One of the most pressing public policy issues that has involved transdisciplinary research in the field of data science is rapidly detecting widespread misinformation. While data science can pose a lot of potential for solving the big-data problem of misinformation on an automated scale, it likewise requires insights from the field of communications and journalism to define quantifiable features that can assist in more accurate misinformation predictions. Currently, the preeminent tools used for misinformation detection are large language models (LLMs) as they are renowned for their ability to capture the context and meaning of textual data. However, despite advancements in developing effective data science models and tools for identifying misinformation, there are not many available options for evaluating news article content for misinformation potential. This study proposes TRUExT, an explainable, regression-based data tool that integrates multiple communication-based natural language processing (NLP) dimensions with a base LLM to holistically evaluate trustworthiness in news articles. It was found that the Hugging Face LLM RoBERTa with the added NLP dimensions as features was the most effective foundational model after testing multiple LLMs. Furthermore, TRUExT introduced a potential big-data solution to the growing problem of misinformation through research intersecting data science and communications to capture not only the technicality of misinformation data predictions but also certain communication factors in the data. In the future, this tool could likewise be deployed to be used by U.S.-based stakeholders who have an important role in the ongoing information war.

Presents the welcome message from the VIS 2022 General Chairs.

Abstract Computer‐based technology has played a significant role in crime prevention over the past 30 years, especially with the popularization of spatial databases and crime mapping systems. Police departments frequently use hotspot analysis to identify regions that should be a priority in receiving preventive resources. Practitioners and researchers agree that tracking crime over time and identifying its geographic patterns are vital information for planning efficiently. Frequently, police departments have access to systems that are too complicated and excessively technical, leading to modest usage. By working closely together with domain experts from police agencies of two different countries, we identified and characterized five domain tasks inherent to the hotspot analysis problem and developed SHOC, a visualization tool that strives for simplicity and ease of use in helping users to perform all the domain tasks. SHOC is included in a visual analytics system that allows users without technical expertise to annotate, save, and share analyses. We also demonstrate that our system effectively supports the completion of the domain tasks in two different real‐world case studies.

Many evaluation methods have been used to assess the usefulness of Visual Analytics (VA) solutions. These methods stem from a variety of origins with different assumptions and goals, which cause confusion about their proofing capabilities. Moreover, the lack of discussion about the evaluation processes may limit our potential to develop new evaluation methods specialized for VA. In this paper, we present an analysis of evaluation methods that have been used to summatively evaluate VA solutions. We provide a survey and taxonomy of the evaluation methods that have appeared in the VAST literature in the past two years. We then analyze these methods in terms of validity and generalizability of their findings, as well as the feasibility of using them. We propose a new metric called summative quality to compare evaluation methods according to their ability to prove usefulness, and make recommendations for selecting evaluation methods based on their summative quality in the VA domain.

Social media platforms are filled with social spambots. Detecting these malicious accounts is essential, yet challenging, as they continually evolve to evade detection techniques. In this article, we present VASSL, a visual analytics system that assists in the process of detecting and labeling spambots. Our tool enhances the performance and scalability of manual labeling by providing multiple connected views and utilizing dimensionality reduction, sentiment analysis and topic modeling, enabling insights for the identification of spambots. The system allows users to select and analyze groups of accounts in an interactive manner, which enables the detection of spambots that may not be identified when examined individually. We present a user study to objectively evaluate the performance of VASSL users, as well as capturing subjective opinions about the usefulness and the ease of use of the tool.

We present an interactive visual analytics system that enables traffic congestion exploration, surveillance, and forecasting based on vehicle detector data. Through domain expert collaboration, we have extracted task requirements, incorporated the Long Short-Term Memory (LSTM) model for congestion forecasting, and designed a weighting method for detecting the causes of congestion and congestion propagation directions. Our visual analytics system is designed to enable users to explore congestion causes, directions, and severity. Congestion conditions of a city are visualized using a Volume-Speed Rivers (VSRivers) visualization that simultaneously presents traffic volumes and speeds. To evaluate our system, we report performance comparison results, wherein our model is more accurate than other forecasting algorithms. We demonstrate the usefulness of our system in the traffic management and congestion broadcasting domains through three case studies and domain expert feedback.

Various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. However, due to the high noise in the deluge of data, effectively determining semantically relevant information can be difficult, further complicated by the changing definition of relevancy by each end user for different events. The majority of existing methods for short text relevance classification fail to incorporate users' knowledge into the classification process. Existing methods that incorporate interactive user feedback focus on historical datasets. Therefore, classifiers cannot be interactively retrained for specific events or user-dependent needs in real-time. This limits real-time situational awareness, as streaming data that is incorrectly classified cannot be corrected immediately, permitting the possibility for important incoming data to be incorrectly classified as well. We present a novel interactive learning framework to improve the classification process in which the user iteratively corrects the relevancy of tweets in real-time to train the classification model on-the-fly for immediate predictive improvements. We computationally evaluate our classification model adapted to learn at interactive rates. Our results show that our approach outperforms state-of-the-art machine learning models. In addition, we integrate our framework with the extended Social Media Analytics and Reporting Toolkit (SMART) 2.0 system, allowing the use of our interactive learning framework within a visual analytics system tailored for real-time situational awareness. To demonstrate our framework's effectiveness, we provide domain expert feedback from first responders who used the extended SMART 2.0 system.

Evaluating employee performance in organizations with varying workloads and tasks is challenging. Specifically, it is important to understand how quantitative measurements of employee achievements relate to supervisor expectations, what the main drivers of good performance are, and how to combine these complex and flexible performance evaluation metrics into an accurate portrayal of organizational performance in order to identify shortcomings and improve overall productivity. To facilitate this process, we summarize common organizational performance analyses into four visual exploration task categories. Additionally, we develop MetricsVis, a visual analytics system composed of multiple coordinated views to support the dynamic evaluation and comparison of individual, team, and organizational performance in public safety organizations. MetricsVis provides four primary visual components to expedite performance evaluation: (1) a priority adjustment view to support direct manipulation on evaluation metrics; (2) a reorderable performance matrix to demonstrate the details of individual employees; (3) a group performance view that highlights aggregate performance and individual contributions for each group; and (4) a projection view illustrating employees with similar specialties to facilitate shift assignments and training. We demonstrate the usability of our framework with two case studies from medium-sized law enforcement agencies and highlight its broader applicability to other domains.

Information, not just data, is key to today's global challenges. To solve these challenges requires not only advancing geospatial and big data analytics but requires new analysis and decision-making environments that enable reliable decisions from trustable, understandable information that go beyond current approaches to machine learning and artificial intelligence. These environments are successful when they effectively couple human decision making with advanced, guided spatial analytics in human-computer collaborative discourse and decision making (HCCD). Our HCCD approach builds upon visual analytics, natural scale templates, traceable information, human-guided analytics, and explainable and interactive machine learning, focusing on empowering the decisionmaker through interactive visual spatial analytic environments where non-digital human expertise and experience can be combined with state-of-the-art and transparent analytical techniques. When we combine this approach with real-world application-driven research, not only does the pace of scientific innovation accelerate, but impactful change occurs. I'll describe how we have applied these techniques to challenges in sustainability, security, resiliency, public safety, and disaster management.

Abstract Designing, evaluating, and improving visual analytics (VA) systems is a primary area of activities in our discipline. In this paper, we present an ontological framework for recording and categorizing technical shortcomings to be addressed in a VA workflow, reasoning about the causes of such problems, identifying technical solutions, and anticipating secondary effects of the solutions. The methodology is built on the theoretical premise that designing a VA workflow is an optimization of the cost‐benefit ratio of the processes in the workflow. It makes uses three fundamental measures to group and connect “symptoms”, “causes”, “remedies”, and “side‐effects”, and guide the search for potential solutions to the problems. In terms of requirement analysis and system design, the proposed methodology can enable system designers to explore the decision space in a structured manner. In terms of evaluation, the proposed methodology is time‐efficient and complementary to various forms of empirical studies, such as user surveys, controlled experiments, observational studies, focus group discussions, and so on. In general, it reduces the amount of trial‐and‐error in the lifecycle of VA system development.

Abstract Communication‐minded visualizations are designed to provide their audience—managers, decision‐makers, and the public—with new knowledge. Authoring such visualizations effectively is challenging because the audience often lacks the expertise, context, and time that professional analysts have at their disposal to explore and understand datasets. We present a novel summarized line graph visualization technique designed specifically for data analysts to communicate data to decision‐makers more effectively and efficiently. Our summarized line graph reduces a large and detailed dataset of multiple quantitative time‐series into (1) representative data that provides a quick takeaway of the full dataset; (2) analytical highlights that distinguish specific insights of interest; and (3) a data envelope that summarizes the remaining aggregated data. Our summarized line graph achieved the best overall results when evaluated against line graphs, band graphs, stream graphs, and horizon graphs on four representative tasks.

Interpretation and diagnosis of machine learning models have gained renewed interest in recent years with breakthroughs in new approaches. We present Manifold, a framework that utilizes visual analysis techniques to support interpretation, debugging, and comparison of machine learning models in a more transparent and interactive manner. Conventional techniques usually focus on visualizing the internal logic of a specific model type (i.e., deep neural networks), lacking the ability to extend to a more complex scenario where different model types are integrated. To this end, Manifold is designed as a generic framework that does not rely on or access the internal logic of the model and solely observes the input (i.e., instances or features) and the output (i.e., the predicted result and probability distribution). We describe the workflow of Manifold as an iterative process consisting of three major phases that are commonly involved in the model development and diagnosis process: inspection (hypothesis), explanation (reasoning), and refinement (verification). The visual components supporting these tasks include a scatterplot-based visual summary that overviews the models' outcome and a customizable tabular view that reveals feature discrimination. We demonstrate current applications of the framework on the classification and regression tasks and discuss other potential machine learning use scenarios where Manifold can be applied.

TopoText is a context-preserving technique for visualizing text data for multi-scale spatial aggregates to gain insight into spatial phenomena. Conventional exploration requires users to navigate across multiple scales but only presents the information related to the current scale. This limitation potentially adds more steps of interaction and cognitive overload to the users. TopoText renders multi-scale aggregates into a single visual display combining novel text-based encoding and layout methods that draw labels along the boundary or filled within the aggregates. The text itself not only summarizes the semantics at each individual scale, but also indicates the spatial coverage of the aggregates and their underlying hierarchical relationships. We validate TopoText with both a user study as well as several application examples.

Accurate phenotyping with unmanned aerial vehicles is a remote sensing application that has received recent attention as plant breeders seek to automate the expensive and time consuming traditional manual acquisition of measurements of plant traits. This paper focuses on the prediction of sorghum biomass utilizing high temporal and spatial resolution remote sensing data. Two methods are investigated for biomass prediction. The first uses nonlinear regression models to predict biomass directly from remote sensing data, based on features from Light Detection And Ranging (LiDAR) point clouds and hyperspectral data. The second strategy focuses on the biophysical sorghum crop model, APSIM, first, using remote sensing data to parametrize the crop model, and then simulating the biomass. Results from both approaches are provided and evaluated for an agricultural test field at the Agronomy Center for Research and Education (ACRE) at Purdue University.

Geographic visualization research has focused on a variety of techniques to represent and explore spatiotemporal data. The goal of those techniques is to enable users to explore events and interactions over space and time in order to facilitate the discovery of patterns, anomalies and relationships within the data. However, it is difficult to extract and visualize data flow patterns over time for non-directional statistical data without trajectory information. In this work, we develop a novel flow analysis technique to extract, represent, and analyze flow maps of non-directional spatiotemporal data unaccompanied by trajectory information. We estimate a continuous distribution of these events over space and time, and extract flow fields for spatial and temporal changes utilizing a gravity model. Then, we visualize the spatiotemporal patterns in the data by employing flow visualization techniques. The user is presented with temporal trends of geo-referenced discrete events on a map. As such, overall spatiotemporal data flow patterns help users analyze geo-referenced temporal events, such as disease outbreaks, crime patterns, etc. To validate our model, we discard the trajectory information in an origin-destination dataset and apply our technique to the data and compare the derived trajectories and the original. Finally, we present spatiotemporal trend analysis for statistical datasets including twitter data, maritime search and rescue events, and syndromic surveillance.

To solve the world's challenges requires not only advancing computer science and big data analytics but requires new analysis and decision-making environments that effectively couple human decision making with advanced, guided analytics in a human-computer collaborative discourse and decision making (HCCD). While many researchers and companies are focusing solely on Big Data Analytics to harness the potential power in available massive, multisource, multivariate, evolving digital data, many of these big data solutions don't effectively factor the human decision maker into their proposed solution. The HCCD approach builds upon visual analytics and focuses on empowering the decision maker through interactive visual analytic environments where visual cognition, guided discovery, and non-digital human expertise and experience can be combined with state-of-the-art analytical techniques. When we combine this approach with real-world application-driven research, not only does the pace of scientific innovation accelerate, but impactful change occurs. This work and these systems can be applied to social change, advancing engineering, and science and solving some of the world's greatest challenges such as sustainability and security. In this talk, I'll outline this approach and highlight the potential and impact of application driven HCCD research.

Spatial datasets, such as tweets in a geographic area, often exhibit different distribution patterns at multiple levels of scale, such as live updates about events occurring in very specific locations on the map. Navigating in such multi-scale data-rich spaces is often inefficient, requires users to choose between overview or detail information, and does not support identifying spatial patterns at varying scales. In this paper, we propose TopoGroups, a novel context-preserving technique that aggregates spatial data into hierarchical clusters to improve exploration and navigation at multiple spatial scales. The technique uses a boundary distortion algorithm to minimize the visual clutter caused by overlapping aggregates. Our user study explores multiple visual encoding strategies for TopoGroups including color, transparency, shading, and shapes in order to convey the hierarchical and statistical information of the geographical aggregates at different scales.

The 2016 Visualization Technical Achievement Award goes to David Ebert in recognition of foundational work in visual analytics, both through development of fundamental predictive techniques and as Director of the Purdue/DHS Visual Analytics Center of Excellence.

We present TimeFork, an interactive prediction technique to support users predicting the future of time-series data, such as in financial, scientific, or medical domains. TimeFork combines visual representations of multiple time series with prediction information generated by computational models. Using this method, analysts engage in a back-and-forth dialogue with the computational model by alternating between manually predicting future changes through interaction and letting the model automatically determine the most likely outcomes, to eventually come to a common prediction using the model. This computer-supported prediction approach allows for harnessing the user's knowledge of factors influencing future behavior, as well as sophisticated computational models drawing on past performance. To validate the TimeFork technique, we conducted a user study in a stock market prediction game. We present evidence of improved performance for participants using TimeFork compared to fully manual or fully automatic predictions, and characterize qualitative usage patterns observed during the user study.

Abstract Market participants and businesses have made tremendous efforts to make the best decisions in a timely manner under varying economic and business circumstances. As such, decision‐making processes based on Financial data have been a popular topic in industries. However, analyzing Financial data is a non‐trivial task due to large volume, diversity and complexity, and this has led to rapid research and development of visualizations and visual analytics systems for Financial data exploration. Often, the development of such systems requires researchers to collaborate with Financial domain experts to better extract requirements and challenges in their tasks. Work to systematically study and gather the task requirements and to acquire an overview of existing visualizations and visual analytics systems that have been applied in Financial domains with respect to real‐world data sets has not been completed. To this end, we perform a comprehensive survey of visualizations and visual analytics. In this work, we categorize Financial systems in terms of data sources, applied automated techniques, visualization techniques, interaction, and evaluation methods. For the categorization and characterization, we utilize existing taxonomies of visualization and interaction. In addition, we present task requirements extracted from interviews with domain experts in order to help researchers design better systems with detailed goals.

Abstract Real‐time microblogs can be utilized to provide situational awareness during emergency and disaster events. However, the utilization of these datasets requires the decision makers to perform their exploration and analysis across a range of data scales from local to global, while maintaining a cohesive thematic context of the transition between the different granularity levels. The exploration of different information dimensions at the varied data and human scales remains to be a non‐trivial task. To this end, we present a visual analytics situational awareness environment that supports the real‐time exploration of microblog data across multiple scales of analysis. We classify microblogs based on a fine‐grained, crisis‐related categorization approach, and visualize the spatiotemporal evolution of multiple categories by coupling a spatial lens with a glyph‐based visual design. We propose a transparency‐based spatial context preserving technique that maintains a smooth transition between different spatial scales. To evaluate our system, we conduct user studies and provide domain expert feedback.

The advent of modern smart phones and handheld devices has given analysts, decision-makers, and even the general public the ability to rapidly ingest data and translate it into actionable information on-the-go. In this paper, we explore the design and use of a mobile visual analytics toolkit for public safety data that equips law enforcement agencies with effective situation awareness and risk assessment tools. Our system provides users with a suite of interactive tools that allow them to perform analysis and detect trends, patterns and anomalies among criminal, traffic and civil (CTC) incidents. The system also provides interactive risk assessment tools that allow users to identify regions of potential high risk and determine the risk at any user-specified location and time. Our system has been designed for the iPhone/iPad environment and is currently being used and evaluated by a consortium of law enforcement agencies. We report their use of the system and some initial feedback.

This editorial introduction describes the aims and scope of ACM Transactions on Interactive Intelligent Systems 's special issue on interactive computational visual analytics. It explains why visual analytics is crucial to the growing needs surrounding data analysis, and it shows how the four articles selected for this issue reflect this theme.

We present VASA, a visual analytics platform consisting of a desktop application, a component model, and a suite of distributed simulation components for modeling the impact of societal threats such as weather, food contamination, and traffic on critical infrastructure such as supply chains, road networks, and power grids. Each component encapsulates a high-fidelity simulation model that together form an asynchronous simulation pipeline: a system of systems of individual simulations with a common data and parameter exchange format. At the heart of VASA is the Workbench, a visual analytics application providing three distinct features: (1) low-fidelity approximations of the distributed simulation components using local simulation proxies to enable analysts to interactively configure a simulation run; (2) computational steering mechanisms to manage the execution of individual simulation components; and (3) spatiotemporal and interactive methods to explore the combined results of a simulation run. We showcase the utility of the platform using examples involving supply chains during a hurricane as well as food contamination in a fast food restaurant chain.

In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets.

We present Bristle Maps, a novel method for the aggregation, abstraction, and stylization of spatiotemporal data that enables multiattribute visualization, exploration, and analysis. This visualization technique supports the display of multidimensional data by providing users with a multiparameter encoding scheme within a single visual encoding paradigm. Given a set of geographically located spatiotemporal events, we approximate the data as a continuous function using kernel density estimation. The density estimation encodes the probability that an event will occur within the space over a given temporal aggregation. These probability values, for one or more set of events, are then encoded into a bristle map. A bristle map consists of a series of straight lines that extend from, and are connected to, linear map elements such as roads, train, subway lines, and so on. These lines vary in length, density, color, orientation, and transparency—creating the multivariate attribute encoding scheme where event magnitude, change, and uncertainty can be mapped as various bristle parameters. This approach increases the amount of information displayed in a single plot and allows for unique designs for various information schemes. We show the application of our bristle map encoding scheme using categorical spatiotemporal police reports. Our examples demonstrate the use of our technique for visualizing data magnitude, variable comparisons, and a variety of multivariate attribute combinations. To evaluate the effectiveness of our bristle map, we have conducted quantitative and qualitative evaluations in which we compare our bristle map to conventional geovisualization techniques. Our results show that bristle maps are competitive in completion time and accuracy of tasks with various levels of complexity.

Currently, user centered transfer function design begins with the user interacting with a one or two-dimensional histogram of the volumetric attribute space. The attribute space is visualized as a function of the number of voxels, allowing the user to explore the data in terms of the attribute size/magnitude. However, such visualizations provide the user with no information on the relationship between various attribute spaces (e.g., density, temperature, pressure, x, y, z) within the multivariate data. In this work, we propose a modification to the attribute space visualization in which the user is no longer presented with the magnitude of the attribute; instead, the user is presented with an information metric detailing the relationship between attributes of the multivariate volumetric data. In this way, the user can guide their exploration based on the relationship between the attribute magnitude and user selected attribute information as opposed to being constrained by only visualizing the magnitude of the attribute. We refer to this modification to the traditional histogram widget as an abstract attribute space representation. Our system utilizes common one and two-dimensional histogram widgets where the bins of the abstract attribute space now correspond to an attribute relationship in terms of the mean, standard deviation, entropy, or skewness. In this manner, we exploit the relationships and correlations present in the underlying data with respect to the dimension(s) under examination. These relationships are often times key to insight and allow us to guide attribute discovery as opposed to automatic extraction schemes which try to calculate and extract distinct attributes a priori. In this way, our system aids in the knowledge discovery of the interaction of properties within volumetric data.

The concept of preconditioning data (utilizing a power transformation as an initial step) for analysis and visualization is well established within the statistical community and is employed as part of statistical modeling and analysis. Such transformations condition the data to various inherent assumptions of statistical inference procedures, as well as making the data more symmetric and easier to visualize and interpret. In this paper, we explore the use of the Box-Cox family of power transformations to semiautomatically adjust visual parameters. We focus on time-series scaling, axis transformations, and color binning for choropleth maps. We illustrate the usage of this transformation through various examples, and discuss the value and some issues in semiautomatically using these transformations for more effective data visualization.

When analyzing metabolomics data, cancer care researchers are searching for differences between known healthy samples and unhealthy samples. By analyzing and understanding these differences, researchers hope to identify cancer biomarkers. Due to the size and complexity of the data produced, however, analysis can still be very slow and time consuming. This is further complicated by the fact that datasets obtained will exhibit incidental differences in intensity and retention time, not related to actual chemical differences in the samples being evaluated. Additionally, automated tools to correct these errors do not always produce reliable results. This work presents a new analytics system that enables interactive comparative visualization and analytics of metabolomics data obtained by two-dimensional gas chromatography-mass spectrometry (GC × GC-MS). The key features of this system are the ability to produce visualizations of multiple GC × GC-MS data sets, and to explore those data sets interactively, allowing a user to discover differences and features in real time. The system provides statistical support in the form of difference, standard deviation, and kernel density estimation calculations to aid users in identifying meaningful differences between samples. These are combined with novel transfer functions and multiform, linked visualizations in order to provide researchers with a powerful new tool for GC × GC-MS exploration and bio-marker discovery.

Abstract Competitive intelligence is a systematic approach for gathering, analyzing, and managing information to make informed business decisions. Many companies use competitive intelligence to identify risks and opportunities within markets. Point of sale data that retailers share with vendors is of critical importance in developing competitive intelligence. However, existing tools do not easily enable the analysis of such large and complex data. therefore, new approaches are needed in order to facilitate better analysis and decision making. In this paper, we present MarketAnalyzer, an interactive visual analytics system designed to allow vendors to increase their competitive intelligence. MarketAnalyzer utilizes pixel‐based matrices to present sale data, trends, and market share growths of products of the entire market within a single display. These matrices are augmented by advanced underlying analytical methods to enable the quick evaluation of growth and risk within market sectors. Furthermore, our system enables the aggregation of point of sale data in geographical views that provide analysts with the ability to explore the impact of regional demographics and trends. Additionally, overview and detailed information is provided through a series of coordinated multiple views. In order to demonstrate the effectiveness of our system, we provide two use‐case scenarios as well as feedback from market analysts.

We present a method for automatically building typographic maps that merge text and spatial data into a visual representation where text alone forms the graphical features. We further show how to use this approach to visualize spatial data such as traffic density, crime rate, or demographic data. The technique accepts a vector representation of a geographic map and spatializes the textual labels in the space onto polylines and polygons based on user-defined visual attributes and constraints. Our sample implementation runs as a Web service, spatializing shape files from the OpenStreetMap project into typographic maps for any region.

In many scientific simulations, the temporal variation and analysis of features are important. Visualization and visual analysis of time series data is still a significant challenge because of the large volume of data. Irregular and scattered time series data sets are even more problematic to visualize interactively. Previous work proposed functional representation using basis functions as one solution for interactively visualizing scattered data by harnessing the power of modern PC graphics boards. In this paper, we use the functional representation approach for time-varying data sets and develop an efficient encoding technique utilizing temporal similarity between time steps. Our system utilizes a graduated approach of three methods with increasing time complexity based on the lack of similarity of the evolving data sets. Using this system, we are able to enhance the encoding performance for the time-varying data sets, reduce the data storage by saving only changed or additional basis functions over time, and interactively visualize the time-varying encoding results. Moreover, we present efficient rendering of the functional representations using binary space partitioning tree textures to increase the rendering performance.

Time is a universal and essential aspect of data in any investigative analysis. It helps analysts establish causality, build storylines from evidence, and reject infeasible hypotheses. For this reason, many investigative analysis tools provide visual representations designed for making sense of temporal data. However, the field of visual analytics still needs more evidence explaining how temporal visualization actually aids the analysis process, as well as design recommendations for how to build these visualizations. To fill this gap, we conducted an insight-based qualitative study to investigate the influence of temporal visualization on investigative analysis. We found that visualizing temporal information helped participants externalize chains of events. Another contribution of our work is the lightweight evaluation approach used to collect, visualize, and analyze insight.

We have developed an intuitive method to semiautomatically explore volumetric data in a focus-region-guided or value-driven way using a user-defined ray through the 3D volume and contour lines in the region of interest. After selecting a point of interest from a 2D perspective, which defines a ray through the 3D volume, our method provides analytical tools to assist in narrowing the region of interest to a desired set of features. Feature layers are identified in a 1D scalar value profile with the ray and are used to define default rendering parameters, such as color and opacity mappings, and locate the center of the region of interest. Contour lines are generated based on the feature layer level sets within interactively selected slices of the focus region. Finally, we utilize feature-preserving filters and demonstrate the applicability of our scheme to noisy data.

Current visual analytics systems provide users with the means to explore trends in their data. Linked views and interactive displays provide insight into correlations among people, events, and places in space and time. Analysts search for events of interest through statistical tools linked to visual displays, drill down into the data, and form hypotheses based upon the available information. However, current systems stop short of predicting events. In spatiotemporal data, analysts are searching for regions of space and time with unusually high incidences of events (hotspots). In the cases where hotspots are found, analysts would like to predict how these regions may grow in order to plan resource allocation and preventative measures. Furthermore, analysts would also like to predict where future hotspots may occur. To facilitate such forecasting, we have created a predictive visual analytics toolkit that provides analysts with linked spatiotemporal and statistical analytic views. Our system models spatiotemporal events through the combination of kernel density estimation for event distribution and seasonal trend decomposition by loess smoothing for temporal predictions. We provide analysts with estimates of error in our modeling, along with spatial and temporal alerts to indicate the occurrence of statistically significant hotspots. Spatial data are distributed based on a modeling of previous event locations, thereby maintaining a temporal coherence with past events. Such tools allow analysts to perform real-time hypothesis testing, plan intervention strategies, and allocate resources to correspond to perceived threats.

In this paper, we describe the Technology Assisted Dietary Assessment (TADA) project at Purdue University. Dietary intake, what someone eats during the course of a day, provides valuable insights for mounting intervention programs for prevention of many chronic diseases such as obesity and cancer. Accurate methods and tools to assess food and nutrient intake are essential for research on the association between diet and health. An overview of our methods used in the TADA project is presented. Our approach includes the use of image analysis tools for identification and quantification of food that is consumed at a meal. Images obtained before and after foods are eaten are used to estimate the amount and type of food consumed.

In this paper, we present a mobile user interface for image-based dietary assessment. The mobile user interface provides a front end to a client-server image recognition and portion estimation software. In the client-server configuration, the user interactively records a series of food images using a built-in camera on the mobile device. Images are sent from the mobile device to the server, and the calorie content of the meal is estimated. In this paper, we describe and discuss the design and development of our mobile user interface features. We discuss the design concepts, through initial ideas and implementations. For each concept, we discuss qualitative user feedback from participants using the mobile client application. We then discuss future designs, including work on design considerations for the mobile application to allow the user to interactively correct errors in the automatic processing while reducing the user burden associated with classical pen-and-paper dietary records.

Abstract Procedural noise functions are widely used in computer graphics, from off‐line rendering in movie production to interactive video games. The ability to add complex and intricate details at low memory and authoring cost is one of its main attractions. This survey is motivated by the inherent importance of noise in graphics, the widespread use of noise in industry and the fact that many recent research developments justify the need for an up‐to‐date survey. Our goal is to provide both a valuable entry point into the field of procedural noise functions, as well as a comprehensive view of the field to the informed reader. In this report, we cover procedural noise functions in all their aspects. We outline recent advances in research on this topic, discussing and comparing recent and well‐established methods. We first formally define procedural noise functions based on stochastic processes and then classify and review existing procedural noise functions. We discuss how procedural noise functions are used for modelling and how they are applied to surfaces. We then introduce analysis tools and apply them to evaluate and compare the major approaches to noise generation. We finally identify several directions for future work.

This article presents a method for automating rendering parameter selection to simplify tedious user interaction and improve the usability of visualization systems. Our approach acquires the important/interesting regions of a dataset through simple user interaction with an eye tracker. Based on this importance information, we automatically compute reasonable rendering parameters using a set of heuristic rules, which are adapted from visualization experience and psychophysical experiments. A user study has been conducted to evaluate these rendering parameters, and while the parameter selections for a specific visualization result are subjective, our approach provides good preliminary results for general users while allowing additional control adjustment. Furthermore, our system improves the interactivity of a visualization system by significantly reducing the required amount of parameter selections and providing good initial rendering parameters for newly acquired datasets of similar types.

As data sources become larger and more complex, the ability to effectively explore and analyze patterns among varying sources becomes a critical bottleneck in analytic reasoning. Incoming data contain multiple variables, high signal-to-noise ratio, and a degree of uncertainty, all of which hinder exploration, hypothesis generation/exploration, and decision making. To facilitate the exploration of such data, advanced tool sets are needed that allow the user to interact with their data in a visual environment that provides direct analytic capability for finding data aberrations or hotspots. In this paper, we present a suite of tools designed to facilitate the exploration of spatiotemporal data sets. Our system allows users to search for hotspots in both space and time, combining linked views and interactive filtering to provide users with contextual information about their data and allow the user to develop and explore their hypotheses. Statistical data models and alert detection algorithms are provided to help draw user attention to critical areas. Demographic filtering can then be further applied as hypotheses generated become fine tuned. This paper demonstrates the use of such tools on multiple geospatiotemporal data sets.

Abstract In this paper, we present a novel two‐dimensional (2D) shape context preserving image manipulation approach which constructs and manipulates a 2D mesh with a new differential mesh editing algorithm. We introduce a novel shape context descriptor and integrate it into the deformation framework, facilitating shape‐preserving deformation for 2D anatomical illustrations. Our new scheme utilizes an analogy based shape transfer technique in order to learn shape styles from reference images. Experimental results show that visually plausible deformation can be quickly generated from an existing example at interactive frame rates. An experienced artist has evaluated our approach and his feedback is quite encouraging.

Abstract Multi‐dimensional transfer functions are commonly used in rectilinear volume renderings to effectively portray materials, material boundaries and even subtle variations along boundaries. However, most unstructured grid rendering algorithms only employ one‐dimensional transfer functions. This paper proposes a novel pre‐integrated Projected Tetrahedra (PT) rendering technique that applies bivariate transfer functions on unstructured grids. For each type of bivariate transfer function, an analytical form that pre‐integrates the contribution of a ray segment in one tetrahedron is derived, and can be precomputed as a lookup table to compute the color and opacity in a projected tetrahedron on‐the‐fly. Further, we show how to approximate the integral using the pre‐integration method for faster unstructured grid rendering. We demonstrate the advantages of our approach with a variety of examples and comparisons with one‐dimensional transfer functions.

Abstract When performing daily maintenance and repair tasks, technicians require access to a variety of technical diagrams. As technicians trace components and diagrams from page‐to‐page, within and across manuals, the contextual information of the components they are analyzing can easily be lost. To overcome these issues, we have developed a Schematic Diagram Visualization System (SDViz) designed for maintaining and highlighting contextual information in technical documents, such as schematic and wiring diagrams. Our system incorporates various features to aid in the navigation and diagnosis of faults, as well as maintaining contextual information when tracing components/connections through multiple diagrams. System features include highlighting relationships between components and connectors, diagram annotation tools, the animation of flow through the system, a novel contextual blending method, and a variety of traditional focus+context visualization techniques. We have evaluated the usefulness of our system through a qualitative user study in which subjects utilized our system in diagnosing faults during a standard aircraft maintenance exercise.

Abstract This paper presents an interactive volume modeling method that constructs skeletal muscles from an existing volumetric dataset. Our approach provides users with an intuitive modeling interface and produces compelling results that conform to the characteristic anatomy in the input volume. The algorithmic core of our method is an intuitive anatomy classification approach, suited to accommodate spatial constraints on the muscle volume. The presented work is useful in illustrative visualization, volumetric information fusion and volume illustration that involve muscle modeling, where the spatial context should be faithfully preserved.

Medical illustration has demonstrated its effectiveness to depict salient anatomical features while hiding the irrelevant details. Current solutions are ineffective for visualizing fibrous structures such as muscle, because typical datasets (CT or MRI) do not contain directional details. In this paper, we introduce a new muscle illustration approach that leverages diffusion tensor imaging (DTI) data and example-based texture synthesis techniques. Beginning with a volumetric diffusion tensor image, we reformulate it into a scalar field and an auxiliary guidance vector field to represent the structure and orientation of a muscle bundle. A muscle mask derived from the input diffusion tensor image is used to classify the muscle structure. The guidance vector field is further refined to remove noise and clarify structure. To simulate the internal appearance of the muscle, we propose a new two-dimensional example based solid texture synthesis algorithm that builds a solid texture constrained by the guidance vector field. Illustrating the constructed scalar field and solid texture efficiently highlights the global appearance of the muscle as well as the local shape and structure of the muscle fibers in an illustrative fashion. We have applied the proposed approach to five example datasets (four pig hearts and a pig leg), demonstrating plausible illustration and expressiveness.

An editorail on the 15th Anniversary of the IEEE Transactions on Visualization and Computer Graphics.

The use of multi-dimensional transfer functions for direct volume rendering has been shown to be an effective means of extracting materials and their boundaries for both scalar and multivariate data. The most common multi-dimensional transfer function consists of a two-dimensional (2D) histogram with axes representing a subset of the feature space (e.g., value vs. value gradient magnitude), with each entry in the 2D histogram being the number of voxels at a given feature space pair. Users then assign color and opacity to the voxel distributions within the given feature space through the use of interactive widgets (e.g., box, circular, triangular selection). Unfortunately, such tools lead users through a trial-and-error approach as they assess which data values within the feature space map to a given area of interest within the volumetric space. In this work, we propose the addition of non-parametric clustering within the transfer function feature space in order to extract patterns and guide transfer function generation. We apply a non-parametric kernel density estimation to group voxels of similar features within the 2D histogram. These groups are then binned and colored based on their estimated density, and the user may interactively grow and shrink the binned regions to explore feature boundaries and extract regions of interest. We also extend this scheme to temporal volumetric data in which time steps of 2D histograms are composited into a histogram volume. A three-dimensional (3D) density estimation is then applied, and users can explore regions within the feature space across time without adjusting the transfer function at each time step. Our work enables users to effectively explore the structures found within a feature space of the volume and provide a context in which the user can understand how these structures relate to their volumetric data. We provide tools for enhanced exploration and manipulation of the transfer function, and we show that the initial transfer function generation serves as a reasonable base for volumetric rendering, reducing the trial-and-error overhead typically found in transfer function design.

The availability of commodity volumetric displays provides ordinary users with a new means of visualizing 3D data. Many of these displays are in the class of isotropically emissive light devices, which are designed to directly illuminate voxels in a 3D frame buffer, producing X-ray-like visualizations. While this technology can offer intuitive insight into a 3D object, the visualizations are perceptually different from what a computer graphics or visualization system would render on a 2D screen. This paper formalizes rendering on isotropically emissive displays and introduces a novel technique that emulates traditional rendering effects on isotropically emissive volumetric displays, delivering results that are much closer to what is traditionally rendered on regular 2D screens. Such a technique can significantly broaden the capability and usage of isotropically emissive volumetric displays. Our method takes a 3D dataset or object as the input, creates an intermediate light field, and outputs a special 3D volume dataset called a lumi-volume. This lumi-volume encodes approximated rendering effects in a form suitable for display with accumulative integrals along unobtrusive rays. When a lumi-volume is fed directly into an isotropically emissive volumetric display, it creates a 3D visualization with surface shading effects that are familiar to the users. The key to this technique is an algorithm for creating a 3D lumi-volume from a 4D light field. In this paper, we discuss a number of technical issues, including transparency effects due to the dimension reduction and sampling rates for light fields and lumi-volumes. We show the effectiveness and usability of this technique with a selection of experimental results captured from an isotropically emissive volumetric display, and we demonstrate its potential capability and scalability with computer-simulated high-resolution results.

Volume illustration can be used to provide insight into source data from CT/MRI scanners in much the same way as medical illustration depicts the important details of anatomical structures. As such, proven techniques used in medical illustration should be transferable to volume illustration, providing scientists with new tools to visualize their data. In recent years, a number of techniques have been developed to enhance the rendering pipeline and create illustrative effects similar to the ones found in medical textbooks and surgery manuals. Such effects usually highlight important features of the subject while subjugating its context and providing depth cues for correct perception. Inspired by traditional visual and line-drawing techniques found in medical illustration, we have developed a collection of fast algorithms for more effective emphasis/de-emphasis of data as well as conveyance of spatial relationships. Our techniques utilize effective outlining techniques and selective depth enhancement to provide perceptual cues of object importance as well as spatial relationships in volumetric datasets. Moreover, we have used illustration principles to effectively combine and adapt basic techniques so that they work together to provide consistent visual information and a uniform style.

Abstract Diffusion tensor imaging (DTI) has been used to generate fibrous structures in both brain white matter and muscles. Fiber clustering groups the DTI fibers into spatially and anatomically related tracts. As an increasing number of fiber clustering methods have been recently developed, it is important to display, compare, and explore the clustering results efficiently and effectively. In this paper, we present an anatomical visualization technique that reduces the geometric complexity of the fiber tracts and emphasizes the high‐level structures. Beginning with a volumetric diffusion tensor image, we first construct a hierarchical clustering representation of the fiber bundles. These bundles are then reformulated into a 3D multi‐valued volume data. We then build a set of geometric hulls and principal fibers to approximate the shape and orientation of each fiber bundle. By simultaneously visualizing the geometric hulls, individual fibers, and other data sets such as fractional anisotropy, the overall shape of the fiber tracts are highlighted, while preserving the fibrous details. A rater with expert knowledge of white matter structure has evaluated the resulting interactive illustration and confirmed the improvement over straightforward DTI fiber tract visualization.

Abstract We introduce a novel volume illustration technique for regularly sampled volume datasets. The fundamental difference between previous volume illustration algorithms and ours is that our results are shape‐aware, as they depend not only on the rendering styles, but also the shape styles. We propose a new data structure that is derived from the input volume and consists of a distance volume and a segmentation volume. The distance volume is used to reconstruct a continuous field around the object boundary, facilitating smooth illustrations of boundaries and silhouettes. The segmentation volume allows us to abstract or remove distracting details and noise, and apply different rendering styles to different objects and components. We also demonstrate how to modify the shape of illustrated objects using a new 2D curve analogy technique. This provides an interactive method for learning shape variations from 2D hand‐painted illustrations by drawing several lines. Our experiments on several volume datasets demonstrate that the proposed approach can achieve visually appealing and shape‐aware illustrations. The feedback from medical illustrators is quite encouraging.

To create a new, flexible system for volume illustration, we have explored the use of Wang Cubes, the 3D extension of 2D Wang Tiles. We use small sets of Wang Cubes to generate a large variety of nonperiodic illustrative 3D patterns and texture, which otherwise would be too large to use in real applications. We also develop a direct volume rendering framework with the generated patterns and textures. Our framework can be used to render volume datasets effectively and a variety of rendering styles can be achieved with less storage. Specifically, we extend the nonperiodic tiling process of Wang Tiles to Wang Cubes and modify it for multipurpose tiling. We automatically generate isotropic Wang Cubes consisting of 3D patterns or textures to simulate various illustrative effects. Anisotropic Wang Cubes are generated to yield patterns by using the volume data, curvature, and gradient information. We also extend the definition of Wang Cubes into a set of different sized cubes to provide multiresolution volume rendering. Finally, we provide both coherent 3D geometry-based and texture-based rendering frameworks that can be integrated with arbitrary feature exploration methods.

AS we begin the 13th year of TVCG, I am delighted to report that the journal has continued to sustain strong growth and leadership over the past four years. Our submission rate continues to grow at more than 20 percent per year. The average time from submission to decision continues to decrease with the diligent work of our editorial board and the IEEE Computer Society staff. In fact, for the 22 months ending 1 October 2006, this average time was just 79 days! Through the excellent work of the IEEE Computer Society staff and support of both the Publisher, Angela Burgess, and Vice President for Publications, Jon Rokne, we have succeeded in increasing the frequency and number of pages published each year within TVCG, while also decreasing the time from when a paper is accepted until it appears online in the Computer Society’s Digital Library. TVCG is a high-impact, effi cient, and infl uential publication venue for the fi elds of computer graphics, visualization, virtual reality, and haptics. The continued growth of the journal is the culmination of work by many individuals and groups. First of all, the success of the journal relies on the authors and readers of TVCG for their high-quality submissions and subscriptions. The journal cofounders and previous EICs started TVCG on a great course from its inception and have continued to provide valuable advice and guidance. The Associate Editors and reviewers over the past 12 years have succeeded in providing effi cient, high-quality review and assistance in creating excellent archival papers. I am indebted to my excellent Associate EICs, Charles Hansen and Thomas Ertl, as well as the Associate Editors that I have had the opportunity to work with over the past four years. Their work is invaluable to the success of TVCG. The Computer Society publications staff, especially Alicia Stickley, Suzanne Werner, Selina Norman, Niki Gifford, Erin Espriu, and Kathy Santa Maria, have been incredible colleagues and I am extremely thankful for their guidance and hard work in making TVCG successful. In refl ecting on the changes and expansion of TVCG over my tenure as EIC, I am pleased to say that we have succeeded in increasing the publication frequency (four to six times per year), drastically increasing the number of published papers per year, and decreasing the time from submission to publication of material. We have also created open-call special issues on emerging topics in the fi eld, expanded the scope, and embarked on a new partnership with the IEEE Visualization Conference and Symposium on Information Visualization that should continue to thrive. I am grateful to have had the opportunity to be a part of this successful growth of TVCG. It has been an extremely rewarding experience. As my second term as EIC is ending, it is my pleasure to introduce Thomas Ertl as EIC. TVCG is very fortunate to have such an excellent researcher and leader to guide it. Dr. Ertl has been on the editorial board for the past four years and he was also an Associate EIC of TVCG. This past year, an international search for a new EIC was conducted. The search committee received nominations from several very well-qualifi ed candidates and eventually made their recommendations to the IEEE Computer Society Publications Board, with the offi cial appointment being made by the President of the IEEE Computer Society. I look forward to watching TVCG continue to grow under Dr. Ertl’s guidance and expect many great advances in the journal over the next four years.

This paper presents a method to automate rendering parameter selection, simplifying tedious user interaction and improving the usability of visualization systems. Our approach acquires regions-of-interest for a dataset with an eye tracker and simple user interaction. Based on this importance information, we then automatically compute reasonable rendering parameters using a set of heuristic rules adapted from visualization experience and psychophysics experiments. While the parameter selections for a specific visualization task are subjective, our approach provides good starting results that can be refined by the user. Our system improves the interactivity of a visualization system by significantly reducing the necessary parameter selection and providing good initial rendering parameters for newly acquired datasets of similar types.

Abstract Functional approximation of scattered data is a popular technique for compactly representing various types of datasets in computer graphics, including surface, volume, and vector datasets. Typically, sums of Gaussians or similar radial basis functions are used in the functional approximation and PC graphics hardware is used to quickly evaluate and render these datasets. Previously, researchers presented techniques for spatially‐limited spherical Gaussian radial basis function encoding and visualization of volumetric scalar, vector, and multifield datasets. While truncated radially symmetric basis functions are quick to evaluate and simple for encoding optimization, they are not the most appropriate choice for data that is not radially symmetric and are especially problematic for representing linear, planar, and many non‐spherical structures. Therefore, we have developed a volumetric approximation and visualization system using ellipsoidal Gaussian functions which provides greater compression, and visually more accurate encodings of volumetric scattered datasets. In this paper, we extend previous work to use ellipsoidal Gaussians as basis functions, create a rendering system to adapt these basis functions to graphics hardware rendering, and evaluate the encoding effectiveness and performance for both spherical Gaussians and ellipsoidal Gaussians. Categories and Subject Descriptors (according to ACMCCS): I.3.3 [Computer Graphics]: Scientific Visualization, Ellipsoidal Basis Functions, Functional Approximation, Texture Advection

The Network for Computational Nanotechnology (NCN) has developed a science gateway at nanoHUB.org for nanotechnology education and research. Remote users can browse through online seminars and courses, and launch sophisticated nanotechnology simulation tools, all within their web browser. Simulations are supported by a middleware that can route complex jobs to grid supercomputing resources. But what is truly unique about the middleware is the way that it uses hardware accelerated graphics to support both problem setup and result visualization. This paper describes the design and integration of a remote visualization framework into the nanoHUB for interactive visual analytics of nanotechnology simulations. Our services flexibly handle a variety of nanoscience simulations, render them utilizing graphics hardware acceleration in a scalable manner, and deliver them seamlessly through the middleware to the user. Rendering is done only on-demand, as needed, so each graphics hardware unit can simultaneously support many user sessions. Additionally, a novel node distribution scheme further improves our system's scalability. Our approach is not only efficient but also cost-effective. Only a half-dozen render nodes are anticipated to support hundreds of active tool sessions on the nanoHUB. Moreover, this architecture and visual analytics environment provides capabilities that can serve many areas of scientific simulation and analysis beyond nanotechnology with its ability to interactively analyze and visualize multivariate scalar and vector fields.

Meteorological research involves the analysis of multi-field, multi-scale, and multi-source data sets. In order to better understand these data sets, models and measurements at different resolutions must be analyzed. Unfortunately, traditional atmospheric visualization systems only provide tools to view a limited number of variables and small segments of the data. These tools are often restricted to two-dimensional contour or vector plots or three-dimensional isosurfaces. The meteorologist must mentally synthesize the data from multiple plots to glean the information needed to produce a coherent picture of the weather phenomenon of interest. In order to provide better tools to meteorologists and reduce system limitations, we have designed an integrated atmospheric visual analysis and exploration system for interactive analysis of weather data sets. Our system allows for the integrated visualization of 1D, 2D, and 3D atmospheric data sets in common meteorological grid structures and utilizes a variety of rendering techniques. These tools provide meteorologists with new abilities to analyze their data and answer questions on regions of interest, ranging from physics-based atmospheric rendering to illustrative rendering containing particles and glyphs. In this paper, we will discuss the use and performance of our visual analysis for two important meteorological applications. The first application is warm rain formation in small cumulus clouds. Here, our three-dimensional, interactive visualization of modeled drop trajectories within spatially correlated fields from a cloud simulation has provided researchers with new insight. Our second application is improving and validating severe storm models, specifically the Weather Research and Forecasting (WRF) model. This is done through correlative visualization of WRF model and experimental Doppler storm data.

Scientific illustrations use accepted conventions and methodologies to effectively convey object properties and improve our understanding. We present a method to illustrate volume datasets by emulating example illustrations. As with technical illustrations, our volume illustrations more clearly delineate objects, enrich details, and artistically visualize volume datasets. For both color and scalar 3D volumes, we have developed an automatic color transfer method based on the clustering and similarities in the example illustrations and volume sources. As an extension to 2D Wang tiles, we provide a new, general texture synthesis method for Wang cubes that solves the edge discontinuity problem. We have developed a 2D illustrative slice viewer and a GPU-based direct volume rendering system that uses these non-periodic 3D textures to generate illustrative results similar to the 2D examples. Both applications simulate scientific illustrations to provide more information than the original data and visualize objects more effectively, while only requiring simple user interaction.

In this work we present a hardware-accelerated direct volume rendering system for visualizing multivariate wave functions in semiconducting quantum dot (QD) simulations. The simulation data contains the probability density values of multiple electron orbitals for up to tens of millions of atoms, computed by the NEMO3-D quantum device simulator software run on large-scale cluster architectures. These atoms form two interpenetrating crystalline face centered cubic lattices (FCC), where each FCC cell comprises the eight corners of a cubic cell and six additional face centers. We have developed compact representation techniques for the FCC lattice within PC graphics hardware texture memory, hardware-accelerated linear and cubic reconstruction schemes, and new multi-field rendering techniques utilizing logarithmic scale transfer functions. Our system also enables the user to drill down through the simulation data and execute statistical queries using general-purpose computing on the GPU (GPGPU).

Understanding and analyzing complex volumetrically varying data is a difficult problem. Many computational visualization techniques have had only limited success in succinctly portraying the structure of three-dimensional turbulent flow. Motivated by both the extensive history and success of illustration and photographic flow visualization techniques, we have developed a new interactive volume rendering and visualization system for flows and volumes that simulates and enhances traditional illustration, experimental advection, and photographic flow visualization techniques. Our system uses a combination of varying focal and contextual illustrative styles, new advanced two-dimensional transfer functions, enhanced Schlieren and shadowgraphy shaders, and novel oriented structure enhancement techniques to allow interactive visualization, exploration, and comparative analysis of scalar, vector, and time-varying volume datasets. Both traditional illustration techniques and photographic flow visualization techniques effectively reduce visual clutter by using compact oriented structure information to convey three-dimensional structures. Therefore, a key to the effectiveness of our system is using one-dimensional (Schlieren and shadowgraphy) and two-dimensional (silhouette) oriented structural information to reduce visual clutter, while still providing enough three-dimensional structural information for the user's visual system to understand complex three-dimensional flow data. By combining these oriented feature visualization techniques with flexible transfer function controls, we can visualize scalar and vector data, allow comparative visualization of flow properties in a succinct, informative manner, and provide continuity for visualizing time-varying datasets.

Many techniques have already been proposed to improve the efficiency of maximum intensity projection (MIP) volume rendering, but none of them considered the possible hypothesis of a better complexity than either O( n ) for finding the maximum value of n samples along a ray or O( n 3 ) for an object-order algorithm. Here, we fully model and analyze the use of octrees for MIP, and we mathematically show that the average MIP complexity can be reduced to O( n 2 ) for an object-order algorithm, or to O(log( n )) per ray when using the image-order variant of our algorithm. Therefore, this improvement establishes a major advance for interactive MIP visualization of large-volume data.In parallel, we also present an object-order implementation of our algorithm, satisfying the theoretical O( n 2 ) result. It is based on hierarchical occlusion maps that perform on-the-fly visibility of the data, and our results show that it is the most efficient solution for MIP available to date.

The evolution of computational science over the last decade has resulted in a dramatic increase in raw problem solving capabilities. This growth has given rise to advances in scientific and engineering simulations that have put a high demand on tools for high-performance large-scale data exploration and analysis. These simulations have the potential to generate large amounts of data. Humans, however are relatively poor at gaining insight from raw numerical data, and as a result, have used visualization as a tool for understanding, interpreting and exploring data of all types and sizes. Allowing for efficient visual explorations of data, however, requires that the ratio of knowledge gained versus the cost of the visualization be maximized. This, in turn, mandates the integration of principles from human perception. Understanding perception as it relates to visualization requires that we understand not only the biology of the human visual system, but principles from vision theory, and perceptual psychology as well. This panel is the result of bringing together practioners and researchers from a broad spectrum of interests relating to the ability to maximize the amount of information that is effectively perceived from a given visualization. Position statements will be given by researchers interested in perceptual psychology and the perception of natural images, integrating art and design principles, non-photorealistic rendering techniques, and the use of global illumination methods to provide benefical perceptual cues.

Advances in graphics hardware and rendering methods are shaping the future of visualization. For example, programmable graphics processors are redefining the traditional visualization cycle. In some cases it is now possible to run the computational simulation and associated visualization side-by-side on the same chip. Moreover, global illumination and non-photorealistic effects promise to deliver imagery which enables greater insight into high resolution, multivariate, and higher-dimensional data. The panelists will offer distinct viewpoints on the direction of future graphics hardware and its potential impact on visualization, and on the nature of advanced visualizationrelated tools and techniques. Presentation of these viewpoints will be followed by audience participation in the form of a question and answer period moderated by the panel organizer.

Hardware-accelerated direct volume rendering of unstructured volumetric meshes is often based on tetrahedral cell projection, in particular, the projected tetrahedra (PT) algorithm and its variants. Unfortunately, even implementations of the most advanced variants of the PT algorithm are very prone to rendering artifacts. In this work, we identify linear interpolation in screen coordinates as a cause for significant rendering artifacts and implement the correct perspective interpolation for the PT algorithm with programmable graphics hardware. We also demonstrate how to use features of modern graphics hardware to improve the accuracy of the coloring of individual tetrahedra and the compositing of the resulting colors, in particular, by employing a logarithmic scale for the preintegrated color lookup table, using textures with high color resolution, rendering to floating-point color buffers, and alpha dithering. Combined with a correct visibility ordering, these techniques result in the first implementation of the PT algorithm without objectionable rendering artifacts. Apart from the important improvement in rendering quality, our approach also provides a test bed for different implementations of the PT algorithm that allows us to study the particular rendering artifacts introduced by these variants.

Abstract Rapid, visual understanding of volumetric datasets is a crucial outcome of a good volume rendering application, but few current volume rendering systems deliver this result. Our goal is to reduce the volumetric surfing that is required to understand volumetric features by conveying more information in fewer images. In order to achieve this goal, and in contrast with most current methods which still use optical models and alpha blending, our approach reintroduces the order‐independent contribution of every sample along the ray in order to have an equiprobable visualization of all the volume samples. Therefore, we demonstrate how order independent sampling can be suitable for fast volume understanding, show useful extensions to MIP and X‐ray like renderings, and, finally, point out the special advantage of using stereo visualization in these models to circumvent the lack of depth cues. Categories and Subject Descriptors: I.3.3 [Computer Graphics]: Picture/Image, Generation, I.3.7 [Computer Graphics]: Three‐Dimensional graphics and realism.

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Volume illustration is a developing trend in volume visualization, focused on conveying volume information effectively by enhancing interesting features of the volume and omitting insignificant data. However, the calculations involved have limited the illustration process to non-interactive rendering. We have developed a new interactive volume illustration system (IVIS) that harnesses the power of programmable graphics processors, and includes a novel approach for feature halo enhancement. This interactive illustration system is a powerful tool for exploration and analysis of volumetric datasets.

Weather visualization is a difficult problem because it comprises volumetric multi-field data and traditional surface-based approaches obscure details of the complex three-dimensional structure of cloud dynamics. Therefore, visually accurate volumetric multi-field visualization of storm scale and cloud scale data is needed to effectively and efficiently communicate vital information to weather forecasters, improving storm forecasting, atmospheric dynamics models, and weather spotter training. We have developed a new approach to multi-field visualization that uses field specific, physically-based opacity, transmission, and lighting calculations per-field for the accurate visualization of storm and cloud scale weather data. Our approach extends traditional transfer function approaches to multi-field data and to volumetric illumination and scattering.

Simulating hand-drawn illustration can succinctly express information in a manner that is communicative and informative. We present a framework for an interactive direct stipple rendering of volume and surface-based objects. By combining the principles of artistic and scientific illustration, we explore several feature enhancement techniques to create effective, interactive visualizations of scientific and medical data sets. We also introduce a rendering mechanism that generates appropriate point lists at all resolutions during an automatic preprocess and modifies rendering styles through different combinations of these feature enhancements. The new system is an effective way to interactively preview large, complex volume and surface data sets in a concise, meaningful, and illustrative manner. Stippling is effective for many applications and provides a quick and efficient method to investigate both volume and surface models.

Direct volume rendering is a commonly used technique in visualization applications. Many of these applications require sophisticated shading models to capture subtle lighting effects and characteristics of volumetric data and materials. Many common objects and natural phenomena exhibit visual quality that cannot be captured using simple lighting models or cannot be solved at interactive rates using more sophisticated methods. We present a simple yet effective interactive shading model which captures volumetric light attenuation effects to produce volumetric shadows and the subtle appearance of translucency. We also present a technique for volume displacement or perturbation that allows realistic interactive modeling of high frequency detail for real and synthetic volumetric data.

Simulating hand-drawn illustration techniques can succinctly express information in a manner that is communicative and informative. We present a framework for an interactive direct volume illustration system that simulates traditional stipple drawing. By combining the principles of artistic and scientific illustration, we explore several feature enhancement techniques to create effective, interactive visualizations of scientific and medical datasets. We also introduce a rendering mechanism that generates appropriate point lists at all resolutions during an automatic preprocess, and modifies rendering styles through different combinations of these feature enhancements. The new system is an effective way to interactively preview large, complex volume datasets in a concise, meaningful, and illustrative manner. Volume stippling is effective for many applications and provides a quick and efficient method to investigate volume models.

Photographic volumes present a unique, interesting challenge for volume rendering. In photographic volumes, the voxel color is pre-determined, making color selection through transfer functions unnecessary. However, photographic data does not contain a clear mapping from the multi-valued color values to a scalar density or opacity, making projection and compositing much more difficult than with traditional volumes. Moreover, because of the nonlinear nature of color spaces, there is no meaningful norm for the multi-valued voxels. Thus, the individual color channels of photographic data must be treated as incomparable data tuples rather than as vector values. Traditional differential geometric tools, such as intensity gradients, density and Laplacians, are distorted by the nonlinear non-orthonormal color spaces that are the domain of the voxel values. We have developed different techniques for managing these issues while directly rendering volumes from photographic data. We present and justify the normalization of color values by mapping RGB values to the CIE L*u*v* color space. We explore and compare different opacity transfer functions that map three-channel color values to opacity. We apply these many-to-one mappings to the original RGB values as well as to the voxels after conversion to L*u*v* space. Direct rendering using transfer functions allows us to explore photographic volumes without having to commit to an a-priori segmentation that might mask fine variations of interest. We empirically compare the combined effects of each of the two color spaces with our opacity transfer functions using source data from the Visible Human project.

This paper describes a minimally immersive interactive system for flow visualization of multivariate volumetric data. The system, SFA, uses perceptually motivated rendering to increase the quantity and clarity of information perceived. Proprioception, stereopsis, perceptually motivated shape visualization, and three-dimensional interaction are combined in SFA to allow the three-dimensional volumetric visualization, manipulation, navigation, and analysis of multivariate, time-varying flow data.

Accurately and automatically conveying the structure of a volume model is a problem which has not been fully solved by existing volume rendering approaches. Physics-based volume rendering approaches create images which may match the appearance of translucent materials in nature but may not embody important structural details. Transfer function approaches allow flexible design of the volume appearance but generally require substantial hand-tuning for each new data set in order to be effective. We introduce the volume illustration approach, combining the familiarity of a physics-based illumination model with the ability to enhance important features using non-photorealistic rendering techniques. Since the features to be enhanced are defined on the basis of local volume characteristics rather than volume sample values, the application of volume illustration techniques requires less manual tuning than the design of a good transfer function. Volume illustration provides a flexible unified framework for enhancing the structural perception of volume models through the amplification of features and the addition of illumination effects.

Accurately and automatically conveying the structure of a volume model is a problem that has not been fully solved by existing volume rendering approaches. Physics-based volume rendering approaches create images which may match the appearance of translucent materials in nature but may not embody important structural details. Transfer function approaches allow flexible design of the volume appearance but generally require substantial hand-tuning for each new data set in order to be effective. We introduce the volume illustration approach, combining the familiarity of a physics-based illumination model with the ability to enhance important features using non-photorealistic rendering techniques. Since the features to be enhanced are defined on the basis of local volume characteristics rather than volume sample values, the application of volume illustration techniques requires less manual tuning than the design of a good transfer function. Volume illustration provides a flexible unified framework for enhancing structural perception of volume models through the amplification of features and the addition of illumination effects.

—————————— ✦ —————————— HIS issue contains extended versions of five outstanding papers from the IEEE Visualization ‘99 conference and the IEEE 1999 Symposium on Information Visualization (InfoVis ‘99) held in San Francisco, California. The authors of these papers were invited to significantly extend their work and submit journal quality papers for this special issue. The papers were then reviewed and revised according to suggestions by expert referees. These papers represent a sampling of the state-of-the-art research from a diverse collection of research areas presented at IEEE Visualization ‘99 and InfoVis ‘99. The first two papers discuss important systems issues and new techniques for visualization of large scale data sets. The next two papers discuss new techniques in classical areas of visualization research: flow visualization and medical visualization. The final paper presents new research in one of the newest areas of visualization research, information visualization. Systems issues and visualization performance are still important research problems because dataset sizes are growing just as rapidly (or more rapidly) than processor speed, data transfer rates, and memory capacity. Sutton and Hansen present a new data structure and algorithm for interactive visualization and isosurface extraction of timevarying datasets. Their Temporal-Branch-on-Need Octree reduces I/O bottlnecks and achieves high performance isosurface extraction for time-varying fields. New techniques for the time-critical rendering of high-complexity scenes is presented in the paper by Klosowski and Silva. Their system takes the approach of producing partially-correct images using a new visibility culling algorithm that can be used for time-critical rendering of visualization data, as well as architectural scenes and other geometric datasets. There are still many open research problems in classic application areas of visualization research. The paper by Kindlmann, Weinstein, and Hart presents new techniques for volume rendering medical data from a relatively new source, diffusion-weighted magnetic resonance imaging. They present extensions to previous volume visualization techniques, including barycentric opacity maps, hue-maps, and lit-tensors. Diewald, Preusser, and Rumpf present new techniques in another important area of visualization research: vector field visualization. Their work uses nonlinear anisotropic diffusion to aid the perception of complex flow pattens and flow fields. The work by Fua, Ward, and Rundensteiner presents new techniques in one of the newest areas of visualization research: information visualization. Their work on structure-based brushes helps to solve the navigation problem for visualization of hierarchically organized data. These five papers demonstrate the range of diverse research and the high-quality innovation presented at IEEE Visualization ‘99 and InfoVis ‘99.

The paper describes new techniques for minimally immersive visualization of 3D scalar and vector fields, and visualization of document corpora. In our glyph based visualization system, the user interacts with the 3D volume of glyphs using a pair of button-enhanced 3D position and orientation trackers. The user may also examine the volume using an interactive lens, which is a rectangle that slices through the 3D volume and displays scalar information on its surface. A lens allows the display of scalar data in the 3D volume using a contour diagram, and a texture based volume rendering.

A framework of complementary behavior has been proposed which maintains that direct-manipulation and speech interfaces have reciprocal strengths and weaknesses. This suggests that user interface performance and acceptance may increase by adopting a multimodal approach that combines speech and direct manipulation. This effort examined the hypothesis that the speed, accuracy, and acceptance of multimodal speech and direct-manipulation interfaces will increase when the modalities match the perceptual structure of the input attributes. A software prototype that supported a typical biomedical data collection task was developed to test this hypothesis. A group of 20 clinical and veterinary pathologists evaluated the prototype in an experimental setting using repeated measures. The results of this experiment supported the hypothesis that the perceptual structure of an input task is an important consideration when designing a multimodal computer interface. Task completion time, the number of speech errors, and user acceptance improved when interface best matched the perceptual structure of the input attributes.

This paper describes a minimally immersive interactive system for visualization of multivariate volumetric data. The system, SFA, uses glyph-based volume rendering which does not suffer the initial costs of isosurface rendering or voxel-based volume rendering, while offering the capability of viewing the entire volume. Glyph rendering also allows the simultaneous display of multiple data values per volume location. Two-handed interaction using three-space magnetic trackers and stereoscopic viewing are combined to produce a minimally immersive volumetric visualization system that enhances the user's three-dimensional perception of the data. We describe the usefulness of this system for visualizing volumetric scalar and vector data. SFA allows the three-dimensional volumetric visualization, manipulation, navigation, and analysis of multivariate, time-varying volumetric data, increasing the quantity and clarity of the information conveyed from the visualization system.

This paper advocates the use of a group of renderers rather than any specific rendering method. We describe a bundle containing four alternative approaches to visualizing volume data. One new approach uses realistic volumetric gas rendering techniques to produce photo-realistic images and animations. The second uses ray casting that is based on a simpler illumination model and is mainly centered around a versatile new tool for the design of transfer functions. The third method employs a simple illumination model and rapid rendering mechanisms to provide efficient preview capabilities. The last one reduces data magnitude by displaying the most visible components and exploits rendering hardware to provide real time browsing capabilities. We show that each rendering tool provides a unique service and demonstrate the combined utility of our group of volume renderers in computational fluid dynamic (CFD) visualization. While one tool allows the explorer to render rapidly for navigation through the data, another tool allows one to emphasize data features (e.g., shock waves), and yet another tool allows one to realistically render the data. We believe that only through the deployment of groups of renderers will the scientist be well served and equipped to form numerous perspectives of the same dataset, each providing different insights into the data.>

The paper describes three alternative volume rendering approaches to visualizing computational fluid dynamics (CFD) data. One new approach uses realistic volumetric gas rendering techniques to produce photo-realistic images and animations from scalar CFD data. The second uses ray casting that is based an a sampler illumination model and is mainly centered around a versatile new tool for the design of transfer functions. The third method employs a simple illumination model and rapid rendering mechanisms to provide efficient preview capabilities. These tools provide a large range of volume rendering capabilities to be used by the CFD explorer to render rapidly for navigation through the data, to emphasize data features (e.g., shock waves) with a specific transfer function, or to present a realistic rendition of the model.>

Abstract This paper describes the design and animation of volume density functions for creating images containing gases, solid textures and hypertextures. The development of functions for producing images and animations of gases is explained in detail. Procedures for modelling the geometry of various gases are described. Next, animation techniques for volume density functions are described and examples of the animation of these functions are presented, including steam rising from a teacup, wind effects, marble forming, and gas and liquid flow into an opening in a wall. Solid spaces are introduced as a unifying approach to describing and animating object attributes.

This paper describes a new technique that efficiently combines volume rendering and scanline a-buffer techniques. This technique is useful for combining all types of volume-rendered objects with scanline rendered objects and is especially useful for rendering scenes containing gaseous phenomena such as clouds, fog, and smoke. The rendering and animation of these phenomena has been a difficult problem in computer graphics.A new algorithm for realistically modeling and animating gaseous phenomena is presented, providing true three-dimensional volumes of gas. The gases are modeled using turbulent flow based solid texturing to define their geometry and are animated based on turbulent flow simulations. A low albedo illumination model is used that takes into consideration self-shadowing of the volumes.