Effective data visualizations enhance perception, support cognitive processing, and facilitate informed decision-making by aligning with human perceptual strengths. Conversely, poorly designed visualizations can impede comprehension, introduce interpretive bias, and diminish the perceived credibility of the conveyed message. This paper investigates the extent to which visual embellishments influence perceived message credibility in data visualizations. We conducted two crowdsourced experiments to examine both holistic and component-level effects of embellishment. In the first experiment, participants evaluated the relative credibility of plain bar charts versus two embellished variants-cartoon-style and image-style-across topics. Participants provided both comparative judgments and qualitative feedback. In the second experiment, we systematically isolated the influence of specific design elements-color, font, and bar style-on credibility perceptions through controlled variations. Our findings reveal that the impact of embellishments on perceived message credibility is complex and context-dependent. While certain embellishments, such as the use of color and image style bars, enhanced credibility, others-most notably hand-drawn fonts and cartoon-style bars-significantly undermined it. By operationalizing trust through the lens of message credibility, this work offers empirical insight into the design factors that shape viewers' perceptions. We conclude by proposing actionable design guidelines to support the creation of visualizations that are effective for communication and credible.

Large Language Models (LLMs) like GPT-4 which support multimodal input (i.e., prompts containing images in addition to text) have immense potential to advance visualization research. However, many questions exist about the visual capabilities of such models, including how well they can read and interpret visually represented data. In our work, we address this question by evaluating the GPT-4 multimodal LLM using a suite of task sets meant to assess the model's visualization literacy. The task sets are based on existing work in the visualization community addressing both automated chart question answering and human visualization literacy across multiple settings. Our assessment finds that GPT-4 can perform tasks such as recognizing trends and extreme values, and also demonstrates some understanding of visualization design best-practices. By contrast, GPT-4 struggles with simple value retrieval when not provided with the original dataset, lacks the ability to reliably distinguish between colors in charts, and occasionally suffers from hallucination and inconsistency. We conclude by reflecting on the model's strengths and weaknesses as well as the potential utility of models like GPT-4 for future visualization research. We also release all code, stimuli, and results for the task sets at the following link: https://doi.org/10.17605/OSF.IO/F39J6.

Application developers frequently augment their code to produce event logs of specific operations performed by their users. Subsequent analysis of these event logs can help provide insight about the users' behavior relative to its intended use. The analysis process typically includes both event organization and pattern discovery activities. However, most existing visual analytics systems for interaction log analysis excel at supporting pattern discovery and overlook the importance of flexible event organization. This omission limits the practical application of these systems. Therefore, we developed a novel visual analytics system called IntiVisor that implements the entire end-to-end interaction analysis approach. An evaluation of the system with interaction data from four visualization applications showed the value and importance of supporting event organization in interaction log analysis.

Visual augmentations are commonly added to charts and graphs in order to convey richer and more nuanced information about relationships in the data. However, many design spaces proposed for categorizing augmentations were defined in a top-down manner, based on expert heuristics or from surveys of published visualizations. Less well understood are user preferences and intuitions when designing augmentations. In this paper, we address the gap by conducting a design elicitation study, where study participants were asked to draw the different ways they would visually express the meaning of ten different prompts. We obtained 364 drawings from the study, and identified the emergent categories of augmentations used by participants. The contributions of this paper are: (i) a user-defined design space of visualization augmentations, (ii) a repository of hand drawn augmentations made by study participants, and (iii) a discussion of insights into participant considerations, and connections between our study and existing design guidelines.

Fact-checking data claims requires data evidence retrieval and analysis, which can become tedious and intractable when done manually. This work presents Aletheia, an automated fact-checking prototype designed to facilitate data claims verification and enhance data evidence communication. For verification, we utilize a pre-trained LLM to parse the semantics for evidence retrieval. To effectively communicate the data evidence, we design representations in two forms: data tables and visualizations, tailored to various data fact types. Additionally, we design interactions that showcase a real-world application of these techniques. We evaluate the performance of two core NLP tasks with a curated dataset comprising 400 data claims and compare the two representation forms regarding viewers’ assessment time, confidence, and preference via a user study with 20 participants. The evaluation offers insights into the feasibility and bottlenecks of using LLMs for data fact-checking tasks, potential advantages and disadvantages of using visualizations over data tables, and design recommendations for presenting data evidence.

Data visualization has the power to revolutionize sports. For example, the rise of shot maps has changed basketball strategy by visually illustrating where "good/bad" shots are taken from. As a result, professional basketball teams today take shots from very different positions on the court than they did 20 years ago. Although the shot map has transformed many facets of the game, there is still much room for improvement to support richer and more complex analytical tasks. More specifically, we believe that the lack of sufficient interactivity to support various analytical queries and the inability to visually compare differences across situations are significant limitations of current shot maps. To address these limitations and showcase new possibilities, we designed and developed HoopInSight, an interactive visualization system that centers around a novel spatial comparison visual technique, enhancing the capabilities of shot maps in basketball analytics. This article presents the system, with a focus on our proposed visual technique and its accompanying interactions, all designed to promote comparison of two different scenarios. Furthermore, we provide reflections on and a discussion of relevant issues, including considerations for designing spatial comparison techniques, the scalability and transferability of this approach, and the benefits and pitfalls of designing as domain experts.

Data visualization and journalism are deeply connected. From early infographics to recent data-driven storytelling, visualization has become an integrated part of contemporary journalism, primarily as a communication artifact to inform the general public. Data journalism, harnessing the power of data visualization, has emerged as a bridge between the growing volume of data and our society. Visualization research that centers around data storytelling has sought to understand and facilitate such journalistic endeavors. However, a recent metamorphosis in journalism has brought broader challenges and opportunities that extend beyond mere communication of data. We present this article to enhance our understanding of such transformations and thus broaden visualization research's scope and practical contribution to this evolving field. We first survey recent significant shifts, emerging challenges, and computational practices in journalism. We then summarize six roles of computing in journalism and their implications. Based on these implications, we provide propositions for visualization research concerning each role. Ultimately, by mapping the roles and propositions onto a proposed ecological model and contextualizing existing visualization research, we surface seven general topics and a series of research agendas that can guide future visualization research at this intersection.

Social media (i.e., Reddit) users are overloaded with people’s opinions when viewing discourses about divisive topics. Traditional user interfaces in such media present those opinions in a linear structure, which can limit users in viewing diverse social opinions at scale. Prior work has recognized this limitation, that the linear structure can reinforce biases, where a certain point of view becomes widespread simply because many viewers seem to believe it. This limitation can make it difficult for users to have a truly conversational mode of mediated discussion. Thus, when designing a user interface for viewing people’s opinions, we should consider ways to mitigate selective exposure to information and polarization of opinions. We conducted a needs-finding study with 11 Reddit users, who follow climate change threads and make posts and comments regularly. In the study, we aimed to understand key limitations in people viewing online controversial discourses and to extract design implications to address these problems. Our findings discuss potential future directions to address these problems.

Basketball writers and journalists report on the sport that millions of fans follow and love. However, the recent emergence of pervasive data about the sport and the growth of new forms of sports analytics is changing writers’ jobs. While these writers seek to leverage the data and analytics to create engaging, data-driven stories, they typically lack the technical background to perform analytics or efficiently explore data. We investigated and analyzed the work and context of basketball writers, interviewed nine stakeholders to understand the challenges from a holistic view. Based on what we learned, we designed and constructed two interactive visualization systems that support rapid and in-depth sports data exploration and sense-making to enhance their articles and reporting. We deployed the systems during the recent NBA playoffs to gather initial feedback. This article describes the visualization design study we conducted, the resulting visualization systems, and what we learned to potentially help basketball writers in the future.

Contact tracers assist in containing the spread of highly infectious diseases such as COVID-19 by engaging community members who receive a positive test result in order to identify close contacts. Many contact tracers rely on community member’s recall for those identifications, and face limitations such as unreliable memory. To investigate how technology can alleviate this challenge, we developed a visualization tool using de-identified location data sensed from campus WiFi and provided it to contact tracers during mock contact tracing calls. While the visualization allowed contact tracers to find and address inconsistencies due to gaps in community member’s memory, it also introduced inconsistencies such as false-positive and false-negative reports due to imperfect data, and information sharing hesitancy. We suggest design implications for technologies that can better highlight and inform contact tracers of potential areas of inconsistencies, and further present discussion on using imperfect data in decision making.

It has been widely suggested that a key goal of visualization systems is to assist decision making, but is this true? We conduct a critical investigation on whether the activity of decision making is indeed central to the visualization domain. By approaching decision making as a user task, we explore the degree to which decision tasks are evident in visualization research and user studies. Our analysis suggests that decision tasks are not commonly found in current visualization task taxonomies and that the visualization field has yet to leverage guidance from decision theory domains on how to study such tasks. We further found that the majority of visualizations addressing decision making were not evaluated based on their ability to assist decision tasks. Finally, to help expand the impact of visual analytics in organizational as well as casual decision making activities, we initiate a research agenda on how decision making assistance could be elevated throughout visualization research.

Root cause analysis is a common data analysis task. While question-answering systems enable people to easily articulate a why question (e.g., why students in Massachusetts have high ACT Math scores on average) and obtain an answer, these systems often produce questionable causal claims. To investigate how such claims might mislead users, we conducted two crowdsourced experiments to study the impact of showing different information on user perceptions of a question-answering system. We found that in a system that occasionally provided unreasonable responses, showing a scatterplot increased the plausibility of unreasonable causal claims. Also, simply warning participants that correlation is not causation seemed to lead participants to accept reasonable causal claims more cautiously. We observed a strong tendency among participants to associate correlation with causation. Yet, the warning appeared to reduce the tendency. Grounded in the findings, we propose ways to reduce the illusion of causality when using question-answering systems.

Natural language interfaces (NLIs) for data visualization are becoming increasingly popular both in academic research and in commercial software. Yet, there is a lack of empirical understanding of how people specify visualizations through natural language. We conducted an online study (N = 102), showing participants a series of visualizations and asking them to provide utterances they would pose to generate the displayed charts. From the responses, we curated a dataset of 893 utterances and characterized the utterances according to (1) their phrasing (e.g., commands, queries, questions) and (2) the information they contained (e.g., chart types, data aggregations). To help guide future research and development, we contribute this utterance dataset and discuss its applications toward the creation and benchmarking of NLIs for visualization.

Animation helps viewers follow transitions in data graphics. When authoring animations that incorporate data, designers must carefully coordinate the behaviors of visual objects such as entering, exiting, merging and splitting, and specify the temporal rhythms of transition through staging and staggering. We present Data Animator, a system for authoring animated data graphics without programming. Data Animator leverages the Data Illustrator framework to analyze and match objects between two static visualizations, and generates automated transitions by default. Designers have the flexibility to interpret and adjust the matching results through a visual interface. Data Animator also supports the division of a complex animation into stages through hierarchical keyframes, and uses data attributes to stagger the start time and vary the speed of animating objects through a novel timeline interface. We validate Data Animator’s expressiveness via a gallery of examples, and evaluate its usability in a re-creation study with designers.

Natural language interfaces (NLls) have shown great promise for visual data analysis, allowing people to flexibly specify and interact with visualizations. However, developing visualization NLIs remains a challenging task, requiring low-level implementation of natural language processing (NLP) techniques as well as knowledge of visual analytic tasks and visualization design. We present NL4DV, a toolkit for natural language-driven data visualization. NL4DV is a Python package that takes as input a tabular dataset and a natural language query about that dataset. In response, the toolkit returns an analytic specification modeled as a JSON object containing data attributes, analytic tasks, and a list of Vega-Lite specifications relevant to the input query. In doing so, NL4DV aids visualization developers who may not have a background in NLP, enabling them to create new visualization NLIs or incorporate natural language input within their existing systems. We demonstrate NL4DV's usage and capabilities through four examples: 1) rendering visualizations using natural language in a Jupyter notebook, 2) developing a NLI to specify and edit Vega-Lite charts, 3) recreating data ambiguity widgets from the DataTone system, and 4) incorporating speech input to create a multimodal visualization system.

Multimodal interfaces that combine direct manipulation and natural language have shown great promise for data visualization. Such multimodal interfaces allow people to stay in the flow of their visual exploration by leveraging the strengths of one modality to complement the weaknesses of others. In this article, we introduce an approach that interweaves multimodal interaction combining direct manipulation and natural language with flexible unit visualizations. We employ the proposed approach in a proof-of-concept system, DataBreeze. Coupling pen, touch, and speech-based multimodal interaction with flexible unit visualizations, DataBreeze allows people to create and interact with both systematically bound (e.g., scatterplots, unit column charts) and manually customized views, enabling a novel visual data exploration experience. We describe our design process along with DataBreeze's interface and interactions, delineating specific aspects of the design that empower the synergistic use of multiple modalities. We also present a preliminary user study with DataBreeze, highlighting the data exploration patterns that participants employed. Finally, reflecting on our design process and preliminary user study, we discuss future research directions.

Abstract Creating expressive animated data graphics often requires designers to possess highly specialized programming skills. Alternatively, the use of direct manipulation tools is popular among animation designers, but these tools have limited support for generating graphics driven by data. Our goal is to inform the design of next‐generation animated data graphic authoring tools. To understand the composition of animated data graphics, we survey real‐world examples and contribute a description of the design space. We characterize animated transitions based on object, graphic, data, and timing dimensions. We synthesize the primitives from the object, graphic, and data dimensions as a set of 10 transition types, and describe how timing primitives compose broader pacing techniques. We then conduct an ideation study that uncovers how people approach animation creation with three authoring paradigms: keyframe animation, procedural animation, and presets & templates. Our analysis shows that designers have an overall preference for keyframe animation. However, we find evidence that an authoring tool should combine these three paradigms as designers’ preferences depend on the characteristics of the animated transition design and the authoring task. Based on these findings, we contribute guidelines and design considerations for developing future animated data graphic authoring tools.

Interaction plays a vital role during visual network exploration as users need to engage with both elements in the view (e.g., nodes, links) and interface controls (e.g., sliders, dropdown menus). Particularly as the size and complexity of a network grow, interactive displays supporting multimodal input (e.g., touch, speech, pen, gaze) exhibit the potential to facilitate fluid interaction during visual network exploration and analysis. While multimodal interaction with network visualization seems like a promising idea, many open questions remain. For instance, do users actually prefer multimodal input over unimodal input, and if so, why? Does it enable them to interact more naturally, or does having multiple modes of input confuse users? To answer such questions, we conducted a qualitative user study in the context of a network visualization tool, comparing speech- and touch-based unimodal interfaces to a multimodal interface combining the two. Our results confirm that participants strongly prefer multimodal input over unimodal input attributing their preference to: 1) the freedom of expression, 2) the complementary nature of speech and touch, and 3) integrated interactions afforded by the combination of the two modalities. We also describe the interaction patterns participants employed to perform common network visualization operations and highlight themes for future multimodal network visualization systems to consider.

An emerging generation of visualization authoring systems support expressive information visualization without textual programming. As they vary in their visualization models, system architectures, and user interfaces, it is challenging to directly compare these systems using traditional evaluative methods. Recognizing the value of contextualizing our decisions in the broader design space, we present critical reflections on three systems we developed -Lyra, Data Illustrator, and Charticulator. This paper surfaces knowledge that would have been daunting within the constituent papers of these three systems. We compare and contrast their (previously unmentioned) limitations and trade-offs between expressivity and learnability. We also reflect on common assumptions that we made during the development of our systems, thereby informing future research directions in visualization authoring systems.

Abstract Many visual analytics systems allow users to interact with machine learning models towards the goals of data exploration and insight generation on a given dataset. However, in some situations, insights may be less important than the production of an accurate predictive model for future use. In that case, users are more interested in generating of diverse and robust predictive models, verifying their performance on holdout data, and selecting the most suitable model for their usage scenario. In this paper, we consider the concept of Exploratory Model Analysis (EMA), which is defined as the process of discovering and selecting relevant models that can be used to make predictions on a data source. We delineate the differences between EMA and the well‐known term exploratory data analysis in terms of the desired outcome of the analytic process: insights into the data or a set of deployable models. The contributions of this work are a visual analytics system workflow for EMA, a user study, and two use cases validating the effectiveness of the workflow. We found that our system workflow enabled users to generate complex models, to assess them for various qualities, and to select the most relevant model for their task.

Recently, an increasing number of visualization systems have begun to incorporate natural language generation (NLG) capabilities into their interfaces. NLG-based visualization systems typically leverage a suite of statistical functions to automatically extract key facts about the underlying data and surface them as natural language sentences alongside visualizations. With current systems, users are typically required to read the system-generated sentences and mentally map them back to the accompanying visualization. However, depending on the features of the visualization (e.g., visualization type, data density) and the complexity of the data fact, mentally mapping facts to visualizations can be a challenging task. Furthermore, more than one visualization could be used to illustrate a single data fact. Unfortunately, current tools provide little or no support for users to explore such alternatives. In this paper, we explore how system-generated data facts can be treated as interactive widgets to help users interpret visualizations and communicate their findings. We present Voder, a system that lets users interact with automatically-generated data facts to explore both alternative visualizations to convey a data fact as well as a set of embellishments to highlight a fact within a visualization. Leveraging data facts as interactive widgets, Voder also facilitates data fact-based visualization search. To assess Voder's design and features, we conducted a preliminary user study with 12 participants having varying levels of experience with visualization tools. Participant feedback suggested that interactive data facts aided them in interpreting visualizations. Participants also stated that the suggestions surfaced through the facts helped them explore alternative visualizations and embellishments to communicate individual data facts.

To interpret data visualizations, people must determine how visual features map onto concepts. For example, to interpret colormaps, people must determine how dimensions of color (e.g., lightness, hue) map onto quantities of a given measure (e.g., brain activity, correlation magnitude). This process is easier when the encoded mappings in the visualization match people's predictions of how visual features will map onto concepts, their inferred mappings. To harness this principle in visualization design, it is necessary to understand what factors determine people's inferred mappings. In this study, we investigated how inferred color-quantity mappings for colormap data visualizations were influenced by the background color. Prior literature presents seemingly conflicting accounts of how the background color affects inferred color-quantity mappings. The present results help resolve those conflicts, demonstrating that sometimes the background has an effect and sometimes it does not, depending on whether the colormap appears to vary in opacity. When there is no apparent variation in opacity, participants infer that darker colors map to larger quantities (dark-is-more bias). As apparent variation in opacity increases, participants become biased toward inferring that more opaque colors map to larger quantities (opaque-is-more bias). These biases work together on light backgrounds and conflict on dark backgrounds. Under such conflicts, the opaque-is-more bias can negate, or even supersede the dark-is-more bias. The results suggest that if a design goal is to produce colormaps that match people's inferred mappings and are robust to changes in background color, it is beneficial to use colormaps that will not appear to vary in opacity on any background color, and to encode larger quantities in darker colors.

Multimodal interaction offers many potential benefits for data visualization. It can help people stay in the flow of their visual analysis and presentation, with the strengths of one interaction modality offsetting the weaknesses of others. Furthermore, multimodal interaction offers strong promise for leveraging data visualization on diverse display hardware including mobile, AR/VR, and large displays. However, prior research on visualization and interaction techniques has mostly explored a single input modality such as mouse, touch, pen, or more recently, natural language. The unique challenges and opportunities of synergistic multimodal interaction for data visualization have yet to be investigated. This workshop will bring together researchers with expertise in visualization, interaction design, and natural user interfaces. We aim to build a community of researchers focusing on multimodal interaction for data visualization, explore opportunities and challenges in our research, and establish an agenda for multimodal interaction research specifically for data visualization.

Touch-based displays are becoming a popular medium for interacting with visualizations. Network visualizations are a frequently used class of visualizations across domains to explore entities and relationships between them. However, little work has been done in exploring the design of network visualizations and corresponding interactive tasks such as selection, browsing, and navigation on touch-based displays. Network visualizations on touch-based displays are usually implemented by porting the conventional pointer based interactions as-is to a touch environment and replacing the mouse cursor with a finger. However, this approach does not fully utilize the potential of naturalistic multi-touch gestures afforded by touch displays. We present a set of single hand, multi-touch gestures for interactive exploration of network visualizations and employ these in a prototype system, Tangraphe. We discuss the proposed interactions and how they facilitate a variety of commonly performed network visualization tasks including selection, navigation, adjacency-based exploration, and layout modification. We also discuss advantages of and potential extensions to the proposed set of one-handed interactions including leveraging the non-dominant hand for enhanced interaction, incorporation of additional input modalities, and integration with other devices.

Building graphical user interfaces for visualization authoring is challenging as one must reconcile the tension between flexible graphics manipulation and procedural visualization generation based on a graphical grammar or declarative languages. To better support designers' workflows and practices, we propose Data Illustrator, a novel visualization framework. In our approach, all visualizations are initially vector graphics; data binding is applied when necessary and only constrains interactive manipulation to that data bound property. The framework augments graphic design tools with new concepts and operators, and describes the structure and generation of a variety of visualizations. Based on the framework, we design and implement a visualization authoring system. The system extends interaction techniques in modern vector design tools for direct manipulation of visualization configurations and parameters. We demonstrate the expressive power of our approach through a variety of examples. A qualitative study shows that designers can use our framework to compose visualizations.

Abstract In this report, we organize and reflect on recent advances and challenges in the field of sports data visualization. The exponentially‐growing body of visualization research based on sports data is a prime indication of the importance and timeliness of this report. Sports data visualization research encompasses the breadth of visualization tasks and goals: exploring the design of new visualization techniques; adapting existing visualizations to a novel domain; and conducting design studies and evaluations in close collaboration with experts, including practitioners, enthusiasts, and journalists. Frequently this research has impact beyond sports in both academia and in industry because it is i) grounded in realistic, highly heterogeneous data, ii) applied to real‐world problems, and iii) designed in close collaboration with domain experts. In this report, we analyze current research contributions through the lens of three categories of sports data: box score data (data containing statistical summaries of a sport event such as a game), tracking data (data about in‐game actions and trajectories), and meta‐data (data about the sport and its participants but not necessarily a given game). We conclude this report with a high‐level discussion of sports visualization research informed by our analysis—identifying critical research gaps and valuable opportunities for the visualization community. More information is available at the STAR's website: https://sportsdataviz.github.io/ .

In this article, we present an interactive visual information retrieval and recommendation system, called VisIRR, for large-scale document discovery. VisIRR effectively combines the paradigms of (1) a passive pull through query processes for retrieval and (2) an active push that recommends items of potential interest to users based on their preferences. Equipped with an efficient dynamic query interface against a large-scale corpus, VisIRR organizes the retrieved documents into high-level topics and visualizes them in a 2D space, representing the relationships among the topics along with their keyword summary. In addition, based on interactive personalized preference feedback with regard to documents, VisIRR provides document recommendations from the entire corpus, which are beyond the retrieved sets. Such recommended documents are visualized in the same space as the retrieved documents, so that users can seamlessly analyze both existing and newly recommended ones. This article presents novel computational methods, which make these integrated representations and fast interactions possible for a large-scale document corpus. We illustrate how the system works by providing detailed usage scenarios. Additionally, we present preliminary user study results for evaluating the effectiveness of the system.

Data visualization systems have predominantly been developed for WIMP-based direct manipulation interfaces. Only recently have other forms of interaction begun to appear, such as natural language or touch-based interaction, though usually operating only independently. Prior evaluations of natural language interfaces for visualization have indicated potential value in combining direct manipulation and natural language as complementary interaction techniques. We hypothesize that truly multimodal interfaces for visualization, those providing users with freedom of expression via both natural language and touch-based direct manipulation input, may provide an effective and engaging user experience. Unfortunately, however, little work has been done in exploring such multimodal visualization interfaces. To address this gap, we have created an architecture and a prototype visualization system called Orko that facilitates both natural language and direct manipulation input. Specifically, Orko focuses on the domain of network visualization, one that has largely relied on WIMP-based interfaces and direct manipulation interaction, and has little or no prior research exploring natural language interaction. We report results from an initial evaluation study of Orko, and use our observations to discuss opportunities and challenges for future work in multimodal network visualization interfaces.

The papers in this special issue were presented at IEEE VIS 2016, held during October 23-28, 2016 in Baltimore, MD. VIS contains three conferences, held concurrently: the IEEE Visual Analytics Science and Technology Conference (IEEE VAST 2016), the IEEE Information Visualization Conference (IEEE InfoVis 2016), and the IEEE Scientific Visualization Conference (IEEE SciVis2016).

We introduce SentenTree, a novel technique for visualizing the content of unstructured social media text. SentenTree displays frequent sentence patterns abstracted from a corpus of social media posts. The technique employs design ideas from word clouds and the Word Tree, but overcomes a number of limitations of both those visualizations. SentenTree displays a node-link diagram where nodes are words and links indicate word co-occurrence within the same sentence. The spatial arrangement of nodes gives cues to the syntactic ordering of words while the size of nodes gives cues to their frequency of occurrence. SentenTree can help people gain a rapid understanding of key concepts and opinions in a large social media text collection. It is implemented as a lightweight application that runs in the browser.

We have created and made available to all a dataset with information about every paper that has appeared at the IEEE Visualization (VIS) set of conferences: InfoVis, SciVis, VAST, and Vis. The information about each paper includes its title, abstract, authors, and citations to other papers in the conference series, among many other attributes. This article describes the motivation for creating the dataset, as well as our process of coalescing and cleaning the data, and a set of three visualizations we created to facilitate exploration of the data. This data is meant to be useful to the broad data visualization community to help understand the evolution of the field and as an example document collection for text data visualization research.

A bipartite graph models the relation between two different types of entities. It is applicable, for example, to describe persons' affiliations to different social groups or their association with subjects such as topics of interest. In these applications, it is important to understand the connectivity patterns among the entities in the bipartite graph. For the example of a bipartite relation between persons and their topics of interest, people may form groups based on their common interests, and the topics also can be grouped or categorized based on the interested audiences. Co-clustering methods can identify such connectivity patterns and find clusters within the two types of entities simultaneously. In this paper, we propose an interactive visualization design that incorporates co-clustering methods to facilitate the identification of node clusters formed by their common connections in a bipartite graph. Besides highlighting the automatically detected node clusters and the connections among them, the visual interface also provides visual cues for evaluating the homogeneity of the bipartite connections in a cluster, identifying potential outliers, and analyzing the correlation of node attributes with the cluster structure. The interactive visual interface allows users to flexibly adjust the node grouping to incorporate their prior knowledge of the domain, either by direct manipulation (i.e., splitting and merging the clusters), or by providing explicit feedback on the cluster quality, based on which the system will learn a parametrization of the co-clustering algorithm to better align with the users' notion of node similarity. To demonstrate the utility of the system, we present two example usage scenarios on real world datasets.

Selection is a fundamental operation in interactive visualization applications. Although techniques such as clicking and lassoing items of interest are sufficient for basic selections, a more sophisticated interaction mechanism is required for expressing complex queries to modify or generalize existing selections. The ability to perform these advanced selections is critical for effective analysis within visualization systems. On touch-based devices such as tablets, however, expressing advanced selections is difficult due to the absence of a cursor and modifier keys. In this work, we address this limitation by presenting new interaction techniques that leverage a person's non-dominant hand. We use these techniques for advanced selection operations such as expanding, modifying, and replicating existing selections. Further, we introduce a method for performing generalized selection on tablet devices that provides a fluid mechanism to control the attributes and parameters of selection.

Abstract The use of multiple coordinated views (MCV) in data visualization provides analytic power because it allows a person to explore data under a variety of different perspectives. Since this design pattern utilizes multiple visualizations and requires coordinated interactions across the views, a clever use of screen space is vital and many synchronized interface operations must be provided. Bringing this design pattern to tablet computers is challenging due to their small display size and the absence of keyboard and mouse input. In this article, we explain important design considerations for MCV visualization on tablets and describe a prototype MCV visualization system we have built for the iPad. The design is based on the principles of maximizing screen space for data presentation, promoting consistent interactions across visualizations, and minimizing occlusion from a person's hands.

Although researchers have devoted considerable attention to helping database users formulate queries, many users still find it challenging to specify queries that involve joining tables. To help users construct join queries for exploring relational databases, we propose ETable , a novel presentation data model that provides users with a presentation-level interactive view. This view compactly presents one-to-many and many-to-many relationships within a single enriched table by allowing a cell to contain a set of entity references . Users can directly interact with this enriched table to incrementally construct complex queries and navigate databases on a conceptual entity-relationship level. In a user study, participants performed a range of database querying tasks faster with ETable than with a commercial graphical query builder. Subjective feedback about ETable was also positive. All participants found that ETable was easier to learn and helpful for exploring databases.

Abstract Developmental psychology researchers examine the temporal relationships of social and communicative behaviors, such as how a child responds to a name call, to understand early typical and atypical development and to discover early signs of autism and developmental delay. These related behaviors occur together or within close temporal proximity, forming unique patterns and relationships of interest. However, the task of finding these early signs, which are in the form of atypical behavioral patterns, becomes more challenging when behaviors of multiple children at different ages need to be compared with each other in search of generalizable patterns. The ability to visually explore the temporal relationships of behaviors, including flexible redefinition of closeness, over multiple social interaction sessions with children of different ages, can make such knowledge extraction easier. We have designed a visualization tool called TipoVis that helps psychology researchers visually explore the temporal patterns of social and communicative behaviors. We present two case studies to show how TipoVis helped two researchers derive new understandings of their data.

Tablet computers now offer screen sizes and computing capabilities that are competitive with traditional desktop PCs. Their popularity has grown tremendously, but we are just beginning to see information visualization applications designed for this platform. One potential reason for this limited development is the challenge of designing and implementing a multi-touch interface for visualizations on mobile, tablet devices. In this work, we identify the primary challenges that touch screen interactions pose for information visualization applications. We explore the design space of multi-touch interactions for visualizations and present a prototype information visualization application using a specific technique, a dynamic scatterplot, for an iPad.

Visualizing sets to reveal relationships between constituent elements is a complex representational problem. Recent research presents several automated placement and grouping techniques to highlight connections between set elements. However, these techniques do not scale well for sets with cardinality greater than one hundred elements. We present OnSet, an interactive, scalable visualization technique for representing large-scale binary set data. The visualization technique defines a single, combined domain of elements for all sets, and models each set by the elements that it both contains and does not contain. OnSet employs direct manipulation interaction and visual highlighting to support easy identification of commonalities and differences as well as membership patterns across different sets of elements. We present case studies to illustrate how the technique can be successfully applied across different domains such as bio-chemical metabolomics and task and event scheduling.

The field of graph visualization has produced a wealth of visualization techniques for accomplishing a variety of analysis tasks. Therefore analysts often rely on a suite of different techniques, and visual graph analysis application builders strive to provide this breadth of techniques. To provide a holistic model for specifying network visualization techniques (as opposed to considering each technique in isolation) we present the Graph-Level Operations (GLO) model. We describe a method for identifying GLOs and apply it to identify five classes of GLOs, which can be flexibly combined to re-create six canonical graph visualization techniques. We discuss advantages of the GLO model, including potentially discovering new, effective network visualization techniques and easing the engineering challenges of building multi-technique graph visualization applications. Finally, we implement the GLOs that we identified into the GLO-STIX prototype system that enables an analyst to interactively explore a graph by applying GLOs.

Business ecosystems are characterized by large, complex, and global networks of firms, often from many different market segments, all collaborating, partnering, and competing to create and deliver new products and services. Given the rapidly increasing scale, complexity, and rate of change of business ecosystems, as well as economic and competitive pressures, analysts are faced with the formidable task of quickly understanding the fundamental characteristics of these interfirm networks. Existing tools, however, are predominantly query- or list-centric with limited interactive, exploratory capabilities. Guided by a field study of corporate analysts, we have designed and implemented dotlink360, an interactive visualization system that provides capabilities to gain systemic insight into the compositional, temporal, and connective characteristics of business ecosystems. dotlink360 consists of novel, multiple connected views enabling the analyst to explore, discover, and understand interfirm networks for a focal firm, specific market segments or countries, and the entire business ecosystem. System evaluation by a small group of prototypical users shows supporting evidence of the benefits of our approach. This design study contributes to the relatively unexplored, but promising area of exploratory information visualization in market research and business strategy.

Investigators across many disciplines and organizations must sift through large collections of text documents to understand and piece together information. Whether they are fighting crime, curing diseases, deciding what car to buy, or researching a new field, inevitably investigators will encounter text documents. Taking a visual analytics approach, we integrate multiple text analysis algorithms with a suite of interactive visualizations to provide a flexible and powerful environment that allows analysts to explore collections of documents while sensemaking. Our particular focus is on the process of integrating automated analyses with interactive visualizations in a smooth and fluid manner. We illustrate this integration through two example scenarios: an academic researcher examining InfoVis and VAST conference papers and a consumer exploring car reviews while pondering a purchase decision. Finally, we provide lessons learned toward the design and implementation of visual analytics systems for document exploration and understanding.

Visual analytics involves complex analytical processes that can often benefit from collaboration. Many researchers have explored co-located synchronous systems to help support collaborative visual analytics; however, the process can often be long and require a series of sessions. Providing support for asynchronous collaboration in visual analytics systems can help divide the problem between several analysts across many sessions to ensure that they can effectively work together toward a solution. Currently, visual analytics systems offer limited support for asynchronous, multi-session work [1]. In this workshop, we seek to bring together researchers from both the CSCW and Visual Analytics communities to discuss avenues for supporting asynchronous collaboration in visual analytics system.

After the news of Osama Bin Laden's death leaked through Twitter, many people wondered if Twitter would fundamentally change the way we produce, spread, and consume news. In this paper we provide an in-depth analysis of how the news broke and spread on Twitter. We confirm the claim that Twitter broke the news first, and find evidence that Twitter had convinced a large number of its audience before mainstream media confirmed the news. We also discover that attention on Twitter was highly concentrated on a small number of "opinion leaders" and identify three groups of opinion leaders who played key roles in spreading the news: individuals affiliated with media played a large part in breaking the news, mass media brought the news to a wider audience and provided eager Twitter users with content on external sites, and celebrities helped to spread the news and stimulate conversation. Our findings suggest Twitter has great potential as a news medium.

Abstract Clustering plays an important role in many large‐scale data analyses providing users with an overall understanding of their data. Nonetheless, clustering is not an easy task due to noisy features and outliers existing in the data, and thus the clustering results obtained from automatic algorithms often do not make clear sense. To remedy this problem, automatic clustering should be complemented with interactive visualization strategies. This paper proposes an interactive visual analytics system for document clustering, called iVisClustering, based on a widely‐used topic modeling method, latent Dirichlet allocation (LDA). iVisClustering provides a summary of each cluster in terms of its most representative keywords and visualizes soft clustering results in parallel coordinates. The main view of the system provides a 2D plot that visualizes cluster similarities and the relation among data items with a graph‐based representation. iVisClustering provides several other views, which contain useful interaction methods. With help of these visualization modules, we can interactively refine the clustering results in various ways. Keywords can be adjusted so that they characterize each cluster better. In addition, our system can filter out noisy data and re‐cluster the data accordingly. Cluster hierarchy can be constructed using a tree structure and for this purpose, the system supports cluster‐level interactions such as sub‐clustering, removing unimportant clusters, merging the clusters that have similar meanings, and moving certain clusters to any other node in the tree structure. Furthermore, the system provides document‐level interactions such as moving mis‐clustered documents to another cluster and removing useless documents. Finally, we present how interactive clustering is performed via iVisClustering by using real‐world document data sets.

While the formal evaluation of systems in visual analytics is still relatively uncommon, particularly rare are case studies of prolonged system use by domain analysts working with their own data. Conducting case studies can be challenging, but it can be a particularly effective way to examine whether visual analytics systems are truly helping expert users to accomplish their goals. We studied the use of a visual analytics system for sensemaking tasks on documents by six analysts from a variety of domains. We describe their application of the system along with the benefits, issues, and problems that we uncovered. Findings from the studies identify features that visual analytics systems should emphasize as well as missing capabilities that should be addressed. These findings inform design implications for future systems.

Sports analysts live in a world of dynamic games flattened into tables of numbers, divorced from the rinks, pitches, and courts where they were generated. Currently, these professional analysts use R, Stata, SAS, and other statistical software packages for uncovering insights from game data. Quantitative sports consultants seek a competitive advantage both for their clients and for themselves as analytics becomes increasingly valued by teams, clubs, and squads. In order for the information visualization community to support the members of this blossoming industry, it must recognize where and how visualization can enhance the existing analytical workflow. In this paper, we identify three primary stages of today's sports analyst's routine where visualization can be beneficially integrated: 1) exploring a dataspace; 2) sharing hypotheses with internal colleagues; and 3) communicating findings to stakeholders.Working closely with professional ice hockey analysts, we designed and built SnapShot, a system to integrate visualization into the hockey intelligence gathering process. SnapShot employs a variety of information visualization techniques to display shot data, yet given the importance of a specific hockey statistic, shot length, we introduce a technique, the radial heat map. Through a user study, we received encouraging feedback from several professional analysts, both independent consultants and professional team personnel.

Previously, we presented Viz-A-Vis, a VIsualiZation of Activity through computer VISion [17]. Viz-A-Vis visualizes behavior as aggregate motion over observation space. In this paper, we present two complementary user studies of Viz-A-Vis measuring its performance and discovery affordances. First, we present a controlled user study aimed at comparatively measuring behavioral analysis preference and performance for observation and search tasks. Second, we describe a study with architects measuring discovery affordances and potential impacts on their work practices. We conclude: 1) Viz-A-Vis significantly reduced search time; and 2) it increased the number and quality of insightful discoveries.

Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies of the potential benefits of such systems have been conducted, particularly controlled, comparative evaluations. Determining how such systems foster insight and sensemaking is important for their continued growth and study, however. Furthermore, studies that identify how people use such systems and why they benefit (or not) can help inform the design of new systems in this area. We conducted an evaluation of the visual analytics system Jigsaw employed in a small investigative sensemaking exercise, and compared its use to three other more traditional methods of analysis. Sixteen participants performed a simulated intelligence analysis task under one of the four conditions. Experimental results suggest that Jigsaw assisted participants to analyze the data and identify an embedded threat. We describe different analysis strategies used by study participants and how computational support (or the lack thereof) influenced the strategies. We then illustrate several characteristics of the sensemaking process identified in the study and provide design implications for investigative analysis tools based thereon. We conclude with recommendations on metrics and techniques for evaluating visual analytics systems for investigative analysis.

Although previous research has suggested that examining the interplay between internal and external representations can benefit our understanding of the role of information visualization (InfoVis) in human cognitive activities, there has been little work detailing the nature of internal representations, the relationship between internal and external representations and how interaction is related to these representations. In this paper, we identify and illustrate a specific kind of internal representation, mental models, and outline the high-level relationships between mental models and external visualizations. We present a top-down perspective of reasoning as model construction and simulation, and discuss the role of visualization in model based reasoning. From this perspective, interaction can be understood as active modeling for three primary purposes: external anchoring, information foraging, and cognitive offloading. Finally we discuss the implications of our approach for design, evaluation and theory development.

Relatively little is known about how the presence and location of multiple shared displays changes the performance and dynamics of teams collaborating. We conducted a case study evaluating several shared display configurations with groups collaborating on a data-intensive, sense-making task. Teams completed the same task using either a single display, side-by-side dual, or opposing dual shared displays. The location of the second shared display significantly impacted the ability for teams to make logical connections amongst the data. Users were also significantly more satisfied with the collaboration process using the side-by-side dual display condition than those using a single display.

We present a case study of our experience designing SellTrend, a visualization system for analyzing airline travel purchase requests. The relevant transaction data can be characterized as multi-variate temporal and categorical event sequences, and the chief problem addressed is how to help company analysts identify complex combinations of transaction attributes that contribute to failed purchase requests. SellTrend combines a diverse set of techniques ranging from time series visualization to faceted browsing and historical trend analysis in order to help analysts make sense of the data. We believe that the combination of views and interaction capabilities in SellTrend provides an innovative approach to this problem and to other similar types of multivariate, temporally driven transaction data analysis. Initial feedback from company analysts confirms the utility and benefits of the system.

Information awareness applications offer the exciting potential to help people to better manage the data they encounter on a routine basis, but customizing these applications is a difficult task. Most applications allow users to perform basic customizations or programmers to create advanced ones. We present an intermediate customization space and Cocoa Buzz, an application that demonstrates one way to bridge these two extremes. Cocoa Buzz runs on an extra display on the user's desktop or on a large shared display and cycles through different information sources customized by the user. We further demonstrate some of the customizations that have been made using this approach. We show some preliminary evidence to suggest that this approach may be useful at providing users with the ability to perform customizations across this spectrum.

We introduce Imprint, a casual information visualization kiosk that displays data extracted from a printer queue. We designed the system to be open-ended, and to support a workgroup in reflection and conversation about the data. Imprint's visualizations depict environmental issues, such as energy consumption and paper consumption of the printers, as well as social information, such as popular concepts from the printed matter. Imprint is intended to spark reflection and conversation, and to bring data into discussions about paper usage and "waste." Our goal is not to explicitly reduce paper, energy, or toner consumption, but instead to open conversations by community members. Our work highlights a design approach for semi-public displays of personal data.

Making sense of data becomes more challenging as the data grows larger and becomes more complex. If a picture truly can be worth a thousand words, then clever visualizations of data should hold promise in helping people with sense-making tasks. I firmly believe that visual representations of data can help people to better explore, analyze, and understand it, thus transforming the data into information. In this talk, I will explain how visualization and visual analytics help people make sense of data and I will provide many such examples. I also will describe my present research into visualization for investigative analysis. This project explores how visual analytics can help investigators examine a large document collection in order to discover embedded stories and narratives scattered across the documents in the collection.

Even though information visualization (InfoVis) research has matured in recent years, it is generally acknowledged that the field still lacks supporting, encompassing theories. In this paper, we argue that the distributed cognition framework can be used to substantiate the theoretical foundation of InfoVis. We highlight fundamental assumptions and theoretical constructs of the distributed cognition approach, based on the cognitive science literature and a real life scenario. We then discuss how the distributed cognition framework can have an impact on the research directions and methodologies we take as InfoVis researchers. Our contributions are as follows. First, we highlight the view that cognition is more an emergent property of interaction than a property of the human mind. Second, we argue that a reductionist approach to study the abstract properties of isolated human minds may not be useful in informing InfoVis design. Finally we propose to make cognition an explicit research agenda, and discuss the implications on how we perform evaluation and theory building.

Animation has been used to show trends in multi-dimensional data. This technique has recently gained new prominence for presentations, most notably with Gapminder Trendalyzer. In Trendalyzer, animation together with interesting data and an engaging presenter helps the audience understand the results of an analysis of the data. It is less clear whether trend animation is effective for analysis. This paper proposes two alternative trend visualizations that use static depictions of trends: one which shows traces of all trends overlaid simultaneously in one display and a second that uses a small multiples display to show the trend traces side-by-side. The paper evaluates the three visualizations for both analysis and presentation. Results indicate that trend animation can be challenging to use even for presentations; while it is the fastest technique for presentation and participants find it enjoyable and exciting, it does lead to many participant errors. Animation is the least effective form for analysis; both static depictions of trends are significantly faster than animation, and the small multiples display is more accurate.

In the established procedural model of information visualization, the first operation is to transform raw data into data tables [1]. The transforms typically include abstractions that aggregate and segment relevant data and are usually defined by a human, user or programmer. The theme of this paper is that for video, data transforms should be supported by low level computer vision. High level reasoning still resides in the human analyst, while part of the low level perception is handled by the computer. To illustrate this approach, we present Viz-A-Vis, an overhead video capture and access system for activity analysis in natural settings over variable periods of time. Overhead video provides rich opportunities for long-term behavioral and occupancy analysis, but it poses considerable challenges. We present initial steps addressing two challenges. First, overhead video generates overwhelmingly large volumes of video impractical to analyze manually. Second, automatic video analysis remains an open problem for computer vision.

We present an evaluation of mudibo, a prototype system for determining the position of dialog boxes in a multiple-monitor system. The analysis shows that, when compared to a standard approach, mudibo offered a 24% decrease in time needed to begin interaction in a dialog box. Analysis of participant behavior in the evaluation provides insight into the way users perceive and act in multiple-monitor environments. Specifically, the notion of consistency changes for multiple-monitor systems and the prospect of adaptive algorithms becomes further complicated and intricate, especially for window management.

We performed an empirical study exploring people's interactions with an embodied conversational agent (ECA) while performing two tasks. Conditions varied with respect to 1) whether participants were allowed to choose an agent and its characteristics and 2) the putative quality or appropriateness of the agent for the tasks. For both tasks, selection combined with the illusion of further customization significantly improved participants' overall subjective impressions of the ECAs while putative quality had little or no effect. Additionally, performance data revealed that the ECA's motivation and persuasion effects were significantly enhanced when participants chose agents to use. We found that user expectations about and perceptions of the interaction between themselves and an ECA depended very much on the individual's preconceived notions and preferences of various ECA characteristics and might deviate greatly from the models that ECA designers intend to portray.

Peripheral displays provide secondary awareness of news and information to people. When such displays are static, the amount of information that can be presented is limited and the display may become boring or routine over time. Adding animation to peripheral displays can allow them to show more information and can potentially enhance visual interest and appeal, but it may also make the display very distracting. Is it possible to employ animation for visual benefit without increasing distraction? We have created a peripheral display system called BlueGoo that visualizes R.S.S. news feeds as animated photographic collages. We present an empirical study in which participants did not find the system to be distracting, and many found it to be appealing. The study also explored how different display sizes and positions affect information conveyance and distraction. Animations on an angled second monitor appeared to be more distracting than three other configurations.

Information visualization has often focused on providing deep insight for expert user populations and on techniques for amplifying cognition through complicated interactive visual models. This paper proposes a new subdomain for infovis research that complements the focus on analytic tasks and expert use. Instead of work-related and analytically driven infovis, we propose Casual Information Visualization (or Casual Infovis) as a complement to more traditional infovis domains. Traditional infovis systems, techniques, and methods do not easily lend themselves to the broad range of user populations, from expert to novices, or from work tasks to more everyday situations. We propose definitions, perspectives, and research directions for further investigations of this emerging subfield. These perspectives build from ambient information visualization [32], social visualization, and also from artistic work that visualizes information [41]. We seek to provide a perspective on infovis that integrates these research agendas under a coherent vocabulary and framework for design. We enumerate the following contributions. First, we demonstrate how blurry the boundary of infovis is by examining systems that exhibit many of the putative proper ties of infovis systems, but perhaps would not be considered so. Second, we explore the notion of insight and how, instead of a monolithic definition of insight, there may be multiple types, each with particular characteristics. Third, we discuss design challenges for systems intended for casual audiences. Finally we conclude with challenges for system evaluation in this emerging subfield.

Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a user's intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction.

Researchers have explored the design of ambient information systems across a wide range of physical and screen-based media. This work has yielded rich examples of design approaches to the problem of presenting information about a user's world in a way that is not distracting, but is aesthetically pleasing, and tangible to varying degrees. Despite these successes, accumulating theoretical and craft knowledge has been stymied by the lack of a unified vocabulary to describe these systems and a consequent lack of a framework for understanding their design attributes. We argue that this area would significantly benefit from consensus about the design space of ambient information systems and the design attributes that define and distinguish existing approaches. We present a definition of ambient information systems and a taxonomy across four design dimensions: Information Capacity, Notification Level, Representational Fidelity, and Aesthetic Emphasis. Our analysis has uncovered four patterns of system design and points to unexplored regions of the design space, which may motivate future work in the field.

THREE papers in this issue of IEEE Transactions on Visualization and Computer Graphics (TVCG) are expanded versions of ones presented at InfoVis 2005. These examples of the cutting edge of information visualization research showcase the diversity and depth of the field, illustrating new display techniques as well as novel application domains for information visualization systems. The three papers focus on the visualization of three different styles of data: graph-based data, time series data, and categorical data. The techniques developed and described in these papers may also be applicable to data from a variety of problem areas and the authors include both design motivations in their work as well as illustrative examples of the application of the techniques. “Drawing Directed Graphs Using Quadratic Programming,” by Tim Dwyer, Yehuda Koren, and Kim Marriott, won the InfoVis 2005 Best Paper Award. In this paper, the authors introduce a new method for drawing directed graphs that combines constraint programming techniques with a high performance force-directed placement algorithm. The technique is useful for highlighting hierarchies in directed graphs while retaining beneficial properties of force-directed placement strategies such as proximity and symmetry relations. The authors also describe experiments that show this new visualization technique can convey the structure of large digraphs better than the most widely used hierarchical graph drawing method. “Designing for Social Data Analysis,” by Martin Wattenberg and Jesse Kriss, explores how an information visualization tool may become part of a dynamic online social environment. The authors focus on the area of baby naming and provide a delightful tool called the NameVoyager, a Web-based system that allows people to explore historical trends in the names that parents give to their children. The NameVoyager garnered huge interest on the Web when it was deployed and the authors explore how the system facilitates a form of social data analysis. The paper describes design decisions and implementation issues that arose for the system and it considers some of the reasons why the system became so popular. The paper concludes by discussing the design of an extension to the system for a more complex data set. “Parallel Sets: Interactive Exploration and Visual Analysis of Categorical Data,” by Robert Kosara, Fabian Bendix, and Helwig Hauser, applies a variation of the well-known parallel coordinates visualization technique for representing categorical data. The introduced technique shows data frequencies instead of individual data points and uses boxes and parallelograms within the parallel coordinates style plot. The authors include a rich set of interaction techniques with the visualization that allow viewers to examine many different perspectives on the data. They illustrate the power of their visualization through sample analysis scenarios with two example data sets.

Information visualization is an effective way to easily comprehend large amounts of data. For such systems to be truly effective, the information visualization designer must be aware of the ways in which their system may be manipulated and protect their users from attack. In addition, users should be aware of potential attacks in order to minimize or negate their effect. These attacks target the information visualization system as well as the perceptual, cognitive and motor capabilities of human end users. To identify and help counter these attacks we present a framework for information visualization system security analysis, a taxonomy of visualization attacks and technology independent principles for countering malicious visualizations. These themes are illustrated with case studies and working examples from the network security visualization domain, but are widely applicable to virtually any information visualization system.

The massive amount of alarm data generated from intrusion detection systems is cumbersome for network system administrators to analyze. Often, important details are overlooked and it is difficult to get an overall picture of what is occurring in the network by manually traversing textual alarm logs. We have designed a novel visualization to address this problem by showing alarm activity within a network. Alarm data is presented in an overview where system administrators can get a general sense of network activity and easily detect anomalies. They then have the option of zooming and drilling down for details. The information is presented with local network IP (Internet Protocol) addresses plotted over multiple yaxes to represent the location of alarms. Time on the x-axis is used to show the pattern of the alarms and variations in color encode the severity and amount of alarms. Based on our system administrator requirements study, this graphical layout addresses what system administrators need to see, is faster and easier than analyzing text logs, and uses visualization techniques to effectively scale and display the data. With this design, we have built a tool that effectively uses operational alarm log data generated on the Georgia Tech campus network. The motivation and background of our design is presented along with examples that illustrate its usefulness.

The design and evaluation of most current information visualization systems descend from an emphasis on a user's ability to "unpack" the representations of data of interest and operate on them independently. Too often, successful decision-making and analysis are more a matter of serendipity and user experience than of intentional design and specific support for such tasks; although humans have considerable abilities in analyzing relationships from data, the utility of visualizations remains relatively variable across users, data sets, and domains. In this paper, we discuss the notion of analytic gaps, which represent obstacles faced by visualizations in facilitating higher-level analytic tasks, such as decision-making and learning. We discuss support for bridging these gaps, propose a framework for the design and evaluation of information visualization systems, and demonstrate its use.

Recent research and technology advances indicate that multiple monitor systems are likely to become commonplace in the near future. An important property of such systems is that the physical separation of the display prompts users to place windows entirely within monitors, and thus does not fully alleviate the problem of managing windows on smaller monitors. Another finding about multiple monitor systems is that an additional monitor often holds windows that help the user maintain awareness rather than support interaction with information, but that multiple monitor users tend not to have many more windows visible than their single-monitor counterparts. We therefore present a window shrinking operation that specifically intends to help users display a window's relevant information. The operation should help to create smaller windows to manage, helping the "small monitor management" problem and targeting use of awareness windows on multiple monitor systems.

To investigate the program-execution data efficiently, we must be able to view the data at different levels of detail. In our visualization approach, we represent software systems at three different levels: statement level, file level, and system level. At the statement level, we represent the actual code. The representation at the file level provides a miniaturized view of the source code similar to the one used in the SeeSoft system (Eick et al., 1992). The system level uses treemaps (Shneiderman, 1992 and Bruls et al., 2000) to represent the software and is the most abstracted level in our visualization. At each level, coloring is used to represent one- or two-dimensional information about the code, using the colors' hue and brightness components. The coloring technique that we apply is a generalization of the coloring technique defined for fault-localization by Jones and colleagues (2001). GAMMATELLA is a toolset that implements our visualization approach and provides capabilities for instrumenting the code, collecting program-execution data from the field, and storing and retrieving the data locally. GAMMATELLA is written in Java, supports the monitoring of Java programs, and consists of three main components: an instrumentation, execution, and coverage tool, a data collection daemon, and a program visualizer.

Although PDAs typically run applications in a "stand-alone" mode, they are increasingly equipped with wireless communications, which makes them useful in new domains. This capability for more powerful information exchange with larger information systems presents a new situated context for PDA applications, and provides new design and usability evaluation challenges.In this work we examine how grocery shopping could be aided by a mobile shopping application that consumers access via a PDA while in a store. The interactive relationship between the physical space of the store and the human activity of shopping are crucial when designing for this application. To better understand this interaction, we studied people's grocery shopping habits, designed and evaluated prototypes, and performed usability tests within the shopping environment. This paper reveals our design process for this problem and a framework for designing and evaluating situated applications for mobile handhelds.

Georges Grinstein Questions often asked when presenting some new model, new theory, new research or new visualization include: How useful or how usable is it? and Have you performed any tests? Visualization is an interface technology and as such includes not just software algorithms and techniques, but computer human interaction issues as well. This makes it draw from both areas, one appearing more focused on utility and the other on usability. One key step in the development of a new theory is the attempt to first solve a problem. That problem or question does not include a section; in some domain it may not even contain a utility one from most people's perspectives. We discuss both sides of the issue to clarify the role of each in the development of new visualization technologies. Position Statement Alfred Kobsa In the HCI literature one can find studies which conclude that ease of use is more important than usefulness (Hubona & Blanton 1996), that the opposite holds true (Liao and Landry 2000), and that ease of use is more important for females while the usefulness is more important for males (Yuen and Ma 2002). In this panel contribution, we will present several user studies with information visualization systems, ranging from lab experiments with closed questions to longitudinal adoption studies with administrative data analysts (Gonzales & Kobsa, 2003; Kobsa 2001, 2003; Mark et al. 2003). -------------------------------------------a e-mail: [email protected] e-mail: [email protected] e-mail: [email protected] e-mail [email protected] e-mail [email protected] Consistent with general HCI research, the results show that both factors are important in certain situations, but do not indicate a clear superiority of one factor over the other. Position Statement Catherine Plaisant Is an airplane a better vehicle than a Jaguar, a mountain bicycle or a kid scooter? It all depends of where you need to go, what your goal for the travel is, how old you are, what terrain you will encounter on the way, how long you can spend learning, and many other parameters. All those vehicles are fairly usable but they all require training except for adults using the scooter, and their utility varies enormously as a function of the task and the user. The average car drivers benefit from years of human factor engineering and a large amount of standardization, allowing them to switch from a pickup truck to a convertible in a snap. Similarly, the success of a visualization tool depends on how well it fits the needs of the users it attempts to serve, and the tasks they want to accomplish. If utility may come first for an expert tool (e.g. for discovery tasks requires days of data examination and manipulation), has to come first in public access information systems that requires immediate usability (e.g. interactive displays of census statistics) otherwise users will walk away frustrated. Usability design principles imply that designers and evaluators understand the needs of users to decide which one of the two utility or comes first, and to set levels of required utility and usability. Utility and are both attainable goals that make each other stronger. Like others, we at the University of Maryland have been developing visualization techniques and have struggled over the years to find the best way to evaluate their benefits. Many evaluations have been controlled experiments and we have found that the most useful evaluations were multi-faceted, including qualitative and quantitative measurements or performance, preference and learnability, and I will be show examples from our research. Often we also find that the observations gathered during the experiment can be as informative as the collected measurements. More recently we have been promoting the development of benchmark datasets and tasks that will allow better comparisons between tools and techniques. We have been involved in the 1st InfoVis contest, which calls for the submission of case studies of pairwise comparison of trees. Three pairs of datasets were provided: philogenies, classifications and file system usage data, 605 Proceedings of the 14th IEEE Visualization Conference (VIS’03) 0-7695-2030-8/03 $ 17.00 © 2003 IEEE and open ended tasks described. Accepted submissions will seed an online repository that can be enhanced over time with additional datasets, tasks, case studies and controlled experiment results. I will report on the results of the contest and reflect on how evaluation repository and benchmark datasets might help us understand how to judge the and utility of our tools. The contest is at: http://www.cs.umd.edu/hcil/iv03contest/ Position Statement Ben Shneiderman The answer to the question of or utility first depends on your definition of usability. For me, is more than the color of widgets and placement of text. Usability is about understanding, stating, and serving user needs. Since these needs are the requirements that shape the tool, they determine the utility. The design of excellent tools depends upon understanding how they will be used; therefore is a pre-requisite for successful utility. Position Statement John T. Stasko If the question is, Which comes first, utility or usability? my answer is yes. Both notions are vitally important in the development of information visualization techniques and systems, and they are just two sides of the same coin. When the field of information visualization formed and first grew, the computer graphics and visualization aspects dominated. More recently, an increasing emphasis on the HCI aspects of the field has emerged as we strive to better understand how people can truly benefit from our ideas. When an information visualization technique is implemented in a system, the component is crucial. Poor interface design can hamper adoption and cloud the utility benefits that may be possible. In information visualization, typically does not equate with the common notion of a system being easy to learn. Information visualization systems are complex and they often will be used extensively for long periods of time. Consequently, making a system efficient and natural to use, making functions and operations visible, and simply paying attention to the user interface are key components. This is especially true in systems where interaction and multiple views are essential. For any information visualization technique to be adopted beyond the initial idea generation, there must be some utility or value in that technique. Our field is not about making pretty pictures. It is about helping people with the complex tasks involved in data analysis and understanding. We need to do a better job of articulating the cognitive tasks that occur in data analysis (location, correlation, emphasis, association, etc.) and articulating how information visualization techniques and systems can help with these tasks. Ultimately, a kind of natural selection will occur: systems with utility and value will be adopted and used, while others will quietly fade away.

The Internet and World Wide Web have made a tremendous amount of information available to people today. Taking advantage of and managing this information, however, is becoming increasingly challenging due to its volume and the variety of sources available. We attempt to reduce this overload with the InfoCanvas, an ambient display of a personalized, information-driven, visual collage. Through a web-based interface, people identify information of interest, associate a pictorial representation with it, and place the representation on a virtual canvas. The end result is an information collage, displayed on a secondary monitor or net appliance, that allows people to keep tabs on information in a calm, unobtrusive manner. This paper presents details on how a person can create and manage information with the InfoCanvas, and how we provide such capabilities.

La gloria no consiste en no caer nunca, sino más bien en levantarse las veces que sea necesario."

One of the most expensive and time-consuming components of the debugging process is locating the errors or faults. To locate faults, developers must identify statements involved in failures and select suspicious statements that might contain faults. This paper presents a new technique that uses visualization to assist with these tasks. The technique uses color to visually map the participation of each program statement in the outcome of the execution of the program with a test suite, consisting of both passed and failed test cases. Based on this visual mapping, a user can inspect the statements in the program, identify statements involved in failures, and locate potentially faulty statements. The paper also describes a prototype tool that implements our technique along with a set of empirical studies that use the tool for evaluation of the technique. The empirical studies show that, for the subject we studied, the technique can be effective in helping a user locate faults in a program.

We hope to alert attendees of this panel to a number of aspects of CS education research:• previous work that provides good models for future research;• current projects and results;• areas that deserve more inquiry;• questions for which research is unlikely at the moment to yield useful information.The panel is aimed at people who don't need to be convinced about the value of CS education research, but who perhaps are unfamiliar with what's happening or how they might get involved themselves.

Article Evaluating image filtering based techniques in media space applications Share on Authors: Qiang Alex Zhao Graphics, Visualisation, and Usability Center, Georgia institute of Technology, Atlanta GA Graphics, Visualisation, and Usability Center, Georgia institute of Technology, Atlanta GAView Profile , John T. Stasko Graphics, Visualisation, and Usability Center, Georgia institute of Technology, Atlanta GA Graphics, Visualisation, and Usability Center, Georgia institute of Technology, Atlanta GAView Profile Authors Info & Claims CSCW '98: Proceedings of the 1998 ACM conference on Computer supported cooperative workNovember 1998 Pages 11–18https://doi.org/10.1145/289444.289450Published:01 November 1998 36citation533DownloadsMetricsTotal Citations36Total Downloads533Last 12 Months14Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

We present the Mocha wide area computing infrastructure we are developing. Mocha provides support for robust shared objects on heterogeneous platforms, and utilizes advanced distributed shared memory techniques for maintaining consistency of shared objects that are replicated at multiple nodes to improve performance. In addition, our system handles failures that we feel will be common in wide area environments. We have used an approach that makes use of multiple communication protocols to improve the efficiency of shared object state transfers in Mocha. We also provide an empirical evaluation of our prototype implementation for local area, wide area, and home service networks and present a sample home service application that has been programmed with the system.

Network computing has evolved into a popular and effective mode of high performance computing. Network computing environments have fundamental differences from hardware multiprocessors, involving a different approach to measuring and characterizing performance, monitoring an application's progress and understanding program behavior. In this paper, we present the design and implementation of PVaniM, an experimental visualization environment we have developed for the PVM network computing system. PVaniM supports a two-phase approach whereby on-line visualization focuses on large-grained events that are influenced by and relate to the dynamic network computing environment, and postmortem visualization provides for detailed program analysis and tuning. PVaniM's capabilities are illustrated via its use on several applications and a comparison with single-phase visualization environments developed for network computing. Our experiences indicate that, for several classes of applications, the two-phase visualization scheme can provide valuable insight into the behavior, efficiency and operation of distributed and parallel programs in network computing environments. © 1998 John Wiley & Sons, Ltd.

Information visualizations must allow users to browse information spaces and focus quickly on items of interest. Being able to see some representation of the entire information space provides an initial gestalt overview and gives context to support browsing and search tasks. However, the limited number of pixels on the screen constrain the information bandwidth and make it difficult to completely display large information spaces. The Information Mural is a two-dimensional, reduced representation of an entire information space that fits entirely within a display window or screen. The Mural creates a miniature version of the information space using visual attributes, such as gray-scale shading, intensity, color, and pixel size, along with antialiased compression techniques. Information Murals can be used as stand-alone visualizations or in global navigational views. We have built several prototypes to demonstrate the use of Information Murals in visualization applications; subject matter for these views includes computer software, scientific data, text documents and geographic information.

Implementing, validating, modifying, or reengineering an object-oriented system requires an understanding of the object and class interactions which occur as a program executes.This work seeks to identify, visualize, and analyze interactions in object-oriented program executions as a means for examining and understanding dynamic behavior.We have discovered recurring interaction scenarios in program executions that can be used as abstractions in the understanding process, and have developed a means for identifying these interaction patterns.Our visualizations focus on supporting design recovery, validation, and reengineering tasks, and can be applied to both object-oriented and procedural programs.

The typical application of algorithm animation to assist instruction involves students viewing already prepared animations. An alternative strategy is to have the students themselves construct animations of algorithms. The Samba algorithm animation tool fosters such student-built animations. Samba was used in an undergraduate algorithms course in which students constructed algorithm animations as regular class assignments. This article describes Samba and documents our experiences using it in the algorithms course. Student reaction to the animation assignments was very positive, and the students appeared to learn the pertinent algorithms extremely well.

The wide accessibility of the World Wide Web makes it a perfect base for developing computer science courseware modules. Since learning involves more than just receiving transmitted information, courseware must be interactive and encourage student engagement, which is a challenge on the Web architecture. This article describes an ongoing effort to develop World Wide Web-based computer science courseware modules that will use interactive components as integral parts of the material, in order to promote student involvement. It also discusses the proposed usage of new technology such as HotJava in this framework.

Visualization and animation tools may become extremely important aids in the understanding, verification, and performance tuning of parallel computations. Presently, however, the use of visualization has had only a limited use for enhancing parallel computation. We hypothesize that one of the primary reasons for the limited use of visualization tools in parallel program development is the difficulty of acquiring the information necessary to drive the visual display. Our approach to this impediment focuses on integrating visualization support directly into a distributed computing system. Central to this integration is the addition of a logical clock that prevents the timestamps of events from violating causality. The implementation requires the "piggybacking" of a negligible amount of extra header information on system messages and the impact on performance is minimal. This results in a system that produces useful visualizations with no extra effort required by the applications programmer. Also integrated into the distributed system is support which simplifies the creation of programmer-defined, application-specific visualizations, unique to each new parallel program developed.

No abstract available.

Icons are used widely in human-computer interfaces. The level of abstractness-concreteness of an icon and its effect upon performance is of widespread interest. The authors have devised a quantitative measure for abstractness based on the complexity of the icon. They test their metric against subjective judgments of abstractness as identified by two different groups of subjects. After ranking two sets of ‘abstract’ and ‘concrete’ icons, the authors examined how well the icons were matched to the Pascal constructs that they represented. Further experiments were conducted using different groups of subjects to check whether correct matching of the icons with constructs was influenced by context. In summary the authors found that their metric was a good match for subjective measures of abstractness-concreteness. They also found that there is a better identification of concrete icons than abstract icons. Finally, it was shown that context does affect the correct identification of icons.

Much of the recent research in software visualization has been polarized toward two opposite domains. In one domain that we call data structure and program visualization , low-level canonical views of program structures are generated automatically. These types of views, which do not require programmer input or intervention, can be useful for testing and debugging software. Often, however, their generic, low-level views are not expressive enough to convey adequately how a program functions. In the second domain called algorithm animation , designers handcraft abstract, application-specific views that are useful for program understanding and teaching. Unfortunately, since algorithm animation development typically requires time-consuming design with a graphics package, it will not be used for debugging, where timeliness is a necessity. However, we speculate that the application-specific nature of algorithm animation views could be a valuable debugging aid for software developers as well, if only the views could be easy and rapid to create. We have developed a system called Lens that occupies a unique niche between the two domains discussed above and explores the capabilities that such a system may offer. Lens allows programmers to build rapidly (in minutes) algorithm animation-style program views without requiring any sophisticated graphics knowledge and without using textual coding. Lens also is integrated with a system debugger to promote iterative design and exploration.

Article Animation support in a user interface toolkit: flexible, robust, and reusable abstractions Share on Authors: Scott E. Hudson Graphics Visualization and Usability Center, College of Computing, Georgia Institute of Technology, Atlanta, GA Graphics Visualization and Usability Center, College of Computing, Georgia Institute of Technology, Atlanta, GAView Profile , John T. Stasko Graphics Visualization and Usability Center, College of Computing, Georgia Institute of Technology, Atlanta, GA Graphics Visualization and Usability Center, College of Computing, Georgia Institute of Technology, Atlanta, GAView Profile Authors Info & Claims UIST '93: Proceedings of the 6th annual ACM symposium on User interface software and technologyDecember 1993 Pages 57–67https://doi.org/10.1145/168642.168648Online:01 December 1993Publication History 51citation1,798DownloadsMetricsTotal Citations51Total Downloads1,798Last 12 Months30Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Article Using direct manipulation to build algorithm animations by demonstration Share on Author: John T. Stasko College of Computing, Georgia Institute of Technology, Atlanta, GA College of Computing, Georgia Institute of Technology, Atlanta, GAView Profile Authors Info & Claims CHI '91: Proceedings of the SIGCHI Conference on Human Factors in Computing SystemsApril 1991 Pages 307–314https://doi.org/10.1145/108844.108930Online:01 March 1991Publication History 41citation462DownloadsMetricsTotal Citations41Total Downloads462Last 12 Months8Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access