Lecture 7

Visualization reports from the 00s - Part I

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NIH/NSF Visualization Research Challenges - January 2006

Visualization Research Challenges Cover
available in pdf format here

20 years since the last report - what's changed? what's the same?

Visualization is fundamental to understanding models of complex phenomena. Visualization reduces and refines data streams rapidly and economically, thus enabling us to winnow huge volumes of data.

Although well-designed visualizations have the power to help people enormously, naive attempts to create visualizations typically lead to “reinventing the wheel” at best, and all too often result in poorly designed visualizations that are ineffective or even actively misleading. Designing effective visualizations is a complex process that requires a sophisticated understanding of human information processing capabilities, both visual and cognitive, and a solid grounding in the considerable body of work that has already been introduced in the visualization field. Further research in visualization, and the technology transfer of effective visualization methodologies into the working practice of medicine, science, engineering, and business, will be critical in handling the ongoing information explosion. The insights provided by visualization will help specialists discover or create new theories, techniques, and methods, and improve the daily lives of the general public.

While visualization is itself a discipline, advances in visualization lead inevitably to advances in other disciplines. Just as knowledge of mathematics and statistics has become indispensable in subjects as diverse as the traditional sciences, economics, security, medicine, sociology, and public policy, so too is visualization becoming indispensable in enabling researchers in other fields to achieve their goals. Like statistics, visualization is concerned with the analysis and interpretation of information, both quantitative and qualitative, and with the presentation of data in a way which conveys their salient features most clearly. Both fields develop, understand, and abstract data analytic ideas and package them in the form of techniques, algorithms, and software for a multitude of application areas.

People are biologically equipped to make spatial inferences and decisions, and experience refines their ability to do so. Visualizations can bootstrap this facility metaphorically, by mapping elements and spatial relations in the abstract domain onto elements and relations in a concrete visualization. Through such maps, the human ability to make spatial inferences can be transferred to abstract domains. However, human information processing capabilities, both visual and cognitive, are limited and systematically biased. Effective visualizations must take these facts into account, selecting and highlighting essential information, eliminating distracting clutter, and conveying ideas that are not inherently visual, such as transformations or causality, through visual channels. Although there are tools and methods for designing effective visualizations, too many naive designers fail to use them, and their failure results in poor visualizations.

While many areas of computer science aim to replace human judgment with automation, visualization systems are explicitly designed not to replace the human but to keep the human in the loop by extending human capabilities. The user is an active participant, interaction is common and flexible, and the process of exploration using visual display and interaction happens in many different ways throughout a complex process.

The invention of abstractions, models, and mechanisms to explain the world around us is an inherently human endeavor. Ultimately, the practice of visualization should assist in the generation, evaluation, and exploration of hypotheses about the information under study, allowing the rapid consideration and possible rejection of old hypotheses and facilitating the creation of new hypotheses. Visualization leverages a combination of imagination, computer tools, and interactive interfaces to extend the power of human insight to aid in the discovery and synthesis of truth.

During the 17 years since the last NSF Visualization Report, the world has experienced an “information big bang,” an exponential explosion of data. New data produced in the two years since 2003 exceeds the information contained in all previously created documents. Of all this new data produced since 2003, more than 90% takes digital form, vastly exceeding information produced in paper and film forms. This growth in data does not necessarily mean a corresponding proportional increase in useful information. Raw data is, in and of itself, of questionable value. We are continually challenged to make sense of the enormous growth and onslaught of information and use it in effective and efficient ways. The 1971 observations of Nobel Prize winning economist, Herbert Simon, are more true now than ever:

What information consumes is rather obvious: it consumes the attention of its recipients. Hence a wealth of information creates a poverty of attention, and a need to allocate that attention efficiently among the overabundance of information sources that might consume it.

Among the greatest scientific challenges of the 21st century, then, is to effectively understand and make use of the vast amount of information being produced. Our primary problem is no longer acquiring sufficient information, but rather making use of it.  By its very nature, visualization addresses the challenges created by such excess – too many data points, too many variables, too many timesteps, and too many potential explanations. Visualization harnesses the human perceptual and cognitive systems to tackle this abundance. Thus, as we work to tame the accelerating information explosion and employ it to advance scientific, biomedical, and engineering research, defense and national security, and industrial innovation, visualization will be among our most important tools.

Moving Beyond Moore’s Law

Loosely interpreted, Moore’s Law is now taken to mean that processing power will double every couple of years without impact on cost. The beauty of Moore’s Law is that certain problems will solve themselves if we just wait. Many extremely important areas of visualization research tackle problems not governed by Moore’s law. Advances in these areas can yield new capabilities, new visions, new applications, and a firmer theoretical basis for visualization research and practice.

Collaborating with Application Domains

To achieve greater penetration of visualization into application domains we must better integrate visualization capabilities with the requirements and environments of these domains. To achieve this integration, we must allow application goals, domain knowledge, and domain-specific conventions and metaphors to shape visualization methods. Visualization methods must address the characteristics of real, rather than ideal, data, addressing among others the challenges of heterogeneity, change over time, error and uncertainty, very large scale, and data provenance.

Integrating with Other Methodologies

Visualization is rarely a stand-alone process: visualization is often necessary but not sufficient for solving problems. Visualization tools and methods should provide tighter integration with other analytic tools and techniques, such as statistics, data mining, and image processing, in order to facilitate analysis from both qualitative and quantitative perspectives. The newly-coined term Visual Analytics is a good example of an explicitly cross-disciplinary approach.

Examining Why and How Visualizations Work

Human perceptual and cognitive capacities are largely fixed, not subject to Moore’s Law. Even our understanding of these capacities grows slowly rather than doubling in a matter of years. Addressing the human element in visualization may require not simply making the system faster, but rather making the system different in order to better leverage human characteristics, strengths, and limitations. To this end, visualization research must actively seek to identify perceptual and cognitive influences on visualization effectiveness in order for visual displays to best augment human reasoning. Many current design principles of visualization are based on the century of work characterizing human psychophysical responses to low-level visual stimuli. We would benefit immensely from a more thorough understanding of higher level phenomena such as spatial memory and environmental cognition. We can furthermore distinguish between the noun visualization, which refers to a display showing visual information, and the verb to visualize, which refers to the process of how a human uses that display. We need to identify more accurately when, why, and how visualization provides insight to enable analytic thinking and decision making in a world of changing data sources, input and display devices, and user needs.

Designing Interaction Research in new interaction techniques will allow users to interactively manipulate and explore data and extract meaning from it. Fluid interaction requires that we create user interfaces that are less visible to the user, create fewer disruptive distractions, and allow faster interaction without sacrificing robustness. In addition to developing novel interaction metaphors, future visualization interfaces will need to respond to rapid innovation in visual display technology that is resulting in a range of hardware devices quite different from the current standard, including high resolution and lightweight projectors, flat panel displays, and touch-sensitive display surfaces. Haptic and tactile devices for both input and output are becoming commercially available, along with embedded and wireless technologies that make computing power ubiquitous. The challenge will be to characterize the strengths and weaknesses of these new kinds of hardware when they are used to support visualization for both single-user and collaborative systems.

Determining Success

As with all computer disciplines, visualization occasionally makes ground-breaking and innovative advances that provide obvious advantages and orders of magnitudes of improvement over previous techniques. More often, however, we must quantify advances and measure improvement through benchmarks and carefully designed evaluation studies. Evaluation allows a researcher to answer the question “Did this technique actually help human users solve their targeted problems?” or “How much does this new approach improve the confidence or accuracy of human insight?” To effectively answer these questions, a visualization researcher must have an active connection with a domain researcher with a driving problem, providing context for the measured improvements. The very act of measuring the performance and value of a visualization helps to guide the field and help it grow.

The field of visualization has unique evaluation challenges. While we can quantitatively measure the time and memory performance of an algorithm, such metrics do not shed light on the ultimate measure: human insight gained by computation or visualization.

We do have some methods for determining whether or not a visualization tool has helped a person solve a problem. A quantitative user study performed in a formal laboratory setting can measure the performance of users on an abstracted task using metrics such as task completion times or error rates. The human-computer interaction and psychology communities teach sound study design and statistical analysis in order to ensure good methodologies and accurate results.

However, there are also many ways to qualitatively evaluate systems. Anecdotal evidence from satisfied real-world users that a visualization system is helpful can be useful in demonstrating that the system has succeeded in its design goal. These anecdotes include accounts of “eureka moments” in which something previously unknown was discovered. The size of the user community can also demonstrate a system’s usefulness, because voluntary adoption reflects a judgment from the users that a visualization tool is effective. Indeed, a powerful measure of success is provided when visualization tools become so pervasively deployed in an application domain that their use is considered unremarkable. Qualitative user studies, ranging from ethnographic analysis of target user work practices to longitudinal field studies to informal usability evaluation of a prototype system, also play an important role in both design and evaluation.

Finally, an analysis that relates design choices to a conceptual framework is a powerful evaluation method. Measuring the effectiveness of the design of a visualization requires the use of case studies, in which design choices are discussed and justified in the context of the theoretical foundations of the research field. The outgrowth of these studies is the ontological organization of visualization itself, organizing the very structure, utility, and expressiveness of visual tools along guidelines and design principles. The resulting frameworks help us move beyond simply asking whether something helps by offering tools to answer questions of why and how it helps.

Too often, visualization is considered the last step of a research project, in which the visualization specialist is engaged to present the results of an experiment already completed. However, visualization can help to frame questions, to guide an investigation, and to develop intuitions and insight about the problem under study. In order to foster these capabilities and empower the field of visualization as an equal partner with domain experts in the exploration of science and society, we need to encourage the formalization of visualization design and the rigorous development of evaluation metrics. When improved formalizations and quantitative performance metrics are established for visualization, the field will more effectively assist research in almost all areas of human endeavor.

Supporting Repositories and Open Standards

One of the basic requirements of science is that experiments be repeatable. For visualization, this requirement entails sharing data, models, and tasks to verify and benchmark new algorithms and techniques, comparing them to the results of previous work.

Many of the arguments for open source software also hold for open science; that is, making the fruits of publicly funded science available to the community. Open data and task repositories are critical for continued progress in visualization. However, the difficulty is that visualization practitioners are typically not themselves the primary source of the data. We must depend on the willingness of those who generate the data to share it. Thus, we can and must be advocates for data sharing whenever possible. The visualization community must consider this advocacy, and the curation of visualization-oriented data and task repositories, as part of our own contribution to open science.

Designing and building systems that solve real-world problems is the best way to make significant progress in refining and adding rigor to both the techniques and the theoretical foundations of visualization. The iterative process of science is to make observations, construct theories to analyze and explain them, and continue the cycle by using the theory to guide the next set of observations. In visualization, we must build a working system before we can gather observations of its use. Building systems for real users with real tasks allows researchers to gather valid data and evaluate whether and how visualization techniques are effective for the intended task. These observations and explanations grounded in specific techniques create a foundation from which we can draw general theoretical conclusions about visualization. Another advantage of using real datasets is that researchers are then driven to create robust and scalable algorithms. Many visualization algorithms that work well for “toy” datasets do not scale to the large or noisy datasets of interest to real users.

(transitional (translational) research is a term that comes from the behavioural sciences - http://en.wikipedia.org/wiki/Translational_research - with the goal of reducing the barriers between basic research (long term, focused on large changes) and  applied research (short term, incremental improvements) through collaboration of multi-disciplinary teams.)

We must close the loop, accelerating the maturation of basic research into applied research. Visualization already functions as a crossroads connecting foundation research with applications, integrating the capacity of computational techniques to simulate and model natural and societal phenomena and to predict and report results. However, we must refine the precision with which we balance the resources in research support, working to promote visualization solutions to realworld problems by providing end-to-end approaches for growing new science into practical answers to hard and important questions.

National Infrastructure

One of the key emphases of the 1987 NSF Report was the need for a national infrastructure to enable visualization research and application. Many of the specific needs discussed have been satisfied in the intervening years, but others have remained a challenge.

Many of the hardware concerns from the original NSF report have been allayed by the passage of time and Moore’s Law. Processors with what used to be considered supercomputer-class power are now available in commodity desktop PCs that cost a few thousand dollars. Graphics performance that used to require special-purpose workstations costing tens or hundreds of thousands of dollars is now available as a commodity graphics card for desktop PCs that cost a few hundred dollars. The good news is that fast and cheap hardware aimed at the business and entertainment mass markets allows unprecedented access to computational and graphics power for visualization, a boon for both visualization researchers and end users. The flexibility of the latest generation of programmable graphics pipelines on these cards has sparked an explosion of sophisticated rendering techniques that are feasible in real time for the first time, which also benefits visualization users by providing real-time interaction when exploring large datasets.

In contrast, display technology improvements have historically lagged far behind the Moore’s Law curve. In the past 20 years, cathode ray tube (CRT) displays have little more than doubled in physical display size and resolution and have retained the same weight and form factor. In the past, our ability to design user interfaces has been constrained by fact that a monitor is a relatively heavy and expensive object. However, recent breakthroughs in flat panel and projector technology have broken the strangle-hold of the CRT. The combination of low cost, high resolution, and freedom from the weight and bulk constraints of CRTs will lead to an explosion of computer-driven displays in many new contexts, far beyond simply replacing the bulky CRT on a user’s desk with a sleek flat panel display that has a smaller footprint.

Pixels are currently a scarce resource. The primary limitation in interactive visualization interfaces is the number of available pixels: we are pixel-bound, not CPU-bound or even render-bound. High-resolution displays will allow us to investigate new and exciting parts of the interface design space as displays approach the resolution of paper. Large wall-sized displays with a resolution of dozens or even hundreds of megapixels can be created by tiling the output of many projectors. Although active surfaces will still be relatively expensive in the near term, a longer-term vision is that gigapixel displays will eventually be as cheap, lightweight, and ubiquitous as wallpaper. Physically large displays that encompass the entire field of view of an observer allow applications that use peripheral vision as well as the foveal vision that we use with medium-sized desktop displays. Small gadget displays will have the one megapixel resolution that we currently associate with desktop displays. Small handhelds have high availability because they can be carried around, and when networked can be used as control panels for a shared large display.


As of 2005, we have reaped vast benefits from the expansion and commoditization of the Internet. However, as the sizes of data sets continue to grow, it is still difficult, and sometimes prohibitive, to move large-scale data sets across even the fastest networks to visualize data locally. As such, there is a need for advances both in networking and in remote and collaborative visualization algorithms, such as view dependent algorithms, image based rendering, multiresolution techniques, importance based methods, and adaptive resource aware algorithms.

Visualization Software

The 1989 introduction of the AVS dataflow toolkit heralded the first generation of general-purpose software for visualization. Other systems of that generation include IBM’s DataExplorer, now the open-source OpenDX system; IRIS Explorer from SGI and then NAG; and the University of Wisconsin Vis5D/VisAD systems. The open-source VTK system (http://www.vtk.org/) stands out as the most widely used of the next generation of systems. Others currently in use include ParaView (http://www.paraview.org/) ,Amira (http://www.amira.com/), and the InfoVis Toolkit (http://ivtk.sourceforge.net/) with the continuing presence of OpenDX (http://www.opendx.org/) and AVS (http://www.avs.com/software/soft_t/mpe.html). Many packages that focus on application-specific needs have been developed, including Ensight, Fieldview, SCIRun, and ITK53.

The movement known as open source is the current incarnation of an idea that has been active for decades in the academic community, namely that there is great value in providing free software. One of the new aspects of the movement is formalizing the value of open source for industry as a business model. We note that there is bidirectional technical transfer with open-source software. In some cases, open-source government-funded research prototypes later evolve into commercial products. In other cases, commercial projects change to open source because the business model is more appealing.

There is a tradeoff between quickly creating a one-off prototype that suffices for a research paper but is too brittle to be used by anybody but the authors and devoting the time to create releasable code at the expense of making progress on the next research project. One of the benefits for researchers of releasing code to a user community is that real-world use typically spawns new research challenges strongly tied to real problems. Such ties are extremely important for our field as it matures. One benefit of the open-source model is that the user community itself sometimes takes over some or all of the support burden. Releasing software does not have to be a gargantuan task; often people who find that a particular piece of research software closely matches their needs are happy to use software that is less polished than a commercial product.

The VTK system began as an open-source initiative within the General Electric Global Research division, and has rapidly moved into mainstream use in universities, national laboratories, and industrial research labs worldwide. It continues to accelerate development by providing reusable software, relieving programmers from reinventing necessary infrastructure. The spinoff company Kitware is built around an open-source business model, where customers can pay for support and customization while development of the free codebase continues. Similarly, ITK was an NIH open-source software initiative intended to support a worldwide community in image processing and data analysis. It is designed to interface openly with visualization platforms such as VTK and SCIRun. The University of Utah’s SCIRun visualization system has also made the move to open-source infrastructure software to ease its integration into public and private research.

Funding - where is support for this research coming from

papers in IEEE Visualization Conference 1998 - 2004 cited support:

papers in IEEE Information Visualization Symposium 1998 - 2004 cited support:

(industry figures include authors employed by in- dustry, even if no explicit acknowledgment of support is given)

Coming Next Time

Visualization reports from the 00s, Part II

last revision 1/31/11