Abstract: We present a novel approach to visualization recommendation that monitors user behavior for implicit signals of user intent to provide more effective recommendation. This is in contrast to previous approaches which are either insensitive to user intent or require explicit, user specified task information. Our approach, called Behavior-Driven Visualization Recommendation (BDVR), consists of two distinct phases: (1) pattern detection, and (2) visualization recommendation. In the first phase, user behavior is analyzed dynamically to find semantically meaningful interaction patterns using a library of pattern definitions developed through observations of real-world visual analytic activity. In the second phase, our BDVR algorithm uses the detected patterns to infer a user's intended visual task. It then automatically suggests alternative visualizations that support the inferred visual task more directly than the user's current visualization. We present the details of BDVR and describe its implementation within our lab's prototype visual analysis system. We also present study results that demonstrate that our approach shortens task completion time and reduces error rates when compared to behavior-agnostic recommendation.

Abstract: Principle Component Analysis (PCA) is a widely used mathematical technique in many fields for factor and trend analysis, dimension reduction, etc. However, it is often considered to be a "black box" operation whose results are difficult to interpret and sometimes counter-intuitive to the user. In order to assist the user in better understanding and utilizing PCA, we have developed a system that visualizes the results of principal component analysis using multiple coordinated views and a rich set of user interactions. Our design philosophy is to support analysis of multivariate datasets through extensive interaction with the PCA output. To demonstrate the usefulness of our system, we performed a comparative user study with a known commercial system, SAS/INSIGHT's Interactive Data Exploration. Participants in our study solved a number of high-level analysis tasks with each interface and rated the systems on ease of learning and usefulness. Based on the participants' accuracy, speed, and qualitative feedback, we observe that our system helps users to better understand relationships between the data and the calculated eigenspace, which allows the participants to more accurately analyze the data. User feedback suggests that the interactivity and transparency of our system are the key strengths of our approach.

Abstract: Insight provenance - a historical record of the process and rationale by which an insight is derived - is an essential requirement in many visual analytics applications. Although work in this area has relied on either manually recorded provenance (for example, user notes) or automaticalily recorded event-based insight provenance (for example, clicks, drags and key-presses), both approaches have fundamental limitations. Our aim is to develop a new approach that combines the benefits of both approaches while avoiding their deficiencies. Toward this goal, we characterize users' visual analytic activity at multiple levels of granularity. Moreover, we identify a critical level of abstraction, Actions, that can be used to represent visual analytic activity, with a set of general but semantically meaningful behavior types. In turn, the action types can be used as the semantic building blocks for insight provenance. We present a catalog of common actions identified through observations of several different visual analytic systems. In addition, we define a taxonomy to categorize actions into three major classes based on their semantic intent. The concept of actions has been integrated into our lab's prototype visual analytic system, HARVEST, as the basis for its insight provenance capabilities.

Abstract: In this state-of-the-art report we discuss relevant research works related to the visualization of complex, multivariate data. We discuss how different techniques take effect at specific stages of the visualization pipeline and how they apply to multi-variate data sets being composed of scalars, vectors and tensors. We also provide a categorization of these techniques with the aim for a better overview of related approaches. Based on this classification we highlight combinable and hybrid approaches and focus on techniques that potentially lead towards new directions in visualization research. In the second part of this paper we take a look at recent techniques that are useful for the visualization of complex data sets either because they are general purpose or because they can be adapted to specific problems.

Abstract: Many real-world analysis tasks can benefit from the combined efforts of a group of people. Past research has shown that to design visualizations for collaborative visual analytics tasks, we need to support both individual as well as joint analysis activities. We present Cambiera, a tabletop visual analytics tool that supports individual and collaborative information foraging activities in large text document collections. We define collaborative brushing and linking as an awareness mechanism that enables analysts to follow their own hypotheses during collaborative sessions while still remaining aware of the group's activities. With Cambiera, users are able to collaboratively search through documents, maintaining awareness of each others' work and building on each others' findings.

Abstract: Understanding how analysts use visual-analytics (VA) tools can help reveal their reasoning processes when using these tools. By examining analysts' interaction logs, the authors identified the analysts' strategies, methods, and findings when using a financial VA tool.

Abstract: 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 we 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 for metrics and techniques for evaluating other visual analytics investigative analysis tools.

Abstract: The application of information visualization holds tremendous promise for the electric power industry, but its potential has so far not been sufficiently exploited by the visualization community. Prior work on visualizing electric power systems has been limited to depicting raw or processed information on top of a geographic layout. Little effort has been devoted to visualizing the physics of the power grids, which ultimately determines the condition and stability of the electricity infrastructure. Based on this assessment, we developed a novel visualization system prototype, GreenGrid, to explore the planning and monitoring of the North American Electricity Infrastructure. The paper discusses the rationale underlying the GreenGrid design, describes its implementation and performance details, and assesses its strengths and weaknesses against the current geographic-based power grid visualization. We also present a case study using GreenGrid to analyze the information collected moments before the last major electric blackout in the Western United States and Canada, and a usability study to evaluate the practical significance of our design in simulated real-life situations. Our result indicates that many of the disturbance characteristics can be readily identified with the proper form of visualization.

Abstract: As display technology continues to improve, there will be an increasing diversity in the available display form factors and scales. Empirical evaluation of how display attributes affect user perceptions and performance can help designers understand the strengths and weaknesses of different display forms, provide guidance for effectively designing multiple display environments, and offer initial evidence for developing theories of ubiquitous display. Although previous research has shown user performance benefits when tiling multiple monitors to increase the number of pixels, little research has analyzed the performance and behavioral impacts of the form factors of much larger, high-resolution displays. This article presents two experiments in which user performance was evaluated on a high-resolution (96 DPI), high pixel-count (approximately 32 million pixels) display for single-user scenarios in both flat and curved forms. We show that for geospatial visual analytics tasks there is a benefit to la...

Abstract: Visual analytics has seen unprecedented growth in its first 5 years of mainstream existence. Great progress has been made in a short time, yet significant challenges must be met in the next decade to provide new technologies that will be widely accepted throughout the world. This article explains some of those challenges in an effort to provide a stimulus for research, both basic and applied, that can realize or even exceed the potential envisioned for visual analytics technologies. We start with a brief summary of the initial challenges, followed by a discussion of the initial driving domains and applications. These are followed by a selection of additional applications and domains that have been a part of recent rapid expansion of visual analytics usage. We then look at the common characteristics of several tools illustrating emerging visual analytics technologies and conclude with the top 10 challenges for the field of study. We encourage feedback and continued participation by members of the research community, the wide array of user communities and private industry.

Abstract: Summary: We introduce a new visual analytics tool named MapView to facilitate the representation of large-scale short reads alignment data and genetic variation analysis. MapView can handle hundreds of millions of short reads on a desktop computer with limited memory. It supports a compact alignment view for both single-end and paired end short reads, multiple navigation and zoom modes and multithread processing. Moreover, MapView offers automated genetic variation detection. MapView has been used in our lab and by over

Abstract: FinVis is a visual analytics tool that allows the non-expert casual user to interpret the return, risk and correlation aspects of financial data and make personal finance decisions. This interactive exploratory tool helps the casual decision-maker quickly choose between various financial portfolio options and view possible outcomes. FinVis allows for exploration of inter-temporal data to analyze outcomes of short-term or long-term investment decisions. FinVis helps the user overcome cognitive limitations and understand the impact of correlation between financial instruments in order to reap the benefits of portfolio diversification. Because this software is accessible by non-expert users, decision-makers from the general population can benefit greatly from using FinVis in practical applications. We quantify the value of FinVis using experimental economics methods and find that subjects using the FinVis software make better financial portfolio decisions as compared to subjects using a tabular version with the same information. We also find that FinVis engages the user, which results in greater exploration of the dataset and increased learning as compared to a tabular display. Further, participants using FinVis reported increased confidence in financial decision-making and noted that they were likely to use this tool in practical application.

Abstract: Understanding information visualization is more than a matter of reading a series of data values; it is also a matter of incorporating a visual structure into one's own thinking about a problem. We have proposed visual metaphors as a framework for understanding high-level visual structure and its effect on visualization use. Although there is some evidence that visual metaphors can affect visualization use, the nature of this effect is still ambiguous. We propose that a user's preconceived metaphors for data and other individual differences play an important role in her ability to think in a variety of visual metaphors, and subsequently in her ability to use a visualization. We test this hypothesis by conducting a study in which a participant's preconceptions and thinking style were compared with the degree to which she is affected by conflicting metaphors in a visualization and its task questions. The results show that metaphor compatibility has a significant effect on accuracy, but that factors such as spatial ability and personality can lessen this effect. We also find a complex influence of self-reported metaphor preference on performance. These findings shed light on how people use visual metaphors to understand a visualization.

Abstract: When using scientific literature to model scholarly discourse, a research specialty can be operationalized as an evolving set of related documents. Each publication can be expected to contribute to the further development of the specialty at the research front. The specific combinations of title words and cited references in a paper can then be considered as a signature of the knowledge claim in the paper: New words and combinations of words can be expected to represent variation, while each paper is at the same time selectively positioned into the intellectual organization of a field using context-relevant references. Can the mutual information among these three dimensions—title words, cited references, and sequence numbers—be used as an indicator of the extent to which intellectual organization structures the uncertainty prevailing at a research front? The effect of the discovery of nanotubes (1991) on the previously existing field of fullerenes is used as a test case. Thereafter, this method is applied to science studies with a focus on scientometrics using various sample delineations. An emerging research front about citation analysis can be indicated. © 2009 Wiley Periodicals, Inc.

Abstract: Although significant progress has been made toward effective insight discovery in visual sense making approaches, there is a lack of effective and efficient approaches to manage the large amounts of insights discovered. In this paper, we propose a systematic approach to leverage this problem around the concept of facts. Facts refer to patterns, relationships, or anomalies extracted from data under analysis. They are the direct products of visual exploration and permit construction of insights together with user's mental model and evaluation. Different from the mental model, the type of facts that can be discovered from data is predictable and application-independent. Thus it is possible to develop a general Fact Management Framework (FMF) to allow visualization users to effectively and efficiently annotate, browse, retrieve, associate, and exchange facts. Since facts are essential components of insights, it will be feasible to extend FMF to effective insight management in a variety of visual analytics approaches. Toward this goal, we first construct a fact taxonomy that categorizes various facts in multidimensional data and captures their essential attributes through extensive literature survey and user studies. We then propose a conceptual framework of fact management based upon this fact taxonomy. A concrete scenario of visual sense making on real data sets illustrates how this FMF will work.

Abstract: A data visualization interactivity architecture may be provided. The architecture may allow the creation of a data visualization, such as a chart, and may expose an interactive feature on the visualization. The architecture may provide integration with multiple rendering platforms. When a user selects the exposed feature, the architecture may translate the selection into a common format and modify the data visualization according to layout rules independent of the rendering platform.

Abstract: The technology behind RSS feeds offers great possibilities to retrieve more news items than ever In contrast to these technical developments, human capabilities to read all these news items have not increased likewise To bridge this gap, this paper presents a visual analytics tool for conducting semi-automatic sentiment analysis of large news feeds While the tool automatically retrieves and analyzes RSS feeds with respect to positive and negative opinion words, the more demanding news analysis of finding trends, spotting peculiarities and putting events into context is left to the human expert For a solid analysis the news similarity filter enables highlighting of similar or redundant news items A case study about news related to the US presidential election in 2008 shows how the visual interface of the tool empowers the analyst to draw meaningful conclusions without the effort of reading all news postings Author Keywords sentiment analysis, opinion mining, information visualization, visual analytics

Abstract: In the field of agent-based modeling (ABM), visualizations play an important role in identifying, communicating and understanding important behavior of the modeled phenomenon. However, many modelers tend to create ineffective visualizations of Agent Based Models (ABM) due to lack of experience with visual design. This paper provides ABM visualization design guidelines in order to improve visual design with ABM toolkits. These guidelines will assist the modeler in creating clear and understandable ABM visualizations. We begin by introducing a non-hierarchical categorization of ABM visualizations. This categorization serves as a starting point in the creation of an ABM visualization. We go on to present well-known design techniques in the context of ABM visualization. These techniques are based on Gestalt psychology, semiology of graphics, and scientific visualization. They improve the visualization design by facilitating specific tasks, and providing a common language to critique visualizations through the use of visual variables. Subsequently, we discuss the application of these design techniques to simplify, emphasize and explain an ABM visualization. Finally, we illustrate these guidelines using a simple redesign of a NetLogo ABM visualization. These guidelines can be used to inform the development of design tools that assist users in the creation of ABM visualizations.

Abstract: Multidimensional Visualization techniques are invaluable tools for analysis of structured and unstructured data with variable dimensionality. This paper introduces PEx-Image—Projection Explorer for Images—a tool aimed at supporting analysis of image collections. The tool supports a methodology that employs interactive visualizations to aid user-driven feature detection and classification tasks, thus offering improved analysis and exploration capabilities. The visual mappings employ similarity-based multidimensional projections and point placement to layout the data on a plane for visual exploration. In addition to its application to image databases, we also illustrate how the proposed approach can be successfully employed in simultaneous analysis of different data types, such as text and images, offering a common visual representation for data expressed in different modalities.

Abstract: Transportation systems are being monitored at an unprecedented scope, which is resulting in tremendously detailed traffic and incident databases. Although the transportation community emphasizes developing standards for storing these incident data, little effort has been made to design appropriate visual analytics tools to explore the data, extract meaningful knowledge, and represent results. Analyzing these large multivariate geospatial data sets is a nontrivial task. A novel, web-based, visual analytics tool called Fervor is proposed as an application that affords sophisticated, yet user-friendly, analysis of transportation incident data sets. Interactive maps, histograms, two-dimensional plots, and parallel coordinates plots are four featured visualizations that are integrated to allow users to interact simultaneously with and see relationships among multiple visualizations. Using a rich set of filters, users can create custom conditions to filter data and focus on a smaller data set. However, because of the multivariate nature of the data, finding interesting relationships can be a time-consuming task. Therefore, a rank-by-feature framework has been adopted and further expanded to quantify the strength of relationships among the different fields describing the data. In this paper, transportation incident data collected by the Maryland State Highway Administration's CHART program are used; however, the tool can be easily modified to accept other transportation data sets.

Abstract: This introduction and future vision section for this special issue of the Journal of Information Visualization hopes to set the stage for an emerging worldwide effort to advance the state of the science of visual analytics. We present some of the driving needs followed by some foundational principals and methods for advancing this science through partnerships among national laboratories, academia, industry, and the international science community. We will present a selection of the many success stories the science, engineering, and industrial communities have of taking core science research to end users in the field during these early years. Next, we will present some thoughts on the future vision. Finally, we will introduce the 8 papers in this special issue, each one addressing part of that vision.

Abstract: With a literature study we found an enormous number of ontology visualization tools. Many of them apply graph visualization but there are other approaches as well. We have identified interesting solutions for dealing with the complexity of large ontologies. Ontology engineering, ontology mapping and alignment can benefit from Information Visualization. Our collection is a starting point to demonstrate the usefulness of Information Visualization techniques, however, a detailed evaluation would be the next step to consolidate this research area and help to boost the adoption of ontologies in common Web applications.

Abstract: There has been progress in the science of analytical reasoning and in meeting the recommendations for future research that were laid out when the field of visual analytics was established. Researchers have also developed a group of visual analyties tools and methods that embody visual analytics principles and attack important and challenging real-world problems. However, these efforts are only the beginning and much study remains to be done. This article examines the state of the art in visual analytics methods and reasoning and gives examples of current tools and capabilities. It shows that the science of visual analytics needs interdisciplinary efforts, indicates some of the disciplines that should be involved and presents an approach to how they might work together. Finally, the article describes some gaps, opportunities and future directions in developing new theories and models that can be enacted in methods and design principles and applied to significant and complex practical problems and data.

Abstract: User testing is an integral component of user-centered design, but has only rarely been applied to visualization for cyber security applications This paper describes a comparative evaluation of a visualization application and a traditional interface for analyzing network packet captures, that was conducted as part of the user-centered design process Structured, well-defined tasks and exploratory, open-ended tasks were completed with both tools Accuracy and efficiency were measured for the well-defined tasks, number of insights was measured for exploratory tasks and user perceptions were recorded for each tool The results of this evaluation demonstrated that users performed significantly more accurately in the well-defined tasks, discovered a higher number of insights and demonstrated a clear preference for the visualization tool The study presented here may be useful for future visualization for network security visualization evaluation designers Some of the challenges and lessons learned are described

Abstract: The role of semantic information systems in today's enterprises is manifold: It ranges from the support of day-to-day operations up to the level of strategic management and business decision-making. The use of visualization techniques, therefore, marks an important aspect of these systems. Hans-Georg Fill presents an innovative framework for visualization based on an analysis of current visualization approaches in business informatics. It encompasses the creation of visualizations both from a technical as well as a contextual point of view. The author in particular elaborates the concepts of visual objects, ontological visualization patterns and semantic visualization. These allow for the integration of aspects of service orientation into the visualization process and the semantic-based selections of visualizations for particular tasks.

Abstract: Patents are an important economic factor in todays globalized markets. Therefore, the analysis of patent information has become an inevitable task for a variety of interest groups. The retrieval of relevant patent information is an integral part of almost every patent analysis scenario. Unfortunately, the complexity of patent material inhibits a straightforward retrieval of all relevant patent documents and leads to iterative, time-consuming approaches in practice. With ‘PatViz’, a new system for interactive analysis of patent information has been developed to leverage iterative query refinement. PatViz supports users in building complex queries visually and in exploring patent result sets interactively. Thereby, the visual query module introduces an abstraction layer that provides uniform access to different retrieval systems and relieves users of the burden to learn different complex query languages. By establishing an integrated environment it allows for interactive reintegration of insights gained from visual result set exploration into the visual query representation. We expect that the approach we have taken is also suitable to improve iterative query refinement in other Visual Analytics systems.

Abstract: The fundamental problem that we face is that a variety of large-scale problems in security, public safety, energy, ecology, health care and basic science all require that we process and understand increasingly vast amounts and variety of data. There is a growing impedance mismatch between data size/complexity and the human ability to understand and interact with data. Visual analytic tools are intended to help reduce that impedance mismatch by using analytic tools to reduce the amount of data that must be viewed, and visualization tools to help understand the patterns and relationships in the reduced data. But visual analytic tools must address a variety of scalability issues if they are to succeed. In this paper, we characterize the scalability and complexity issues in visual analytics. We discuss some highlights on progress that has been made in the past 5 years, as well as key areas where more progress is needed.