Promoting Insight Based Evaluation of Visualizations:

From Contest to Benchmark Repository


Catherine Plaisant
Human-Computer Interaction Lab.

University of Maryland

Jean-Daniel Fekete


Université Paris-Sud

Georges Grinstein

Institute for Vis. & Perception Research

University of Massachusetts Lowell



Information Visualization (InfoVis) is now an accepted and growing field with numerous visualization components used in many applications. However, questions about the potential uses and maturity of novel visualizations remain. Usability studies and controlled experiments are helpful but generalization is difficult. We believe that the systematic development of benchmarks will facilitate the comparison of techniques and help identify their strengths under different conditions.  A benchmark typically consists of a dataset, a list of tasks, and a list of non-trivial discoveries. We were each involved in the organization of three information visualization contests for the 2003, 2004 and 2005 IEEE Information Visualization Symposia.  Our goal is to encourage the development of benchmarks, push the forefront of the InfoVis field by making difficult problems available, create a forum for the discussion of evaluation and provide an interesting event at the InfoVis conference.  The materials produced by the contests are archived in the Information Visualization Benchmark Repository.  We review the state of the art and challenges of evaluation in InfoVis, describe the three contests, summarize their results, discuss outcomes and lessons learned, and conjecture the future of visualization contests.


General Terms

Visualization, information, competition, contest, benchmark, repository, measure, metrics


1            INTRODUCTION


Information Visualization is now an accepted and growing field with numerous visualization components used in mainstream applications such as SPSS/SigmaPlot, SAS/GRAPH, and DataDesk, in commercial products such as Spotfire, Inxight, HumanIT, and ILOG JViews, and in domain specific standalone applications such as interactive financial visualizations [SMo06] and election data maps [NYT06].  Nevertheless, questions remain about the potential uses of these novel techniques, their maturity and their limitations.


Plaisant reviewed evaluation challenges specific to information visualization and proposed initial steps [Pla04] such as refined evaluation methodologies, use of toolkits, dissemination of success stories, and the development of contests (Figure 1), benchmarks and repositories, the focus of this paper.


Figure 1: A collage of sample screens from the InfoVis 2004 contest illustrating the diversity of visualization methods used to address a task. 

Empirical user studies are very helpful but take significant time and resources, and are sometimes found of limited use because they have been conducted with ad-hoc data and tasks in constrained laboratory situations.  Benchmarks facilitate the comparison of different techniques and encourage researchers to work on challenging problems. However to be convincing, the utility of new techniques needs to be demonstrated in a real setting, within a given application domain and set of users.  Contests attempt to create surrogate situations that are representative of real world situations. They engage teams’ competitive spirit to produce materials that can help the community compare visualization tools applied to the same problem.


Competitions help push the forefront of a field quickly. In some cases it is simply the emotional aspect of winning or the excitement of live competition that compels researchers to participate. TREC competitions (the Text REtrieval Conference) [Voo00] exemplify the best of these in being able to bring in many corporate and academic research groups.


A contest poses a problem that many will attempt to solve. If the problem is challenging and representative of a real world situation, then the solutions proposed by contestants provide insights into what techniques are possible, and which ones are potentially better to pursue. Often these solutions provide such good results that other participants are driven to compete in the next year’s contests. The current contest data sets and tasks become part of the baseline against which new techniques can be tested.  These contest submissions then describe the insights found with various tools, illustrate the current state of the art and entice researchers to find even better solutions.


In this paper we review the state of the art and challenges of evaluation in the field of information visualization, describe the three contests, summarize their results, discuss the outcome of those three events, and conjecture the future of visualization contests.


2           State of the art of Information visualization evaluation


Information visualization systems can be very complex [Chen00] and require evaluation efforts targeted at different levels. One approach described in the Visual Analytics research agenda [Tho05] is focused on three levels: the component level, the system level, and the work environment level (Figure 2).

At the component level are the individual algorithms (e.g. clustering or linguistic analysis), visual representations, interactive techniques and interface designs.  Data analysis algorithms can typically be evaluated with metrics that can be observed or computed (e.g. speed, accuracy, sensitivity or scalability), while other components require empirical user evaluation to determine their benefits [Che00, Chen00b]. Metrics include effectiveness (e.g. time to complete simple tasks) and efficacy (e.g. number of errors or incomplete tasks).   There have been demonstrations of faster task completion, reduced error rates or increased user satisfaction measured in laboratory settings using some visualization components.  These studies are helpful to compare isolated interaction techniques or data representations e.g. [Ira03, Alo98]. Studies comparing slightly more complex tools combining a few components – at least a choice of interaction and visual representation - are also available, e.g. [Pla02, Kob04, Sta00]. They often reveal that different tools perform better for different types of tasks, but it is often difficult to pull apart what part of the system really impacts the performance of the tool.  Some limited techniques allow computed scores to be generated to evaluate the potential quality of simple displays, e.g.[Mac86] but controlled experiments remain the workhorse of evaluation.


Figure 2:  The 3 evaluation levels for Visual Analytics (Figure 6.1 in [Tho05])


At the system level, interfaces combine and integrate multiple components and need to be evaluated by comparing them with technology currently used by target users.  Metrics need to address the learnability and utility of the system.  Those evaluations may take place in the laboratory using surrogate scenarios but address complex tasks conducted over a longer period of time than component-level evaluations.  A new approach is to encourage insight-based evaluation.  The Infovis 2003-2006 contests are examples of efforts encouraging insight-based evaluation [Inf03, Inf04, Inf05, Inf06], and there are recent empirical studies which measure insight [Sar04]. 


At the work environment level evaluation addresses issues influencing adoption.  Metrics might include user satisfaction, trust and productivity. Case studies and ethnographic studies are used but they remain rare in the field of information visualization. Case studies report on users in natural environment doing real tasks [Gon03, Tra00]. They can describe discoveries, collaborations among users, the frustrations of data cleansing and the excitement of data exploration. They can report on frequency of use and benefits gained. The disadvantage is that they are very time consuming and may not be replicable or applicable to other domains.


Recently the Beliv’06 workshop [BEL06] provided a good overview of the most recent work on improving information evaluation, including the development of specific heuristics, metrics or taxonomies of tasks.   Of course usability evaluation remains a cornerstone of user-centered design and evaluation. It is of paramount importance for product engineering but also a powerful tool for researchers as it provides feedback on problems encountered by users and guides designers toward better designs at all three evaluation levels.  


2.1         Evaluation programs and efforts in other communities


Simple benchmark data sets abound.   Some repositories simply make data sets available (e.g. the Council of European Social Science Archives [CES06]) while others offer tools to help promote research in specific domaina (e.g. University of California Irvine Machine Learning Repository [MLR06]). Some repositories are clearly meant to promote evaluation (e.g. the Critical Assessment of Microarray Data Analysis or CAMDA, conference [CAM06]).  A few research communities have a long history of success at promoting large scale evaluation programs and methodologies for measuring the effectiveness of information technology.  For example TREC (the Text REtrieval Conference) [Voo00, TRE06] started in 1992 and provides datasets and scoring methods to evaluate information retrieval methods.  The Message Understand Conference (MUC) series [Chi93] was started in 1987 for the purpose of qualitative evaluation of the state of the art in message understanding and has contributed to the development of improved linguistically-based systems.  The speech recognition evaluation program [Pal00] has been creating speech corpora and benchmark tests primarily based on word error rates since 1988.  The earlier work provides baselines against which researchers could demonstrate the effectiveness of recognition algorithms.   Recent work has included the development of broadcast news transcription tasks to support topic detection and speech-to-text research.  Those sponsored evaluation programs are successful in part because clear computed metrics can be established to compare the results of tools with a trusted “ground truth”, even if sometimes the ground truth is generated by humans as it is for TREC where human experts decide the “true” relevance of documents.   Other volunteer efforts have also been useful. The KDD Cup is an example of benchmarking where the knowledge discovery community agrees on scores that can be computed on new datasets created every year [Geh04].    Recenly, the first Visual Analytics Science and Technology contest [VAS06] took place.   It used a synthetic dataset, so “ground truth” was known, facilitating the judges’ comparisons with the teams’ answers, and permitting experimentation with some quantitative metrics.  In the domain of Human-Computer Interaction, community contests are usually design competitions, one memorable exception being the 1997 CHI Browse-off [Mul97].  In parallel with the development of benchmarks, specific evaluation methods and tools for emerging new technologies are being designed, e.g. for collaborative environments [Sch05], ubiquitous computing [Sch02], or intelligence analysis [Cow05].


2.2         The challenge of information visualization evaluation


Although ideally one should be able to evaluate the quality of answers computationally this is often not possible. The problem is the fuzziness of answers to the contest: it may be a collection of articles, a new algorithm, or a new visualization, each of whose correctness may not be computable. This forces human evaluation (TREC still uses human judging for determining the accuracy of the retrieval.) and such is the case for the IEEE InfoVis Competitions. 


Another difficulty for information visualization comes from the impact of the discovery process, an extremely interactive and personal activity. Whereas computational algorithms can be compared through the accuracy of the results, most often it is not possible to accurately measure the results of visualization. We do not have measures of perceptual information transfer. There is beginning research in measures of interestingness and other metrics related to visualization [Kei95, Gri02], but these are in their infancy and too simple to be applied to the current contests.


We can identify simple tasks which yield precise results or we can specify exploratory tasks and thus have much less predictable results. This makes the evaluation process difficult to plan for and forces real time evaluation criteria which end up being reviewer dependent. Despite these constraints, one can still argue for simple tasks. A system which does not make it possible to achieve simple tasks would be a very limiting system and is likely not to support more complex or exploratory tasks. One can also argue that simple tasks are unrealistic. We tried to balance task simplicity vs. complexity to obtain a satisfying tradeoff.


Distinct challenges present themselves for evaluating at the component or system/work environment levels.

At the component level, the main challenge is to move beyond the proliferation of isolated evaluation to a more concerted effort to generate guidelines for selecting techniques based on the tasks and data characteristics.   A characteristic of the field of information visualization is the great diversity of approaches available to designers to handle any type of data and the combinatorial explosion of possible implementations.  Toolkits [Fek06] or even code repositories [Bor06] can help researchers control some of that diversity to adequately compare individual components.  In control studies, dataset and task selection has been until now an ad-hoc process making it difficult to compare results across studies.  This would be aided by the development of comprehensive task taxonomies and benchmark repositories of datasets, tasks and results.  Another problem is that studies generally include only simple tasks.  A literature survey [Kom04] confirms this fact by stating that experiments usually include locate and identify tasks, but that tasks requiring users to compare, associate, distinguish, rank, cluster, correlate or categorize have only been rarely covered.  Those studies are very difficult to design, and better experimental design training for researchers will greatly improve the outcome of evaluation efforts.


Another characteristic of visualization is that the analysis process is rarely an isolated short term process. Users may need to look at the same data from different perspectives and over a long time.  Users may also use analytics to answer questions about visible and non-visible patterns. They may also be able to formulate and answer questions they didn’t anticipate having before looking at the visualization.  This is in contrast with typical empirical studies techniques which recruits subjects for a short time to work on imposed tasks.  Finally, discoveries can have a huge impact but they occur very rarely, or not at all, and are unlikely to be observed during a study.   Insight based studies as described in [Sar04] are one first step but new evaluations methods need to be devised to address this problem.


At the system level, evaluating information visualizations and their interfaces is a daunting challenge.   Success is difficult to quantify and utility measures are elusive.  Tasks become significantly more complex and difficult to emulate in a laboratory environment.  Working with realistic data is crucial but “ground truth” is not always available. Even when available the comparison of steps to results is often impossible. Users’ motivation and expertise greatly influences performance.  In traditional component level empirical studies the level of training of subjects is typically limited and subjects are not allowed to consult with colleagues or use outside sources as they normally would in their work environments.  Using domain experts will lead to more realistic results but individual differences between subjects should be controlled for results to be useful.  Trust is a particularly important aspect of Visual Analytics (VA) system evaluation.  It is challenging to measure while of paramount to user acceptance during product deployment. Discovery is seldom an instantaneous event, but requires studying and manipulating the data repetitively from multiple perspectives and possibly using multiple tools.  Facilitating the transfer of data between heterogeneous tools and keeping the history of the investigation might well be just as important for discovery as the functionalities of individual components.  Longitudinal studies may be more helpful but they are more difficult to conduct.  Measuring the impact of integrated components that require users to manipulate visual as well textual representations, use the web to find complementary information, integrate analytics and possibly spend hours brainstorming with colleagues remains a challenge.  Another challenge is that success may not be due to nor easily traceable back to the visualization.  For example an effective visualization used on a daily basis by an analyst may heighten their awareness of certain activities by allowing them to absorb and remember large amount of information effortlessly.  However it might be difficult or impossible to link later decisions to a particular tool as awareness is difficult to identify and measure, and decision-making uses information from diverse sources.  In fact, the introduction of visualization might even trigger changes in work practices, exacerbating the problem of identifying cause and effect.   Shneiderman and Plaisant have proposed to use Multidimensional In-depth Long-term Case Studies (MILCS) as a way to study and evaluate creativity tools such as visual analytics and information visualization [Shn06].


3           The Infovis Contests


3.1         The 1st contest – Infovis 2003


 The first contest took place in 2003 [Inf03] (Figure 3). We invited submissions of case studies on the use of information visualization for the analysis of tree structured data, and in particular to look at differences between pairs of similar trees.  

The first contest (2003) focused on both simple and higher level tasks; no exploratory tasks were given.  The simple tasks could thus be evaluated with traditional techniques such as controlled experiments but any exploratory tasks would have been more complex to evaluate.  The contest focused on tree comparisons where users were asked to explore differences between similar tree pairs. Three sets were selected from different application domains: phylogenies, taxonomies, and file systems. The data was provided in a standard format with a taxonomy of about forty general tasks in 11 categories.  Open-ended domain specific tasks were provided to guide the analysis. Even with specific data sets and tasks it was difficult to compare the various entries. The 2003 competition process and the limited participation reminded us of how important domain knowledge is for analysis. This was probably the leading argument for our set selection for the 2004 competition.  The other key problem was that the majority of the received materials focused on descriptions of system features and not on how users accomplished the tasks or the meaning of the results, making evaluation and comparisons difficult.  The 2005 competition process focused on easily understood data (technology companies) and proposed more open ended questions which could, it was believed, be more quickly evaluated. The size of the data set was still a problem but the time provided for the exploration probably more the limiting factor. The 2006 competition selected the US census data and established an extremely early start date with the goal of remedying the time constraint. All data, accepted entries and reviews are archived at the Information Visualization Benchmark Repository [BenchRep] and are reviewed in more detailed below.


3.1.1        The 2003 Data


There are hundreds of types of tree with varying characteristics.  In an effort to be representative of this diversity while remaining accessible for a contest we selected three very different examples.  Three pairs of datasets were provided in a simple XML format.

·   Phylogenies
Small binary trees (60 leaf nodes) with a link length attribute. No node attributes except their names.

·   Classifications
Very large trees (about 200,000 leaf nodes) with large fanouts. Three node attributes, all nominal. Labeling, search and showing results in context is important.  We allowed teams to work on a subset of the dataset (the "mammal" subtree) if they could not handle that many nodes.

·   File system and usage logs

The trees are large (about 70,000 leaf nodes). Many attributes, numerical and nominal. Changes between the two trees can be topological changes and attribute value changes. Data for 4 periods was available.


3.1.2        The 2003 tasks


We provided general tasks (about 40 tasks in 11 categories) and tasks specific to the selected datasets. General tasks were low level tasks commonly encountered while analyzing any tree data: topological tasks (e.g., which branch has the largest fan-out?), attribute based tasks (e.g., find nodes with high values of X), or comparison tasks (e.g., did any node or subtrees "move"?).  On the other hand the tasks specific to particular datasets included more broad goal-setting tasks (e.g., for the phylogenies,  what mapping between the two trees topologies could indicate co-evolution, and, maybe, the points where the two proteins were not co-evolving?) We made clear that it was acceptable not to work on all tasks and that partial answers were OK. We also clarified that we were not looking for a detailed result list (e.g., a list of deleted nodes for the task “what nodes where deleted”) but an illustration or demonstration of how the visualization helped find the answer.  General background information was provided about the data and tasks, which was particularly important for the phylogenies.


3.1.3        The 2003 Submissions


Teams had five months to prepare. The participants were required to submit the following materials:


          Two page summary

          Video illustrating the interactive techniques used

          Web page of accompanying information and

          Index page with team information


We received eight entries.  It was a small number but satisfactory for the first contest.


3.1.4        The 2003 Results


The first main finding was that the tasks and datasets were too complex for such a contest. Each tool addressed only a subset of the tasks and only for a subset of the datasets.  The phylogeny chosen required domain expertise hence was “real”, and even though it consisted of a small binary tree, it was not used, probably because the tasks were complex and required working with biologists.


The second main finding was that it was difficult to compare systems even with specific datasets and tasks. We had hoped to focus the attention of submitters on tasks and results (insights), but the majority of the materials received focused on descriptions of system features.  Little information was provided on how users could accomplish the tasks and what the results meant, making it very difficult for the judge to compare. The systems presented were extremely diverse, each using different approaches to visualize the data. 


There were three first-place entries. TreeJuxtaposer [Mun03] (Figure 3) submitted the most convincing description of how the tasks could be conducted and the results interpreted. Zoomology [Hon03] (Figure 4) demonstrated how a custom design for a single dataset could lead to a useful tool that addressed many of the tasks satisfactorily. InfoZoom [Spe00] (Figure 5) was the most surprising entry (Figure 5).  This tool was designed for manipulating tables and not trees. However the authors impressed the judges by showing that they could perform most of the tasks, find errors in the data and provide insights in the data.  The three second-place entries showed promise but provided less information to the judges on how the tasks were conducted and the meaning of the results. EVAT [Aub03] (Figure 6) demonstrated that powerful analytical tools complementing the visualization could assist users in accomplishing their tasks. Taxonote [Mor03] (Figure 7) demonstrated that labeling is an important issue making textual displays attractive. The Indiana University submission [She03] (Figure 8) illustrated the benefits of toolkits by quickly preparing an entry combining several tools, each accomplishing different tasks. 


All entries were given a chance to revise their materials after the contest. We required participants to fill a structured form with screenshots and explanations for each task. That information is archived in the Information Visualization Benchmark Repository [Bmr06].


Figure 3:  Treejuxtaposer


Figure 4: Zoomology


Figure 5: InfoZoom

Figure 6: EVAT


Figure 7: Taxonote


Figure 8:  A combination of tools - Indiana University

3.2         The InfoVis 2004 contest

The second competition coincided with the 10 year anniversary of the InfoVis Symposium. As the visualization of the history of a field of research is a problem interesting in itself, it naturally formed the core part of the contest. The key advantage of the topic is that it was familiar to all participants. The disadvantage was that the selected corpora was not readily available in a usable form.


3.2.1        The 2004 data


The set of all publications on a topic is too large a universal set of discourse for a competition. We first argued about which conferences or journals to include, then decided to limit the dataset to all the IEEE InfoVis Symposium papers and all of the articles used as reference in those papers. Metadata is rich for IEEE and ACM publications and unique keys available. 


Producing a clean file (metadata for the collection of documents) was a much bigger challenge than we had imagined. We first made an assumption that both the articles and the most important authors in information visualization would be referred by most of the articles published within the InfoVis symposium. Our look at the references initiating from articles published within InfoVis seemed to us at the same time focused on the field and complete. It would be unlikely that an important publication in information visualization would seldom be referenced by other articles.


This was partially correct but text metadata still yielded numerous ambiguities.  IEEE manages the InfoVis articles which are less curated than those of the ACM.  Much text metadata was non-unique (e.g., many-to-one names such as Smith, Smyth, Smithe, …).  Reference titles were too noisy and in many cases erroneous as text is handled by the ACM Digital Library as strings and numerical computations such as string comparisons are still weak.  Much curation on our end was necessary as references were noisy, sometimes missing, and even sometimes pointing to non-existing URLs.


We thus embarked on cleaning the data.  This was a complicated process, with multiple passes, and manually intensive, even with automatic reference extraction as we found no reasonable automatic system to suitably resolve the problems.  We manually extracted the articles from eight years of pdf files from the symposia available in the digital library.  We then semi-automatically retrieved the articles referenced in those papers again from the digital library.  We extracted those which existed when found and manually cleaned and unified the publications not included in ACM library.


The result was a file containing 614 descriptions of articles published between 1974 and 2004 by 1,036 authors, referencing 8,502 publications.  It took well over 1,000 hours for us to construct that file, with over 30 people involved.


3.2.2        The 2004 tasks


We proposed 4 high level tasks with a great deal of flexibility for a variety of solutions:

1. Create a static visualization showing an overview of the 10 years of InfoVis

2.   Characterize the research areas and their evolution

3. The People in InfoVis: where does a particular author/researcher fit within the research areas defined in task 2?

4. The People in InfoVis: what if any, are the relationships between two or more researchers?


We suggested particular names for task 3 to facilitate comparisons between submissions, and participants used them, along with other names.


3.2.3        The 2004 submissions


The participants were required to submit

          A two page summary

          A video illustrating the interactive techniques used

          A structured web form providing details as to how the tasks were accomplished and what discovery or insights were identified

There were 18 submissions from 6 countries (USA, Canada, Germany, France, Australia, Netherlands) with 7 academic participants. 12 teams were selected to have their results published in the InfoVis repository.  Four received a first place prize and presented at the conference. Six teams received a second place prize and presented a poster.


3.2.4         The 2004 results


Quality improved dramatically between 2003 and 2004.  The good news was that most teams had provided a lot more insights than we had seen in the 1st contest.  Still, some teams had tools that seem promising “on paper” but reported very few insights (in consequence they did not do very well in the contest.)  On the other hand some teams presented tools that seemed of doubtful utility to the reviewers at first but were able to report useful insights, therefore fairing better than we had expected in the results.   Of course the best teams had everything at once:  promising visualizations, lots of insight reported, and convincing explanations of how the insights were obtained using the tools.


None of the 12 selected teams answered all the questions. A few of the participants had extensive experience with text analysis and that was visible in their results.  Other had background knowledge of the InfoVis community and could provide better hypothesis about what they were seeing.  One tool was developed entirely from scratch for the contest but most teams showed interesting new uses of existing techniques. Node Link diagrams were a very commonly used representation for many of the tasks, with some notable exceptions.


This second contest had a single dataset and simpler tasks so we thought reviewing and comparing results would be much easier.  Not so. Ideally one would be able to evaluate the quality of answers computationally but this was not possible. The problem was the fuzziness of the answers and the lack of “ground truth” or even consensus on what the best answer might look like. Teams’ answers took various forms: from a collection of articles or names to a new algorithm to a new visualization, all of whose correctness was not computable. Only human evaluation was appropriate to judge the validity of the answers. In information retrieval, TREC for example does uses human judging for determining the relevance of documents (i.e. the answers) from which metrics can be computed for a team’s set of results. Short of spending time with the team throughout the discovery process (an extremely interactive and personal activity) we could only base our judgment on the materials provided (video and Web form).


There were three 1st place entries and one student 1st place:

·   The entry from Indiana University [Wei04] had many insights gathered from a variety of mostly low tech displays such as barcharts, displaying the output of separate analysis tools.  The best display showed a simplified view of the coauthorship network clearly highlighting the main players in the field of InfoVis (as represented by the dataset) (Figure 9).  It has since then been used by many as an overview of the community. The entry from the Pacific Northwest National Laboratory [Won04] nicely reflected the extensive experience of the team and the rich set of analysis features provided by their tools (Figure 10).

·   The entry from Microsoft and Maryland [Lee04] was interesting because it took a completely different approach to the problem, departing from node-link diagrams, and using multiple tightly coupled ordered lists to represent the interconnections between authors, papers and references (Figure 11).

·   The student 1st prize went to a team from the University of Sydney [Ahm04].  It surprised us with its whimsical 3D animation and provided sufficient insight to convince us that the technique had some merit (Figure 12).


Second place prizes (see Figure 13 to 19) went to the Université de Bordeaux I with the University of British Columbia [Del04], the Technische Universiteit Eindhoven [Ham04], Georgia Institute of Technology [Hsu04] and [Tym04], the University of Konstanz [Kei04], the two teams from Drexel University [Lin04] and [Chen04], and the University of California, Davis [Teo04]. Each has some interesting technical component and some interesting insights.

We were satisfied that teams reported useful insights but we were still surprised by how few were reported, and even fewer really surprising insights. Insights about the whole structure were rare and only came from teams who had experience looking at other domains (e.g., the fact that InfoVis is a small world, tightly connected, was mentioned only by 2 teams.)  One team noticed that the most referenced papers were published at CHI, not at InfoVis.  Only three teams noticed the existence of references to future papers, a problem resulting from automatically processing references and confusing multiple versions with similar titles such as a video and a paper. Only one insight dealt with something that was surprisingly missing namely that there were no papers in the dataset from several of other competing InfoVis conferences, despite the fact that they had been held for several years.

Teams interpreted the tasks and used the data in surprisingly very different ways. A task such as “describe the relationships between authors” was interpreted in at least the following nine different ways as report on co-authorship; or co-citation; or people working on similar topics; or having a similar number of co-authors; or being a part of big groups or teams;  or having a similar number of publications; or a similar number of references; or working in the same institution; or working in the same clique or empire.

Teams also used the data differently: they created displays showing either only the IEEE Infovis symposium papers or all papers including the references.  Sometimes they combined both authors and topics and sometimes they used separate displays.  In one case we suspected that a team used only the papers first authora but could not tell for sure.    One team only used references from InfoVis papers references but not references to papers from other venues.  The data made visible was generally pruned dramatically to work with the tools or to create more useful or possibly appealing displays. Few ever attempted to show complete views.  Some teams had a “celebrity” approach ignoring everything but the star papers or authors based on some unique criteria (e.g., numbers of citations).  Some clustered first then pruned later with no clear explanation of what has been pruned.

Reviewing the displays seemed easy at first, but it quickly became impossible to remember what data we had just been looking at, not mentioning trying to compare results even when it would have been possible.   Many displays had no legends or very poor legends and none had any summary of the process that generated the display.  Each team probably had a clear model of the scope of the data and how it was filtered, aggregated and interpreted, but the displays did not reflect that.

Few teams even attempted to answer question 1, to create a static visualization showing an overview of the 10 years of InfoVis.  Teams merely reused one of the screen shots from other tasks so we felt only one aspect of the data had been portreyed and not the entire 10 years of InfoVis. Teams reported very different topics and different numbers of topics (from 5 to 12) and some created topics on the fly, refining the topics iteratively.  Sometimes a seemingly narrow topic would take a prominent place: “parallel coordinates” was a major topic in one case while in another system “taxonomy” was a major topic.  Reviewing all the submissions gave us an impression of randomness in the choice or labeling of the topics.  One of the student team used their professor’s notes to extract topics.  It was innovative but again, affected our ability to making comparisons.  Most visualizations limited the total number of topics which limited the insights to be related to those topics.  But topic extractions were not the focus of the contest so we did not judge the quality of the topics. Nevertheless this made it more difficult to compare insights.  Some tools (e. g., In-spire [Won04] and an entry from Rutgers [Lin04]) allowed users to refine the topics quickly and iteratively by entering a seed term or removing words. Some teams chose to remove common words (e.g., Information Visualization) while others kept them making `these labels for major topics which was not very useful. Sadly, evaluation only appeared once as a topic, and only after iterative refinement.

Overall, labeling remained a very big issue. Very rarely could we actually guess paper titles when looking at a display.  Better dynamic layout techniques for labels were clearly needed.  Labels for papers usually consisted of the first few words or even just the first author making it difficult to remember if we were looking at author relationships, or papers, or even topic relationships, e.g. a large node labeled “Johnson” could represent the often-referenced Treemap paper.

Some tools had only one window [Teo04], but most used multiple windows, showing either variants [Ham04] or very different displays for different tasks [Chen04], [Kei04]. The Paperlens submission [Lee04] illustrated the importance of coordinating views.  Only two teams dealt with missing data and uncertainty, others ignored the problem entirely.  Visual metaphors seemed to have had an effect on the words teams used to describe their findings, e.g. one team [Ahm04] talked about empires when looking at towers in 3D, while others talked about cliques while looking at clusters on node link diagrams. Unfortunately, we also saw examples of “nice pictures” that didn’t seem to lead to any insight.

The 2004 contest session at the workshop was very well attended and we received extremely positive feedback.  Attendees reported being able to appreciate the wide diversity of solutions and contrast the different techniques.  We conjecture that the topic we had selected also made the contest more accessible.

Figure 9: Link diagram, from Indiana University [Wei04]


Figure 10  In-Spire clusters from the Pacific Northwest National Laboratory [Won04]


Figure 11  PaperLens distributions from Microsoft and the University of Maryland [Lee04]


Figure 12:  Wilmascope topic flows from the University of Sydney [Ahm04]

Figure 13: Document graphs from the U. de Bordeaux I and the University of British Columbia [Del04]


Figure 14: Link diagram from the Technische Universiteit Eindhoven [Ham04]


Figure 15: Topical overview and focus from Georgia Institute of Technology [Hsu04]


Figure 16: Document timeline and classes from the University of Konstanz [Kei04]



Figure 17: Topic classification from Drexel University [Lin04]


Figure 18: Author link diagram from Drexel University [Che04]



Figure 19: Topic and author timeline from the University of California, Davis [Teo04].


3.3         The InfoVis 2005 contest


In the third competition [Inf05] the chairs aimed for the evaluation of more complete visualization systems and a different type of data.  The data set was larger and the questions more targeted. The goal was to identify how well visualization or visual analytics systems or even specific tools could perform with a large but easily understood data set. The chairs missed a key point in that the problem was probably better phrased as a GIS challenge rather than simply an information visualization one. The chairs also released the data set only for the competition. The owner of the data set did not permit an open release, something that the chairs tried to avoid and hopefully will avoid in the future.


3.3.1        The 2005 data


We selected a large, information rich, and real data set. The data consisted of information on about 87,659 technology companies in the US, including year founded, zip code, yearly sales, yearly employment information, along with industry and product information using the North American Industry Classification System [Nai01]. This was a large data set with geographic interpretation, one which pushed the limits of many systems.  The data was cleaned by graduate students at the University of Massachusetts at Lowell. Missing data was eliminated as best as possible.



3.3.2        The 2005 Tasks

The three questions related to the characterization of correlations or other patterns amongst variables in the data were

1.       Characterize correlations or other patterns among two or more variables in the data.

2.       Characterize clusters of products, industries, sales, regions, and/or companies.

3.       Characterize unusual products, sales, regions, or companies.


One additional question was more general and open-ended 
4.  Characterize any other trend, pattern, or structure that may be of interest.

The chairs felt that these precise questions would make evaluation simpler. And again this was not correct as all questions were open-ended and comparing the discovery of different correlations was difficult.

3.3.3        The 2005 Submissions


The participants were required to submit materials using the format as in 2004. There were only 10 participants. This was a surprise but the short time from available data to submission deadline was probably the most important factor.  We had no submissions from student teams possibly because we released the first version of the data set at the end of February during which most university information visualization classes already are well under way.


3.3.4         The 2005 Results

The chairs managed the review process and evaluated the entries in a similar manner as the previous year, but used specific ratings for insight, presentation, interaction, creativity, flexibility, and novelty.  There were two first and two second place awards. Teams led by the Iowa State University [Hof05] and Penn State University [Che05] took first place having answered all questions, while the Universität Karlsruhe [Hos05] and Augsburg University [Zei05] provided strong answers and received second place prizes.

The first place winners took two different approaches. The team from Iowa State University provided a classic information visualization approach and highlighted the important role of the analyst [Hei05]. Their system did not have high end visualizations but their analysis led to answers for all questions (Figure 20). Their focus on boom and bust was right on target and very elegant. The Penn State University and University of South Carolina team looked at the problem from the point of view of a GIS time-based system and used many of their map visualization tools to explore cross-state patterns (Figure 21) [Jin05]. Their system was the most novel with superb interaction. This of course highlighted the duality nature of the data set. One could look at it strictly from the information visualization point of view or from the GIS point of view and thereby harness a great deal of knowledge and techniques from the mapping and geospacial fields.

The second place winners had strong answers. The Company Positioning System from the Universität Karlsruhe was visually stimulating, and had high scores on interaction and novelty (Figure 22) [Hos05] while the team from Augsburg University used interactive statistical graphics to derive and present their answers (Figure 23) [Zei05]. Their interactive solutions were insightful.


Figure 20: Iowa State University [Hof05]
   Figure 21: Penn State University [Che05]

Figure 22:  Company Positioning System by the Universität Karlsruhe

Figure 23:  Augsburg University

All in all the four winners covered a broad spectrum of techniques for information visualization solutions. In all cases we found that statistical analysis played a key role. The data was just too large for simple human consumption thereby putting visualization in a collaborating role with analysis. Much processing of the data took place and all contestants used coordinated views to answer the questions.


3.4         Lessons Learned from the three contests


3.4.1        About the evaluation process


The contest illustrated the difficulty researchers have at giving adequate evidence that their tools could effectively conduct the tasks.  Demonstrating the power of a tool can be difficult.  Researchers are trained to describe their tools’ novel features more than illustrating them with convincing examples using real data.  In 2003 participants barely reported any insight at all.  Everyone was focused on the description of their tool.  By 2004 more participants (not all) were able to provide insights. In 2005 insights were more common.


Half of the participants were students who built their tools.  These tools were not as polished as industrial products or well developed research systems.  In 2003, we provided large data sets with some meaningful subsets and in 2004 the data set was not very large.  However, in 2005, there was no subset provided and the number of participating students dropped.  Providing benchmarks that fit student project's sizes seems important to the success of future contests.


The evaluation process is time consuming and looked at by some of the chairs as a daunting task.  Ideally one could compute metrics and add these up assuming independence to get a summary score, but since the questions are open-ended and there is no known “ground truth” it requires human evaluation.  Even though there were a limited a number of specific tasks, these can be interpreted differently and of course participants did interpret them in many ways. We seem to end up having to compare non-comparable steps and results.


Evaluating the results remained a subjective activity. After seeing the submissions, the 2003 and 2004 contest reviewers decided to classify the teams in three categories: no evidence of insights gathered using the tool, some insight, and lots of insight, i.e. worthy of a first prize.  The 2005 contest used more than 6 categories. This helped with discussions but required a great deal more detailed reviewing of the submitted videos and papers.


Since the data is the same, repeatedly looking at entries is taxing.  It can be very hard to remember “who did what” or “who had this insight”.   An insight implies novelty of the finding, so reviewers might more positively weigh a reported insight the first time they encounter it and undervalue it later on when reported by another team. Videos were extremely important. Without them it would have made it impossible to understand how most tools worked and what process was used to answer the questions. With videos interactions become understandable.  Verbal comments on the videos were indispensable in explaining what the participants were highlighting.  This is quite different than simply reviewing a paper and ranking the results.  Labeling of the results was not as necessary as verbal description on the videos were sufficient for the descriptions but this required the reviewers to remember key points.  On the downside, dealing with videos was very time consuming.  Videos were large, download times high, and distribution to reviewers slow (something we now know how to resolve).  For the first two contests we were flexible about the format of the videos submitted but this created problems such as finding converters or hunting for missing codecs.  For the 2005 contest we required a single format and this simplified the process.


3.4.2        Data preparation


With the InfoVis 2003 contest we attempted to provide real data and tasks while trying to narrow the problem to one data type (trees) and three representative tree types.  The contest taught us that the problem was still too large for a contest and that the vague nature of the tasks made it impossible to compare answers effectively.  In contrast the 2004 contest had only one dataset, much fewer tasks and a more structured reporting format.  Nevertheless, the open-ended nature of realistic tasks and the diversity of approaches still made judging the submitted entries a challenge. 

We felt that the time to generate a reasonably clean data set was too large, around 1000 man-hours each year. This is a serious issue for the development of benchmarks.  Domain experts should be solicited for cleanup and experimentation on various task solutions should be attempted before the data is released.  We hope that industry groups or government agencies wishing to see more research conducted on specific data of interest to them will take on the burden of developing the benchmarks datasets or support groups to do so.  

3.4.3        Motivation of participants


Participating in the contest takes time and motivation.  Most participants reported working very hard to prepare their submission.  Many acknowledged that it pushed them to improve and test their tools.   Some students were encouraged to work on the contest as a class project.  Some wanted to test their PhD research.  A small company reported appreciating the exposure.


In the 3 contests we gave small prizes to the first and second place teams.  Sponsors provided various prizes.  Those were appreciated, especially by the students who liked the gaming stations.  We also presented winners with certificates that many told us they were happy to hang in their office.  Participants appreciated being able to be able to mention the award and the small publication to their resumes.  On the other hand, some tenure-track faculty reported being interested but preferring focusing on writing full papers.


We realized that we should in the future anticipate the data set and plan earlier.  Given that we ran into errors and noise in almost all the data sets, having more time will help clean the data and prepare better tasks.  The 2006 contest selected the census data and made the announcement at the 2005 conference thereby providing potential participants a great deal of time, almost a year, to work on the problem.  Pushing the deadline further into the late summer would allow summer interns to work on the contest, but would reduces the reviewing period dramatically.


Many people downloaded the dataset without submitting results and we collected names and emails. The chairs performed an informal survey of those that had downloaded the 2005 contest to see why there were so few participants. The participants stated that there were no problems with the data set or questions, that the data set was a great data set to show system and tool capabilities, and that all had enjoyed the process and would do it again. Most expressed that they wanted a better organized website, automated email on data or news updates, and would have preferred the data in a database format. Some expressed strong interest in splitting entries into commercial and academic categories. Four of the non-participants stated that the requirement to attend the conference hindered their participation and most expressed that they were too busy in their company to tackle such a project. Several expressed a desire for some mini-questions such as “find a more elegant way to look at …”.


There was one recurring theme which all participants and non-participants expressed and that was the need for more time. That was the reason the 2006 contest data was made available at the 2005 conference.


3.4.4        Running a successful event at the conference


For all three contests we were able to have a whole session at the conference to summarize the results and allow some authors to present.  In 2003 only the first place authors presented and we summarized the second place submissions.  Attendees commented that it would be better to have shorter presentations but allow more presenters to speak.  The following year we arranged for all first and second place winners to present with the second place ones having only 2 minutes.   This format was very well received.  We specified tasks presenters should focus on so the attendees could better compare the different entries, at the cost of not seeing every feature of the tools.  We found that handing out the awards rapidly and keeping photos to a minimum (a group picture at the end of the session) was preferable.  This left more time for the presentations and still gave a festive atmosphere to the event.  All winners were also given a chance to have a poster displayed during the normal poster session.


3.5         Impact

A contest is only a first step.  The revised materials provided by the authors and the datasets have to be available after the event.  We have strived to keep the contest pages active and we also have made the submissions available in the InfoVis repository hosted at the University of Maryland [BMR06]. We encourage information visualization researchers and application developers to continue using the datasets and tasks.  We hope that others will provide the results of their analysis to the repository thereby enriching it and providing a more comprehensive review of visualization techniques available. This has not happened so far but this is likely due to the lack of advertising and promoting. There are some recent references in papers discussing the contest, the process and some references to the data sets.


3.6         Conclusions


Contests represent an artificial testing situation where the opinion of judges reflects the quality of the submitted materials, as opposed to the actual merits exhibited when tools are tested interactively and discussed with designers.  The impact of contests is most obvious with those that participate and those that see the results but the datasets and tasks remain available after the contests thereby extending their impact. They can be used by developers to exercise their tools and identify missing features, and by evaluators to enrich their testing procedures with complex tasks. These developers and evaluators then have baseline results with which to compare their results. We hope that these data can also be used in controlled experiments, and that the more specific lists of tasks used for those experiment can be added to the repository for reuse.


Benchmarks are difficult to create, promote and use. Our belief is that we are developing solid and evolving benchmarks and are beginning to understand how to better evaluate submissions.  Good benchmarks must be real (witness the success of TREC and CAMDA) to both draw the audiences and participants and to strongly push the technology curve. Good benchmark tasks must be open-ended to provide for the flexibility in solutions. We know that this makes the evaluations more difficult to measure analytically but this is realistic. We need to think that more human evaluation will be required in the future and evolve a collection of volunteer judges. These contests continue to demonstrate the challenges of benchmark design and especially of system and tools evaluation.


By making the results of analyses available to the community we provide a repository of baselines for developers to compare to.  Teams did interpret our tasks in many different ways, making comparison difficult; nevertheless we feel strongly that it was extremely useful to compare with the same data set and tasks.


The integration of analysis is becoming more necessary as data sets are more complex, large, and coming from diverse sources. The identification of anomalous patterns of data from phone calls, from bank transactions, and from news articles requires new techniques and strong analytical tools. We believe that such data sets and competitions will continue to encourage the community to work on difficult problems while building a baseline of comparable tasks and datasets.




We thank the organizers of the IEEE Visualization InfoVis Symposium, in particular John Dill, Tamara Munzner and Stephen Spencer, for their continual support, members of the InfoVis community for their intellectual stimulations, and the participants without whom there would be no contest. We would also like to thank the students for their help in extracting the metadata, cleansing the data set, and producing a richly usable data set: Caroline Appert (Université Paris-Sud, France) and Urska Cvek, Alexander Gee, Howie Goodell, Vivek Gupta, Christine Lawrence, Hongli Li, Mary Beth Smrtic, Min Yu and Jianping Zhou (University of Massachusetts at Lowell).


We thank the sponsors who provided first prizes, The Hive Group, ILOG and Stephen North personally.  After the first release of the datasets many others offered their help, including Jeff Klingner from Stanford, Kevin Stamper, Tzu-Wei Hsu, Dave McColgin, Chris Plaue, Jason Day, Bob Amar, Justin Godfrey and Lee Inman Farabaugh, from Georgia Tech, Niklas Elmqvist from Chalmers, Sweden, Jung-Rung Han, Chia-Ning Chiang and Tamara Munzner from UBC, and Maylis Delest from the Université de Bordeaux.


We thank ACM and IEEE and in particular Mark Mandelbaum and Bernard Rous for helping make the 2004 data available and working with us to prepare the dataset, Shabnam Tafreshi for help with the website, and finally but  not least Paolo Buono from the University of Bari, Italy, for participating in the review process.  We thank Michael Best for working with us on releasing the technology company data for the 2005 contest.


We also thank Sharon Laskowski for working with Catherine Plaisant on the evaluation section of the NVAC research agenda [Tho05] which helped refined some of the sections of this paper and lead to Figure 2.




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