Issue No. 05 - May (2013 vol. 46)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/MC.2013.166
Min Chen , University of Oxford
In 1987, the National Science Foundation (NSF) Panel on Graphics, Image Processing, and Workstations published its landmark report, "Visualization in Scientific Computing," which set out the vision for developing visualization as a scientific field ( www.evl.uic.edu/core.php?mod=4&type=3&indi=348). During the 25 years since then, the visualization field's horizons have broadened significantly, and it now encompasses three major subfields—scientific visualization, information visualization, and visual analytics—as well as many domain-specific areas, including geoinformation visualization, biological data visualization, and software visualization.
This special issue offers an opportunity to refresh and update Computer's broad readership on cutting-edge research in computer-generated visualization.
THE VISUALIZATION FIELD
Visualization is the study of the transformation of data to visual representations for use in developing effective and efficient cognitive processes that make it possible to gain insight from that data.
We can trace one of the most common visual representations, the line graph, back to at least a millennium ago. Roughly three centuries ago, the development of a variety of visual representations such as bar charts, pie charts, circle graphs, scatterplots, and histograms spawned the development of statistical graphics as a collection of techniques to support data analysis.
Although the NSF's 1987 report focused mainly on computer-generated visualization applications in science and engineering, the 25 years since then have witnessed the rapid advancement of visualization as a scientific field in its own right. The 1990s saw the establishment of information visualization as a subfield embracing the nonspatial data commonly found in the humanities, social sciences, economics, and many other disciplines. The 2000s greeted visual analytics as a new subfield that forms a close bond between visualization and other disciplines, such as data mining, machine learning, and human factors.
Today, visualization is more than just a collection of plots, graphs, and computer-generated 3D renderings. There are many visualization techniques for every form of data, including, but not limited to, texts, documents, and corpora; trees, graphs, and networks; image collections and videos; time series; tabular and multivariate data; geographical data; scalar, vector, and tensor fields; isosurfaces; numerical, geometrical, statistical, and other mathematical models; historical events and provenance records; dynamic data streams; algorithms, programs, and computational logs; and a wide range of domain-specific data in disciplines such as biology and medicine.
Visualization is easily perceived as a means for impressing an audience by presenting beautiful computer-generated images and animations. However, significant evidence obtained through perceptual studies and user evaluations confirms that visualization has enabled researchers to be more efficient in making observations and gaining insight from data ( http://dl.acm.org/citation.cfm?id=2442576&picked=prox&preflayout=tabs). It facilitates the formulation of new hypotheses, assists in decision making, enables effectual communication of ideas, and facilitates dissemination of knowledge.
Researchers apply evaluation methodologies as well as quantitative and qualitative analysis to produce the results of their visualization studies. They are progressively strengthening the discipline's foundation by drawing on theories, findings, and technologies from fields such as mathematics, graphic design, communications, and cognitive sciences, as well as other aspects of computer science.
Visualization encompasses a body of knowledge about hundreds of visual designs for different forms of data, thousands of algorithms for transforming data to various visual representations, numerous scientific findings discovered using empirical studies, and a wealth of practical know-how gained through real-world applications.
The visualization literature features mathematical concepts, taxonomies, and process models; algorithms; techniques and metrics; hardware and software systems; controlled user studies and ethnographical studies; and application case studies. This body of knowledge continues to expand rapidly, invigorating serious endeavors for formulating theories and models that can explain and steer visualization processes and stimulating new scientific quests that often require an interdisciplinary approach.
In addition, researchers have developed a range of visualization products and services, resulting in a ubiquitous technology that affects almost every walk of life. The visualization field is a formidable intellectual powerhouse that is delivering solutions in this data deluge era.
IN THIS ISSUE
The call for papers for this issue sought articles addressing visualization research topics in regard to "what could, would, or should have been achieved in the next 5, 10, or 15 years." This involved reflection on how a particular research area has evolved, as well as projections about how it might evolve in the future. We particularly encouraged articles featuring scenario-based vision statements, insightful feasibility analysis, and major obstacles and expected paradigm shifts, as well as the anticipated impact on science, technology, and society.
In "Large Area Displays: The Changing Face of Visuali-zation," Aditi Majumder and Behzad Sajadi provide a survey of visualization displays over the past couple of decades and explore potential future displays.
"Research Challenges for Visualization Software," by Hank Childs and his colleagues, examines how visualization software frameworks have evolved over the past 25 years and presents upcoming challenges in visualization software development in regard to massive parallelization, emerging processor architectures, application architecture and data management, data models, rendering, and interaction.
In "Storytelling: The Next Step for Visualization," Robert Kosara and Jock Mackinlay review the literature on storytelling and propose that the use of storytelling elements is a next logical step beyond exploration and analysis in visualization research.
"Reimagining the Scientific Visualization Interaction Paradigm," by Daniel Keefe and Tobias Isenberg, takes readers on a journey of natural, physical, and spatial human-computer interfaces and proposes future uses of these interfaces for exploring visualizations in science, engineering, art, and the humanities.
Healthcare: An emerging application
"Improving Healthcare with Interactive Visualization," by Ben Shneiderman and his colleagues, explores the use of interactive information visualization and visual analytics methods for personal wellness programs, professional healthcare delivery, and public health policymaking.
Social media data: A new challenge
In "Visual Analysis of Social Media Data," Tobias Schreck and Daniel Keim explore how visual analytics methods can aid in trend analysis for marketing, opinion analysis, demographics, and civil protection of social media services such as Twitter, Flickr, and news blogs.
SUPPLEMENTARY MULTIMEDIA CONTENT
In addition to the articles in this special issue, we also provide multimedia content associated with "Large Area Displays: The Changing Face of Visualization," "Reimagining the Scientific Visualization Interaction Paradigm," "Improving Healthcare with Interactive Visualization," and "Visual Analysis of Social Media Data." This supplementary multimedia content can be found at www.computer.org/portal/web/computingnow/computer/multimedia.
We were delighted to have the opportunity to curate and develop this special issue. We hope the content will inspire you to contribute to visualization research directly or to undertake collaboration with visualization researchers.
We thank all of the authors who submitted papers for inclusion in this special issue, and express our appreciation to the topic-area experts who reviewed these submissions. This issue would not have happened without the support of Computer's editor in chief, Ron Vetter, as well as Computer's editorial and production teams. Additionally, we are grateful to Chris Johnson, at the University of Utah, for his advice in formulating the call for papers as well as his comments on this introduction.
Theresa-Marie Rhyne, an independent visualization consultant, is a member of Computer's advisory panel, the Visualization Viewpoints editor for IEEE Computer Graphics and Applications, and a member of the Computing Now advisory board. Contact her at email@example.com.
Min Chen is a professor of scientific visualization in the Oxford e-Research Centre at the University of Oxford, UK. He is an associate editor in chief of IEEE Transactions on Visualization and Computer Graphics. Contact him at firstname.lastname@example.org.