views

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