The Eyes Have It: a Task by Data Type Taxonomy for Information Visualizations
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The 2008 Visualization Career Award
The 2008 Visualization Career Award Lawrence J. Rosenblum The 2008 Visualization Career Award goes to Lawrence (Larry) Rosenblum, in recognition of early technical contributions and unselfish work to nurture and sustain the field of visuali- zation. In the 1980s and early 1990s Larry developed visualization techniques that produced scientific advances in physical oceanography, ocean acoustics, ocean geophysics, and ocean engineering. He also initiated numerous activities to develop visualization as a recognized research field. Subsequent research by his group has advanced VR/AR, graphics, and visual analytics while he has continued to perform significant service to organizations and confer- ences in visualization and VR/AR. As a Program Officer at NSF and ONR, Larry devel- oped new visualization research programs. For his outstanding contributions in research and in governmental program development, and for his pioneering work to nurture and sustain Lawrence Rosenblum the field of visualization, the IEEE VGTC is pleased to award Larry Rosenblum the 2008 Visualization Career Award. Award Recipient 2008 Biography Larry Rosenblum is Director of the Virtual Reality and was used in many of the early visualization courses in Laboratory at the U.S. Naval Research Laboratory (NRL). academia. He is currently detailed to the U.S. National Science Returning to NRL, Larry focused primarily on virtual Foundation (NSF), where he is Program Director for reality research, including seminal work in U.S. Responsive Graphics and Visualization. Majoring in Mathematics, Workbench technology with encouragement from Wolfgang he received his BA from Queens College (CUNY) and his Krueger, and on augmented reality (AR) systems research. MS and PhD (in Number Theory) from The Ohio State His group’s research into uncertainty visualization produced University. -
Arxiv:2009.03390V1 [Cs.DL] 7 Sep 2020 Meta-Analytical Work Around Geospatial Analytics and Geovisualiza- Tion That May Shed Light on Opportunities for Innovation
A Review of Geospatial Content in IEEE Visualization Publications Alexander Yoshizumi* Megan M. Coffer† Elyssa L. Collins† Mollie D. Gaines† Xiaojie Gao† Kate Jones† Ian R. McGregor† Katie A. McQuillan† Vinicius Perin† Laura M. Tomkins† Thom Worm† Laura Tateosian* Center for Geospatial Analytics, North Carolina State University Figure 1: Attributes of 94 IEEE VIS papers from years 2017-2019 found to have geospatial content. From top to bottom: data domain, geospatial nature of the paper (GEO), and VIS Conference paper type and track (TRK) are shown for each paper. Percentages on the top band (lightest gray bars) correspond to data domain types. The GEO band marks papers as either containing both geospatial data and a geospatial analysis (dark gray) or geospatial data only (light gray). The TRK band is colored by the VIS Conference paper types and tracks listed on Open Access VIS [15]. ABSTRACT gies such as GPS-equipped mobile devices, remote sensing satellites, Geospatial analysis is crucial for addressing many of the world’s and drones have proliferated, the centrality of georeferenced data most pressing challenges. Given this, there is immense value in has only continued to grow. The complexity and volume of the data improving and expanding the visualization techniques used to com- and the importance of the issues at stake drive a need for innovative municate geospatial data. In this work, we explore this important visualization tools to support exploration and communication of intersection – between geospatial analytics and visualization – by geospatial information. examining a set of recent IEEE VIS Conference papers (a selec- As a focal event for the visualization community, the IEEE Vi- tion from 2017-2019) to assess the inclusion of geospatial data and sualization (VIS) Conference profoundly influences the agenda for geospatial analyses within these papers. -
Tamara Munzner
The 2015 Visualization Technical Achievement Award Tamara Munzner The 2015 Visualization Technical Achievement Award goes to Tamara Munzner in recognition of foundational research that has produced a scientific basis for principles and design choices for visualization. The IEEE Visualization & Graphics Technical Community (VGTC) is pleased to award Tamara Munzner the 2015 Visualization Technical Achievement Award. Biography Tamara Munzner Tamara Munzner is a full professor at the University of University of British British Columbia Department of Computer Science, where Columbia she has been since 2002. She was a research scientist from Award Recipient 2015 2000 to 2002 at the Compaq Systems Research Center (the former DEC SRC). She earned her PhD from Stanford between 1995 and 2000, working with Pat Hanrahan. She and prescribe models and methods for visualization design holds a BS from Stanford from 1991, the year she first and the research process itself, including a nested model of attended VIS. design and validation and methodology for design studies. From 1991 to 1995, Tamara was a technical staff Her 2014 book Visualization Analysis and Design provides member at The Geometry Center, based at the University a systematic, comprehensive framework for thinking about of Minnesota. She was one of the architects and imple- visualization in terms of principles and design choices. It mentors of Geomview, the Center’s public domain interac- features a unified approach encompassing information visu- tive 3D visualization system that supported hyperbolic and alization techniques for the abstract data of tables and net- spherical geometry in addition to Euclidean geometry. She works, scientific visualization techniques for spatial data, was co-director and one of the animators of two videos and visual analytics techniques for interweaving data trans- that brought concepts from the cutting edge of geomet- formation and analysis with interactive visual exploration. -
A Bounded Measure for Estimating the Benefit of Visualization
arXiv Report 2021 Volume 0 (1981), Number 0 A Bounded Measure for Estimating the Benefit of Visualization: Theoretical Discourse and Conceptual Evaluation Min Chen1 and Mateu Sbert2 1University of Oxford, UK and 2 University of Girona, Spain Visual Mapping with Topological Abstraction Visual Mapping with a Volume Rendering Integral Color Pixel Color Opacity Color Pixel Color Opacity ...... Color Pixel Color Opacity (a) mapping from different sets of voxel values to the same pixel color (b) mapping from different geographical paths to the same line segment Figure 1: Visual encoding typically features many-to-one mapping from data to visual representations, hence information loss. The significant amount of information loss in volume visualization and metro maps suggests that viewers not only can abide the information loss but also benefit from it. Measuring such benefits can lead to new advancements of visualization, in theory and practice. Abstract Information theory can be used to analyze the cost-benefit of visualization processes. However, the current measure of benefit contains an unbounded term that is neither easy to estimate nor intuitive to interpret. In this work, we propose to revise the existing cost-benefit measure by replacing the unbounded term with a bounded one. We examine a number of bounded measures that include the Jenson-Shannon divergence and a new divergence measure formulated as part of this work. We describe the rationale for proposing a new divergence measure. As the first part of comparative evaluation, we use visual analysis to support the multi-criteria comparison, narrowing the search down to several options with better mathematical properties. -
F I N a L P R O G R a M 1998
1998 FINAL PROGRAM October 18 • October 23, 1998 Sheraton Imperial Hotel Research Triangle Park, NC THE INSTITUTE OF ELECTRICAL IEEE IEEE VISUALIZATION 1998 & ELECTRONICS ENGINEERS, INC. COMPUTER IEEE SOCIETY Sponsored by IEEE Computer Society Technical Committee on Computer Graphics In Cooperation with ACM/SIGGRAPH Sessions include Real-time Volume Rendering, Terrain Visualization, Flow Visualization, Surfaces & Level-of-Detail Techniques, Feature Detection & Visualization, Medical Visualization, Multi-Dimensional Visualization, Flow & Streamlines, Isosurface Extraction, Information Visualization, 3D Modeling & Visualization, Multi-Source Data Analysis Challenges, Interactive Visualization/VR/Animation, Terrain & Large Data Visualization, Isosurface & Volume Rendering, Simplification, Marine Data Visualization, Tensor/Flow, Key Problems & Thorny Issues, Image-based Techniques and Volume Analysis, Engineering & Design, Texturing and Rendering, Art & Visualization Get complete, up-to-date listings of program information from URL: http://www.erc.msstate.edu/vis98 http://davinci.informatik.uni-kl.de/Vis98 Volvis URL: http://www.erc.msstate.edu/volvis98 InfoVis URL: http://www.erc.msstate.edu/infovis98 or contact: Theresa-Marie Rhyne, Lockheed Martin/U.S. EPA Sci Vis Center, 919-541-0207, [email protected] Robert Moorhead, Mississippi State University, 601-325-2850, [email protected] Direct Vehicle Loading Access S Dock Salon Salon Sheraton Imperial VII VI Imperial Convention Center HOTEL & CONVENTION CENTER Convention Center Phones -
An Interview with Visualization Pioneer Ben Shneiderman
6/23/2020 The purpose of visualization is insight, not pictures: An interview with visualization pioneer Ben Shneiderman The purpose of visualization is insight, not pictures: An interview with visualization pioneer Ben Shneiderman Jessica Hullman Follow Mar 12, 2019 · 13 min read Few people in visualization research have had careers as long and as impactful as Ben Shneiderman. We caught up with Ben over email in between his travels to get his take on visualization research, what’s worked in his career, and his advice for practitioners and researchers. Enjoy! Multiple Views: One of the main purposes of this blog is to explain to people what visualization research is to practitioners and, possibly, laypeople. How would you answer the question “what is visualization research”? Ben S: First let me define information visualization and its goals, then I can describe visualization research. Information visualization is a powerful interactive strategy for exploring data, especially when combined with statistical methods. Analysts in every field can use interactive information visualization tools for: more effective detection of faulty data, missing data, unusual distributions, and anomalies deeper and more thorough data analyses that produce profounder insights, and richer understandings that enable researchers to ask bolder questions. Like a telescope or microscope that increases your perceptual abilities, information visualization amplifies your cognitive abilities to understand complex processes so as to support better decisions. In our best -
Treemap Art Project
EVERY ALGORITHM HAS ART IN IT Treemap Art Project By Ben Shneiderman Visit Exhibitions @ www.cpnas.org 2 tree-structured data as a set of nested rectangles) which has had a rippling impact on systems of data visualization since they were rst conceived in the 1990s. True innovation, by denition, never rests on accepted practices but continues to investigate by nding new In his book, “Visual Complexity: Mapping Patterns of perspectives. In this spirit, Shneiderman has created a series Information”, Manuel Lima coins the term networkism which of prints that turn our perception of treemaps on its head – an he denes as “a small but growing artistic trend, characterized eort that resonates with Lima’s idea of networkism. In the by the portrayal of gurative graph structures- illustrations of exhibition, Every AlgoRim has ART in it: Treemap Art network topologies revealing convoluted patterns of nodes and Project, Shneiderman strips his treemaps of the text labels to links.” Explaining networkism further, Lima reminds us that allow the viewer to consider their aesthetic properties thus the domains of art and science are highly intertwined and that laying bare the fundamental property that makes data complexity science is a new source of inspiration for artists and visualization eective. at is to say that the human mind designers as well as scientists and engineers. He states that processes information dierently when it is organized visually. this movement is equally motivated by the unveiling of new In so doing Shneiderman seems to daringly cross disciplinary is exhibit is a project of the knowledge domains as it is by the desire for the representation boundaries to wear the hat of the artist – something that has Cultural Programs of the National Academy of Sciences of complex systems. -
An Exploration of Color in Scientific Visualization
THE END OF THE RAINBOW? AN EXPLORATION OF COLOR IN SCIENTIFIC VISUALIZATION by BRENDA GRIGGS A THESIS Presented to the Department of Computer and Information Science at the University of Oregon in partial fulfillment of the requirements for the degree of Bachelor of Science June 2014 THESIS APPROVAL PAGE Student: Brenda Griggs Title: The End of the Rainbow? An Exploration of Color in Scientific Visualization This thesis has been accepted and approved in partial fulfillment of the requirements for the Bachelor of Science degree in the Department of Computer and Information Science by: Eugene Luks Chair Hank Childs Advisor Original approval signatures are on file with the Department of Computer and Information Science at the University of Oregon. Degree awarded June 2014 ii THESIS ABSTRACT Brenda Griggs Bachelor of Science Computer and Information Science June 2014 Title: The End of the Rainbow? An exploration of Color in Scientific Visualization Approved: ______________________________ Hank Childs Scientific visualization aims to represent, manipulate, and explore scientific data in a way that provides understanding and insight for both expert and non-expert viewers. As color is a key element in visualization, it is important to keep in mind color map selection as it can significantly influence the viewer's perception and interpretation of the data. Although the rainbow color map is a prevalent choice among the scientific community, research has found it to mislead viewers by obscuring features and introducing artifacts into the visualization. This paper explores the impact of data representation through color using various univariate and redundant color maps. Our study indicates the rainbow color map is not the best choice for interval and ratio data representation, and validates various design guidelines from literature. -
Christopher L. North – Curriculum Vitae (Updated Sept 2014)
Christopher L. North – Curriculum Vitae (updated Sept 2014) Department of Computer Science (540) 231-2458 114 McBryde Hall (540) 231-9218 fax Virginia Tech north @ vt . edu Blacksburg, VA 24061-0106 http://www.cs.vt.edu/~north/ Google Scholar: • http://scholar.google.com/citations?user=yBZ7vtkAAAAJ • h-index = 35 Short Bio: Dr. Chris North is a Professor of Computer Science at Virginia Tech. He is Associate Director of the Discovery Analytics Center, and leads the Visual Analytics research group. He is principle architect of the GigaPixel Display Laboratory, one of the most advanced display and interaction facilities in the world. He also participates in the Center for Human-Computer Interaction, and the Hume Center for National Security, and is a member of the DHS supported VACCINE Visual Analytics Center of Excellence. He was awarded Faculty Fellow of the College of Engineering in 2007, and the Dean’s Award for Research Excellence in 2014. He earned his Ph.D. at the University of Maryland, College Park, in 2000. He has served as General Co-Chair of IEEE VisWeek 2009, and as Papers Chair of the IEEE Information Visualization (InfoVis) and IEEE Visual Analytics Science and Technology (VAST) Conferences. He has served on the editorial boards of IEEE Transactions on Visualization and Computer Graphics (TVCG), the Information Visualization journal, and Foundations and Trends in HCI. He has been awarded over $6M in grants, co-authored over 100 peer-reviewed publications, and delivered 3 keynote addresses at symposia in the field. He has graduated 8 Ph.D. and 14 M.S. thesis students, 4 receiving outstanding research awards at Virginia Tech, and advised over 70 undergraduate research students including several award winners at Virginia Tech’s annual undergraduate research symposium. -
Human-Centered Artificial Intelligence: Three Fresh Ideas
AIS Transactions on Human-Computer Interaction Volume 12 Issue 3 Article 1 9-30-2020 Human-Centered Artificial Intelligence: Three Fresh Ideas Ben Shneiderman University of Maryland, [email protected] Follow this and additional works at: https://aisel.aisnet.org/thci Recommended Citation Shneiderman, B. (2020). Human-Centered Artificial Intelligence: Three Fresh Ideas. AIS Transactions on Human-Computer Interaction, 12(3), 109-124. https://doi.org/10.17705/1thci.00131 DOI: 10.17705/1thci.00131 This material is brought to you by the AIS Journals at AIS Electronic Library (AISeL). It has been accepted for inclusion in AIS Transactions on Human-Computer Interaction by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact [email protected]. Transactions on Human-Computer Interaction 109 Transactions on Human-Computer Interaction Volume 12 Issue 3 9-2020 Human-Centered Artificial Intelligence: Three Fresh Ideas Ben Shneiderman Department of Computer Science and Human-Computer Interaction Lab, University of Maryland, College Park, [email protected] Follow this and additional works at: http://aisel.aisnet.org/thci/ Recommended Citation Shneiderman, B. (2020). Human-centered artificial intelligence: Three fresh ideas. AIS Transactions on Human- Computer Interaction, 12(3), pp. 109-124. DOI: 10.17705/1thci.00131 Available at http://aisel.aisnet.org/thci/vol12/iss3/1 Volume 12 pp. 109 – 124 Issue 3 110 Transactions on Human-Computer Interaction Transactions on Human-Computer Interaction Research Commentary DOI: 10.17705/1thci.00131 ISSN: 1944-3900 Human-Centered Artificial Intelligence: Three Fresh Ideas Ben Shneiderman Department of Computer Science and Human-Computer Interaction Lab, University of Maryland, College Park [email protected] Abstract: Human-Centered AI (HCAI) is a promising direction for designing AI systems that support human self-efficacy, promote creativity, clarify responsibility, and facilitate social participation. -
Using Visualization to Understand the Behavior of Computer Systems
USING VISUALIZATION TO UNDERSTAND THE BEHAVIOR OF COMPUTER SYSTEMS A DISSERTATION SUBMITTED TO THE DEPARTMENT OF COMPUTER SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Robert P. Bosch Jr. August 2001 c Copyright by Robert P. Bosch Jr. 2001 All Rights Reserved ii I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Dr. Mendel Rosenblum (Principal Advisor) I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Dr. Pat Hanrahan I certify that I have read this dissertation and that in my opinion it is fully adequate, in scope and quality, as a dissertation for the degree of Doctor of Philosophy. Dr. Mark Horowitz Approved for the University Committee on Graduate Studies: iii Abstract As computer systems continue to grow rapidly in both complexity and scale, developers need tools to help them understand the behavior and performance of these systems. While information visu- alization is a promising technique, most existing computer systems visualizations have focused on very specific problems and data sources, limiting their applicability. This dissertation introduces Rivet, a general-purpose environment for the development of com- puter systems visualizations. Rivet can be used for both real-time and post-mortem analyses of data from a wide variety of sources. The modular architecture of Rivet enables sophisticated visualiza- tions to be assembled using simple building blocks representing the data, the visual representations, and the mappings between them. -
Tamara Munzner Department of Computer
Data Visualization Pitfalls to Avoid Tamara Munzner Department of Computer Science University of British Columbia Department of Industry, Innovation and Science, Economic and Analytical Services Division June 23 2017, Canberra Australia http://www.cs.ubc.ca/~tmm/talks.html#vad17can-morn @tamaramunzner Visualization (vis) defined & motivated Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. Visualization is suitable when there is a need to augment human capabilities rather than replace people with computational decision-making methods. • human in the loop needs the details –doesn't know exactly what questions to ask in advance –longterm exploratory analysis –presentation of known results –stepping stone towards automation: refining, trustbuilding • intended task, measurable definitions of effectiveness more at: Visualization Analysis and Design, Chapter 1. Munzner. AK Peters Visualization Series, CRC Press, 2014. 2 Why use an external representation? Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. • external representation: replace cognition with perception [Cerebral: Visualizing Multiple Experimental Conditions on a Graph with Biological Context. Barsky, Munzner, Gardy, and Kincaid. IEEE TVCG (Proc. InfoVis) 14(6):1253-1260, 2008.] 3 Why represent all the data? Computer-based visualization systems provide visual representations of datasets designed to help people carry out tasks more effectively. • summaries lose information, details matter – confirm expected and find unexpected patterns – assess validity of statistical model Anscombe’s Quartet Identical statistics x mean 9 x variance 10 y mean 7.5 y variance 3.75 x/y correlation 0.816 https://www.youtube.com/watch?v=DbJyPELmhJc Same Stats, Different Graphs 4 What resource limitations are we faced with? Vis designers must take into account three very different kinds of resource limitations: those of computers, of humans, and of displays.