Showing papers in "Information Visualization in 2009"

The science of interaction

Abstract: There is a growing recognition within the visual analytics community that interaction and inquiry are inextricable. It is through the interactive manipulation of a visual interface - the analytic discourse - that knowledge is constructed, tested, refined and shared. This article reflects on the interaction challenges raised in the visual analytics research and development agenda and further explores the relationship between interaction and cognition. It identifies recent exemplars of Visual analytics research that have made substantive progress toward the goals of a true science of interaction, which must include theories and testable premises about the most appropriate mechanisms for human-information interaction. Seven areas for further work are highlighted as those among the highest priorities for the next 5 years of visual analytics research: ubiquitous, embodied interaction; capturing user intentionality; knowledge-based interfaces; collaboration; principles of design and perception; interoperability; and interaction evaluation. Ultimately, the goal of a science of interaction is to support the visual analytics and human-computer interaction communities through the recognition and implementation of best practices in the representation and manipulation of visual displays.

Characterizing users' visual analytic activity for insight provenance

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.

Measuring effectiveness of graph visualizations: a cognitive load perspective

Abstract: Graph visualizations are typically evaluated by comparing their differences in effectiveness, measured by task performance such as response time and accuracy. Such performance-based measures have proved to be useful in their own right. There are some situations, however, where the performance measures alone may not be sensitive enough to detect differences. This limitation can be seen from the fact that the graph viewer may achieve the same level of performance by devoting different amounts of cognitive effort. In addition, it is not often that individual performance measures are consistently in favor of a particular visualization. This makes design and evaluation difficult in choosing one visualization over another. In an attempt to overcome the above-mentioned limitations, we measure the effectiveness of graph visualizations from a cognitive load perspective. Human memory as an information processing system and recent results from cognitive load research are reviewed first. The construct of cognitive load in the context of graph visualization is proposed and discussed. A model of user task performance, mental effort and cognitive load is proposed thereafter to further reveal the interacting relations between these three concepts. A cognitive load measure called mental effort is introduced and this measure is further combined with traditional performance measures into a single multi-dimensional measure called visualization efficiency. The proposed model and measurements are tested in a user study for validity. Implications of the cognitive load considerations in graph visualization are discussed.

Visual cluster analysis of trajectory data with interactive Kohonen maps

Abstract: Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Owing to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on several trajectory clustering problems, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results.

Design Aspects of the VLBI2010 System

Abstract: This report summarizes the progress made in developing the next generation VLBI, dubberd the VLBI2010 system. The VLBI2010 Committee of the International Service for Geodesy and Astrometry (IVS) worked on the design aspects of the new system.…

Building and applying a human cognition model for visual analytics

Abstract: It is well known that visual analytics addresses the difficulty of evaluating and processing large quantities of information. Less often discussed are the increasingly complex analytic and reasoning processes that must be applied in order to accomplish that goal. Success of the visual analytics approach will require us to develop new visualization models that predict how computational processes might facilitate human insight and guide the flow of human reasoning. In this paper, we seek to advance visualization methods by proposing a framework for human 'higher cognition' that extends more familiar perceptual models. Based on this approach, we suggest guidelines for the development of visual interfaces that better integrate complementary capabilities of humans and computers. Although many of these recommendations are novel, some can be found in existing visual analytics applications. In the latter case, much of the value of our contribution lies in the deeper rationale that the model provides for those principles. Lastly, we assess these visual analytics guidelines through the evaluation of several visualization examples.

Challienges for visual analytics

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.

Adaptive visualization of search results: bringing user models to visual analytics

Abstract: Adaptive visualization is a new approach at the crossroads of user modeling and information visualization. Taking into account information about a user, adaptive visualization attempts to provide user-adapted visual presentation of information. This paper proposes Adaptive VIBE, an approach for adaptive visualization of search results in an intelligence analysis context. Adaptive VIBE extends the popular VIBE visualization framework by infusing user model terms as reference points for spatial document arrangement and manipulation. We explored the value of the proposed approach using data obtained from a user study. The result demonstrated that user modeling and spatial visualization technologies are able to reinforce each other, creating an enhanced level of user support. Spatial visualization amplifies the user model's ability to separate relevant and non-relevant documents, whereas user modeling adds valuable reference points to relevance-based spatial visualization.

Foundations and frontiers in visual analytics

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.

Science of analytical reasoning

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.

What does the user want to see?: what do the data want to be?

Abstract: Information visualization is a user-centered design discipline. In this article we argue, however, that designing information visualization techniques often requires more than designing for user requirements. Additionally, the data that are to be visualized must also be carefully considered. An approach based on both the user and their data is encapsulated by two questions, which we argue information visualization designers should continually ask themselves: 'What does the user want to see?' and 'What do the data want to be?' As we show by presenting cases, these two points of departure are mutually reinforcing. By focusing on the data, new insight is gained into the requirements of the user, and vice versa, resulting in more effective visualization techniques.

Scale and complexity in visual analytics

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.

Visual analysis of dynamic data streams

Abstract: For scientific data visualizations, real-time data streams present many interesting challenges when compared to static data. Real-time data are dynamic, transient, high-volume and temporal. Effective visualizations need to be able to accommodate dynamic data behavior as well as abstract and present the data in ways that make sense to and are usable by humans. The Visual Content Analysis of Real-Time Data Streams project at the Pacific Northwest National Laboratory is researching and prototyping dynamic visualization techniques and tools to help facilitate human understanding and comprehension of high-volume, real-time data. The general strategy of the project is to develop and evolve visual contexts that will organize and orient high-volume dynamic data in conceptual and perceptive views. The goal is to allow users to quickly grasp dynamic data in forms that are intuitive and natural without requiring intensive training in the use of specific visualization or analysis tools and methods. Thus far, the project has prototyped five different visualization prototypes that represent and convey dynamic data through human-recognizable contexts and paradigms such as hierarchies, relationships, time and geography. We describe the design considerations and unique features of these dynamic visualization prototypes as well as our findings in the exploration and evaluation of their use.

The scalable reasoning system: lightweight visualization for distributed analytics

Abstract: A central challenge in visual analytics is the creation of accessible, widely distributable analysis applications that bring the benefits of visual discovery to as broad a user base as possible. Moreover, to support the role of visualization in the knowledge creation process, it is advantageous to allow users to describe the reasoning strategies they employ while interacting with analytic environments. We introduce an application suite called the scalable reasoning system (SRS), which provides web-based and mobile interfaces for visual analysis. The service-oriented analytic framework that underlies SRS provides a platform for deploying pervasive visual analytic environments across an enterprise. SRS represents a 'lightweight' approach to visual analytics whereby thin client analytic applications can be rapidly deployed in a platform-agnostic fashion. Client applications support multiple coordinated views while giving analysts the ability to record evidence, assumptions, hypotheses and other reasoning artifacts. We describe the capabilities of SRS in the context of a real-world deployment at a regional law enforcement organization.

Seeing is believing: linking data with knowledge

Abstract: The analysis of data using a visual tool is rarely a task done in isolation, it tends to be part of a wider goal: that of making sense of the current situation, often to support decision-making. A user-centred approach is needed in order to properly design interaction that supports sense-making incorporating visual data analysis. This paper reports the experience gained in X-Medla, a project that aims to support knowledge management (KM), sharing and reuse across different media in large enterprises. We report the user-centred design approach adopted and the design phases that led to the first. prototype. A user evaluation was conducted to assess the design and how different levels of data, information and knowledge were mapped using alternative visual tools. The results show that a clear separation of the visual data analysis from other sense-making sub-tasks helps users. in focussing their attention. Users particularly appreciated the data analysIs across different media and formats, as well as the support for contextualising information within the broader perspective of KM. Further work is needed to develop more fully intuitive visualisations that exploit the richer information in multimedia documents and make the multiple connections between data more easily accessible.

Visual data mining of multimedia data for social and behavioral studies

Abstract: With advances in computing techniques, a large amount of high-resolution high-quality multimedia data (video and audio, and so on) has been collected in research laboratories in various scientific disciplines, particularly in cognitive and behavioral studies. How to automatically and effectively discover new knowledge from rich multimedia data poses a compelling challenge because most state-of-the-art data mining techniques can only search and extract pre-defined patterns or knowledge from complex heterogeneous data. In light of this challenge, we propose a hybrid approach that allows scientists to use data mining as a first pass, and then forms a closed loop of visual analysis of current results followed by more data mining work inspired by visualization, the results of which can be in turn visualized and lead to the next round of visual exploration and analysis. In this way, new insights and hypotheses gleaned from the raw data and the current level of analysis can contribute to further analysis. As a first step toward this goal, we implement a visualization system with three critical components: (1) a smooth interface between visualization and data mining; (2) a flexible tool to explore and query temporal data derived from raw multimedia data; and (3) a seamless interface between raw multimedia data and derived data. We have developed various ways to visualize both temporal correlations and statistics of multiple derived variables as well as conditional and high-order statistics. Our visualization tool allows users to explore, compare and analyze multi-stream derived variables and simultaneously switch to access raw multimedia data.

Data transformations and representations for computation and visualization

Abstract: At the core of successful visual analytics systems are computational techniques that transform data into concise, human comprehensible visual representations. The general process often requires multiple transformation steps before a final visual representation is generated. This article characterizes the complex raw data to be analyzed and then describes two different sets of transformations and representations. The first set transforms the raw data into more concise representations that improve the performance of sophisticated computational methods. The second transforms internal representations into visual representations that provide the most benefit to an interactive user. The end result is a computing system that enhances an end user's analytic process with effective visual representations and interactive techniques. While progress has been made on improving data transformations and representations, there is substantial room for improvement.

Advancing user-centered evaluation of visual analytic environments through contests

Abstract: In this paper, the authors describe the Visual Analytics Science and Technology (VAST) Symposium contests run in 2006 and 2007 and the VAST 2008 and 2009 challenges. These contests were designed to provide researchers with a better understanding of the tasks and data that face potential end users. Access to these end users is limited because of time constraints and the classified nature of the tasks and data. In that respect, the contests serve as an intermediary, with the metrics and feedback serving as measures of utility to the end users. The authors summarize the lessons learned and the future directions for VAST Challenges.

Knowledge generation through human-centered information visualization

Abstract: One important intention of human-centered information visualization is to represent huge amounts of abstract data in a visual representation that allows even users from foreign application domains to interact with the visualization, to understand the underlying data, and finally, to gain new, application-related knowledge. The Visualization will help experts as well as non-experts to link previously or isolated knowledge-items in their mental map with new insights. Our approach explicitly supports the process of linking knowledge-items with three concepts. At first, the representation of data items in an ontology categorizes and relates them. Secondly, the use of various visualization techniques visually correlates isolated items by graph-structures, layout, attachment, integration or hyperlink techniques. Thirdly, the intensive use of Visual metaphors relates a known source domain to a less known target domain. In order to realize a scenario of these concepts, we developed a visual interface for non-experts to maintain complex wastewater treatment plants. This domain-specific application is used to give our concepts a meaningful background.

Visualizing large-scale streaming applications

Abstract: Stream processing is a new and important computing paradigm. Innovative streaming applications are being developed in areas ranging from scientific applications (for example, environment monitoring), to business intelligence (for example, fraud detection and trend analysis), to financial markets (for example, algorithmic trading systems). In this paper we describe Streamsight, a new visualization tool built to examine, monitor and help understand the dynamic behavior of streaming applications. Streamsight can handle the complex, distributed and large-scale nature of stream processing applications by using hierarchical graphs, multi-perspective visualizations, and de-cluttering strategies. To address the dynamic and adaptive nature of these applications, Streamsight also provides real-time visualization as well as the capability to record and replay. All these features are used for debugging, for performance optimization, and for management of resources, including capacity planning. More than 100 developers, both inside and outside IBM, have been using Streamsight to help design and implement large-scale stream processing applications.

Using visual analytics to develop situation awareness in astrophysics

Abstract: We present a novel collaborative visual analytics application for cognitively overloaded users in the astrophysics domain. The system was developed for scientists who need to analyze heterogeneous, complex data under time pressure, and make predictions and time-critical decisions rapidly and correctly under a constant influx of changing data. The Sunfall Data Taking system utilizes several novel visualization and analysis techniques to enable a team of geographically distributed domain specialists to effectively and remotely maneuver a custom-built instrument under challenging operational conditions. Sunfall Data Taking has been in production use for 2 years by a major international astrophysics collaboration (the largest data volume supernova search currently in operation), and has substantially improved the operational efficiency of its users. We describe the system design process by an interdisciplinary team, the system architecture and the results of an informal usability evaluation of the production system by domain experts in the context of Endsley's three levels of situation awareness.

The science of analytic reporting

Abstract: The challenge of visually communicating analysis results is central to the ability of visual analytics tools to support decision making and knowledge construction The benefit of emerging visual methods will be improved through more effective exchange of the insights generated through the use of visual analytics This article outlines the major requirements for next-generation reporting systems in terms of eight maior research needs: the development of best practices, design automation, visual rhetoric, context and audience, connecting analysis to presentation, evidence and argument, collaborative environments and interactive and dynamic documents It also describes an emerging technology called Active Products that introduces new techniques for analytic process capture and dissemination

Visual analytics technology transition progress

Abstract: The authors provide a description of the transition process for visual analytic tools and contrast this with the transition process for more traditional software tools. This paper takes this difference into account and describes a user-oriented approach to technology transition including a discussion of key factors that should be considered and adapted to each situation. The progress made in transitioning visual analytic tools in the past 5 years is described and challenges that remain are enumerated.

Eclipse GEF3D: bringing 3D to existing 2D editors

Abstract: In this paper we present the Eclipse project GEF3D. It is a framework for three-dimensional (3D) editors and editors, based on the widely used two-dimensional (2D) graphical editing framework Eclipse Graphical Editing Framework (GEF). It enhances this framework, enabling programmers to easily implement 3D editors. As an Eclipse plugin GEF3D is seamlessly integrated into the Eclipse integrated development environment, allowing developers to work with one tool for developing and visualizing their software in 3D. The third dimension enables the visualization of more complex relationships than provided by existing two-dimensional representations. In this paper we explain the architecture and certain design patterns of GEF3D in order to give researchers and developers interested in 3D software visualization an overview of how to use GEF3D and the features provided by the framework. We present the results of a usability evaluation, show how GEF3D is applied to embed an existing 2D editor into a 3D editor, and discuss performance issues.

Design and evaluation of extensions to UML sequence diagrams for modeling multithreaded interactions

Abstract: Learning about concurrency and synchronization is difficult for novices. Our research seeks to support and improve the teaching and learning of concurrency concepts and to improve comprehension of the intricacies of multiple thread interactions. This paper describes a series of empirical studies in the first phase of our research. We began by conducting a comparative study to empirically evaluate the usability by novices of the existing variants of the UML sequence diagram notation in solving comprehension tasks involving multiple thread interactions. The results implied that a deliberately designed variant of this notation may provide better support for reasoning about concurrent behavior. We then investigated the factors that complicate learning, with the idea that the same complexities would also complicate comprehension tasks. In order to understand the practical difficulties novices encounter in learning about concurrency, we conducted an instructor interview and an observational study. These investigations guided us in determining the desirable properties of a new notation. We then designed synchronization-adorned UM (saUML) sequence diagrams, which extend UML sequence diagrams with those properties. Finally, we performed four empirical studies to evaluate the usability and efficacy of saUML. Through these empirical studies, we were able to validate the benefits of saUML in enhancing novices' understanding of programs with different levels of synchronization complexity.

Visual analytics: building a vibrant and resilient national science

Abstract: Five years after the science of visual analytics was formally established, we attempt to use two different studies to assess the current state of the community and evaluate the progress the community has made in the past few years. The first study involves a comparison analysis of intellectual and scholastic accomplishments recently made by the visual analytics community with two other visualization communities. The second study aims to measure the degree of community reach and internet penetration of visual-analytics-related resources. This article describes our efforts to harvest the study data, conduct analysis and make interpretations based on parallel comparisons with five other established computer science areas.

Challenges in human-centered information visualization: introduction to the special issue

Abstract: Human-centered computing has been described as ‘an emerging field that aims at bridging the existing gaps between the various disciplines involved with the design and implementation of computing systems that support people’s activities’.1 Information visualization is certainly one of the disciplines in the center of human-centered computing.2 Or is it? As we announced the Call for Paper for a special issue on human-centered information visualization, our authors and reviewers have been debating what should be called ‘Human-Centered Information Visualization’ (HCIV). On one extreme, we might claim that all the research on information visualization is ‘human-centered’, as the ultimate goal of information visualization is to let people understand and use the information presented visually. On the other extreme, we might regard the lack of ‘human-centered’ approach as the major issue of current research in information visualization. In this special issue, through a set of excellent research papers selected by the reviewers, we seek to present the perspectives of information visualization researchers (the authors) on the issue of ‘humancentered’ and hope to encourage more conversations on the advancement of information visualization toward ‘human-centered.’ In the first paper, Measuring Effectiveness of Graph Visualizations: A Cognitive Load Perspective, authors Huang et al question the relationship between information visualization and human cognitive loads. They develop an interesting model to measure cognitive loads of graph visualizations and predict the regions of cognitive loads where visualization would be most effective. Their empirical results show promise of the model in revealing the relationship between visual understanding and domain complexity, data complexity, task complexity and visual complexity. In the second paper, What does the User Want to See? What does the Data Want to Be?, authors Pretorius and Van Wijk argue that information visualization needs to be designed from two perspectives: the user perspective and the data perspective. It is emphasized that the two perspectives are inter-related and reinforced mutually. They present several case studies to demonstrate that it is important to consider not only user requirements but also the data to be visualized. In particular, switching from one perspective to another during the iterative design process will significantly benefit the design and the outcome. Their case studies provide some good lessons about building useful information visualization systems. The third paper is about visual information filtering in the searching environment. Titled ‘Adaptive Visualization of Search Results: Bring User Models to Visual Analytics,’ the paper by Ahn and Brusilovsky introduces a framework that incorporates user modeling and information visualization to provide flexible and user-centered visual information filtering. In their retrieval system, documents retrieved by a query are displayed at locations relatively to the point of interest (POI), which can be either user’s query terms or terms generated through user modeling. The user can select to enact or disable a POI to filter through the retrieved documents. In their experimental testing, they specifically test the user model effect on three different graphical layouts and evaluate how well user modeling helps to differentiate relevant and non-relevant documents on the visual displays. The fourth and fifth papers are about applying information visualization to practical problems in the real world. In the paper Knowledge Generation through Human-centered Information Visualization, authors Einsfeld et al address the issue of making information visualization techniques work for non-experts in specific domains. They describe an application of information visualization in a threedimensional environment that allows non-experts to follow visual metaphors to understand semantic relationships of objects both in the virtual world and in the real world. Their results show that visualization helps the user gain better understanding of the data by connecting previously isolated items. Similarly, in the paper Seeing is Believing: Linking Data with Knowledge, authors Dadzie et al present a research project on the development of a visual semantic information space to support sense-making activities. They develop several ‘knowledge views’ using an enhanced user-centered design process. Their user testing results indicate that users understand the values and semantics presented on the visual views and are able to use the visual views for complex reasoning tasks. The sixth paper, Visual Analysis of Dynamic Data Streams by Chin et al, describes their prototyping activities to refine and generalize existing visualization techniques to