DevLan/vispubs huggingface dataset:
TanGeoMS: Tangible Geospatial Modeling System
Authors: Laura Tateosian, Helena Mitásová, Brendan Harmon, Brent Fogleman, Katherine Weaver, Russell S. Harmon
Published at Vis, 2010

We present TanGeoMS, a tangible geospatial modeling visualization system that couples a laser scanner, projector, and a flexible physical three-dimensional model with a standard geospatial information system (GIS) to create a tangible user interface for terrain data. TanGeoMS projects an image of real-world data onto a physical terrain model. Users can alter the topography of the model by modifying the clay surface or placing additional objects on the surface. The modified model is captured by an overhead laser scanner then imported into a GIS for analysis and simulation of real-world processes. The results are projected back onto the surface of the model providing feedback on the impact of the modifications on terrain parameters and simulated processes. Interaction with a physical model is highly intuitive, allowing users to base initial design decisions on geospatial data, test the impact of these decisions in GIS simulations, and use the feedback to improve their design. We demonstrate the system on three applications: investigating runoff management within a watershed, assessing the impact of storm surge on barrier islands, and exploring landscape rehabilitation in military training areas.
Two-Phase Mapping for Projecting Massive Data Sets
Authors: Fernando Vieira Paulovich, Cláudio T. Silva, Luis Gustavo Nonato
Published at Vis, 2010

Most multidimensional projection techniques rely on distance (dissimilarity) information between data instances to embed high-dimensional data into a visual space. When data are endowed with Cartesian coordinates, an extra computational effort is necessary to compute the needed distances, making multidimensional projection prohibitive in applications dealing with interactivity and massive data. The novel multidimensional projection technique proposed in this work, called Part-Linear Multidimensional Projection (PLMP), has been tailored to handle multivariate data represented in Cartesian high-dimensional spaces, requiring only distance information between pairs of representative samples. This characteristic renders PLMP faster than previous methods when processing large data sets while still being competitive in terms of precision. Moreover, knowing the range of variation for data instances in the high-dimensional space, we can make PLMP a truly streaming data projection technique, a trait absent in previous methods.
Uncertainty-Aware Guided Volume Segmentation
Authors: Jörg-Stefan Praßni, Timo Ropinski, Klaus H. Hinrichs
Published at Vis, 2010

Although direct volume rendering is established as a powerful tool for the visualization of volumetric data, efficient and reliable feature detection is still an open topic. Usually, a tradeoff between fast but imprecise classification schemes and accurate but time-consuming segmentation techniques has to be made. Furthermore, the issue of uncertainty introduced with the feature detection process is completely neglected by the majority of existing approaches.In this paper we propose a guided probabilistic volume segmentation approach that focuses on the minimization of uncertainty. In an iterative process, our system continuously assesses uncertainty of a random walker-based segmentation in order to detect regions with high ambiguity, to which the user's attention is directed to support the correction of potential misclassifications. This reduces the risk of critical segmentation errors and ensures that information about the segmentation's reliability is conveyed to the user in a dependable way. In order to improve the efficiency of the segmentation process, our technique does not only take into account the volume data to be segmented, but also enables the user to incorporate classification information. An interactive workflow has been achieved by implementing the presented system on the GPU using the OpenCL API. Our results obtained for several medical data sets of different modalities, including brain MRI and abdominal CT, demonstrate the reliability and efficiency of our approach.
VDVR: Verifiable Volume Visualization of Projection-Based Data
Authors: Ziyi Zheng, Wei Xu 0020, Klaus Mueller 0001
Published at Vis, 2010

Practical volume visualization pipelines are never without compromises and errors. A delicate and often-studied component is the interpolation of off-grid samples, where aliasing can lead to misleading artifacts and blurring, potentially hiding fine details of critical importance. The verifiable visualization framework we describe aims to account for these errors directly in the volume generation stage, and we specifically target volumetric data obtained via computed tomography (CT) reconstruction. In this case the raw data are the X-ray projections obtained from the scanner and the volume data generation process is the CT algorithm. Our framework informs the CT reconstruction process of the specific filter intended for interpolation in the subsequent visualization process, and this in turn ensures an accurate interpolation there at a set tolerance. Here, we focus on fast trilinear interpolation in conjunction with an octree-type mixed resolution volume representation without T-junctions. Efficient rendering is achieved by a space-efficient and locality-optimized representation, which can straightforwardly exploit fast fixed-function pipelines on GPUs.
View-Dependent Streamlines for 3D Vector fields
Authors: Stéphane Marchesin, Cheng-Kai Chen, Chris Ho, Kwan-Liu Ma
Published at Vis, 2010

This paper introduces a new streamline placement and selection algorithm for 3D vector fields. Instead of considering the problem as a simple feature search in data space, we base our work on the observation that most streamline fields generate a lot of self-occlusion which prevents proper visualization. In order to avoid this issue, we approach the problem in a view-dependent fashion and dynamically determine a set of streamlines which contributes to data understanding without cluttering the view. Since our technique couples flow characteristic criteria and view-dependent streamline selection we are able achieve the best of both worlds: relevant flow description and intelligible, uncluttered pictures. We detail an efficient GPU implementation of our algorithm, show comprehensive visual results on multiple datasets and compare our method with existing flow depiction techniques. Our results show that our technique greatly improves the readability of streamline visualizations on different datasets without requiring user intervention.
Visual Exploration of High Dimensional Scalar Functions
Authors: Samuel Gerber, Peer-Timo Bremer, Valerio Pascucci, Ross T. Whitaker
Published at Vis, 2010

An important goal of scientific data analysis is to understand the behavior of a system or process based on a sample of the system. In many instances it is possible to observe both input parameters and system outputs, and characterize the system as a high-dimensional function. Such data sets arise, for instance, in large numerical simulations, as energy landscapes in optimization problems, or in the analysis of image data relating to biological or medical parameters. This paper proposes an approach to analyze and visualizing such data sets. The proposed method combines topological and geometric techniques to provide interactive visualizations of discretely sampled high-dimensional scalar fields. The method relies on a segmentation of the parameter space using an approximate Morse-Smale complex on the cloud of point samples. For each crystal of the Morse-Smale complex, a regression of the system parameters with respect to the output yields a curve in the parameter space. The result is a simplified geometric representation of the Morse-Smale complex in the high dimensional input domain. Finally, the geometric representation is embedded in 2D, using dimension reduction, to provide a visualization platform. The geometric properties of the regression curves enable the visualization of additional information about each crystal such as local and global shape, width, length, and sampling densities. The method is illustrated on several synthetic examples of two dimensional functions. Two use cases, using data sets from the UCI machine learning repository, demonstrate the utility of the proposed approach on real data. Finally, in collaboration with domain experts the proposed method is applied to two scientific challenges. The analysis of parameters of climate simulations and their relationship to predicted global energy flux and the concentrations of chemical species in a combustion simulation and their integration with temperature.
Visual Optimality and Stability Analysis of 3DCT Scan Positions
Authors: Artem Amirkhanov, Christoph Heinzl, Michael Reiter, M. Eduard Gröller
Published at Vis, 2010

Industrial cone-beam X-Ray computed tomography (CT) systems often face problems due to artifacts caused by a bad placement of the specimen on the rotary plate. This paper presents a visual-analysis tool for CT systems, which provides a simulation-based preview and estimates artifacts and deviations of a specimen's placement using the corresponding 3D geometrical surface model as input. The presented tool identifies potentially good or bad placements of a specimen and regions of a specimen, which cause the major portion of artefacts. The tool can be used for a preliminary analysis of the specimen before CT scanning, in order to determine the optimal way of placing the object. The analysis includes: penetration lengths, placement stability and an investigation in Radon space. Novel visualization techniques are applied to the simulation data. A stability widget is presented for determining the placement parameters' robustness. The performance and the comparison of results provided by the tool compared with real world data is demonstrated using two specimens.
Visualization by Proxy: A Novel Framework for Deferred Interaction with Volume Data
Authors: Anna Tikhonova, Carlos D. Correa, Kwan-Liu Ma
Published at Vis, 2010

Interactivity is key to exploration of volume data. Interactivity may be hindered due to many factors, e.g. large data size,high resolution or complexity of a data set, or an expensive rendering algorithm. We present a novel framework for visualizing volumedata that enables interactive exploration using proxy images, without accessing the original 3D data. Data exploration using directvolume rendering requires multiple (often redundant) accesses to possibly large amounts of data. The notion of visualization by proxyrelies on the ability to defer operations traditionally used for exploring 3D data to a more suitable intermediate representation forinteraction - proxy images. Such operations include view changes, transfer function exploration, and relighting. While previous workhas addressed specific interaction needs, we provide a complete solution that enables real-time interaction with large data sets andhas low hardware and storage requirements.
Visualizing Flow Trajectories Using Locality-based Rendering and Warped Curve Plots
Authors: Chad Jones, Kwan-Liu Ma
Published at Vis, 2010

In flow simulations the behavior and properties of particle trajectories often depend on the physical geometry contained in the simulated environment. Understanding the flow in and around the geometry itself is an important part of analyzing the data. Previous work has often utilized focus+context rendering techniques, with an emphasis on showing trajectories while simplifying or illustratively rendering the physical areas. Our research instead emphasizes the local relationship between particle paths and geometry by using a projected multi-field visualization technique. The correlation between a particle path and its surrounding area is calculated on-the-fly and displayed in a non-intrusive manner. In addition, we support visual exploration and comparative analysis through the use of linked information visualization, such as manipulatable curve plots and one-on-one similarity plots. Our technique is demonstrated on particle trajectories from a groundwater simulation and a computer room airflow simulation, where the flow of particles is highly influenced by the dense geometry.
Volumetric Modeling in Laser BPH Therapy Simulation
Authors: Nan Zhang, Xiangmin Zhou, Yunhe Shen, Robert M. Sweet
Published at Vis, 2010

In this paper, we introduce a novel application of volume modeling techniques on laser Benign Prostatic Hyperplasia (BPH) therapy simulation. The core technique in our system is an algorithm for simulating the tissue vaporization process by laser heating. Different from classical volume CSG operations, our technique takes experimental data as the guidance to determine the vaporization amount so that only a specified amount of tissue is vaporized in each time. Our algorithm uses a predictor-corrector strategy. First, we apply the classical CSG algorithm on a tetrahedral grid based distance field to estimate the vaporized tissue amount. Then, a volume-correction phase is applied on the distance field. To improve the performance, we further propose optimization approaches for efficient implementation.
World Lines
Authors: Jürgen Waser, Raphael Fuchs, Hrvoje Ribicic, Benjamin Schindler, Günter Blöschl, M. Eduard Gröller
Published at Vis, 2010

In this paper we present World Lines as a novel interactive visualization that provides complete control over multiple heterogeneous simulation runs. In many application areas, decisions can only be made by exploring alternative scenarios. The goal of the suggested approach is to support users in this decision making process. In this setting, the data domain is extended to a set of alternative worlds where only one outcome will actually happen. World Lines integrate simulation, visualization and computational steering into a single unified system that is capable of dealing with the extended solution space. World Lines represent simulation runs as causally connected tracks that share a common time axis. This setup enables users to interfere and add new information quickly. A World Line is introduced as a visual combination of user events and their effects in order to present a possible future. To quickly find the most attractive outcome, we suggest World Lines as the governing component in a system of multiple linked views and a simulation component. World Lines employ linking and brushing to enable comparative visual analysis of multiple simulations in linked views. Analysis results can be mapped to various visual variables that World Lines provide in order to highlight the most compelling solutions. To demonstrate this technique we present a flooding scenario and show the usefulness of the integrated approach to support informed decision making.
A closer look at note taking in the co-located collaborative visual analytics process
Authors: Narges Mahyar, Ali Sarvghad, Melanie Tory
Published at VAST, 2010

This paper highlights the important role that record-keeping (i.e. taking notes and saving charts) plays in collaborative data analysis within the business domain. The discussion of record-keeping is based on observations from a user study in which co-located teams worked on collaborative visual analytics tasks using large interactive wall and tabletop displays. Part of our findings is a collaborative data analysis framework that encompasses note taking as one of the main activities. We observed that record-keeping was a critical activity within the analysis process. Based on our observations, we characterize notes according to their content, scope, and usage, and describe how they fit into a process of collaborative data analysis. We then discuss suggestions for the design of collaborative visual analytics tools.
A continuous analysis process between desktop and collaborative visual analytics environments
Authors: Dong Hyun Jeong, Evan A. Suma, Thomas Butkiewicz, William Ribarsky, Remco Chang
Published at VAST, 2010

Since its inception, the field of visual analytics has undergone tremendous growth in understanding how to create interactive visual tools to solve analytical problems. However, with few exceptions, most of these tools have been designed for single users in desktop environments. While often effective on their own, most single-user systems do not reflect the collaborative nature of solving real-world analytical tasks. Many intelligence analysts, for example, have been observed to switch repeatedly between working alone and collaborating with members of a small team. In this paper, we propose that a complete visual analytical system designed for solving real-world tasks ought to have two integrated components: a single-user desktop system and a mirroring system suitable for a collaborative environment.
A radial visualization tool for depicting hierarchically structured video content
Authors: Tobias Ruppert, Jörn Kohlhammer
Published at VAST, 2010

The visual analysis of video content is an important research topic due to the huge amount of video data that is generated every day. Annotating this data will become a major problem since the amount of videos further increases. With this work we introduce a system that combines a visualization tool with automatic video segmentation techniques and a characteristic key-frame extraction. A summary of the content of a whole video in one view is realized. Furthermore, the user can interactively browse through the video via our visualization interface to get more detailed information. The system is adapted to two application scenarios and a third application is discussed for future work.
A Visual Analytics approach to identifying protein structural constraints
Authors: William C. Ray
Published at VAST, 2010

Predicting protein structures has long been a grand-challenge problem. Fine-grained computational simulation of folding events from a protein's synthesis to its final stable structure remains computationally intractable. Therefore, methods which derive constraints from other sources are attractive. To date, constraints derived from known structures have proven to be highly successful. However, these cannot be applied to molecules with no identifiable neighbors having already-determined structures. For such molecules, structural constraints must be derived in other ways. One popular approach has been the statistical analysis of large families of proteins, with the hope that residues that “change together” (co-evolve) imply that those residues are in contact. Unfortunately, despite repeated attempts to use this data to deduce structural constraints, this approach has met with minimal success. The consensus of current literature concludes that there is simply too little information contained within the correlated mutations of many protein families to reliably and generally predict structural constraints. Recent work in my laboratory challenges this conclusion. For some time we have been developing methods (MAVL/StickWRLD) to visualize the pattern of co-evolved mutations within sequence families. While our analysis of individual correlations agrees with the literature consensus, we have recently discovered that the visualized pattern of correlations is highly suggestive of structural relationships. In our preliminary test cases, human researchers can unambiguously determine many positive structural constraints by visual analysis of statistical sequence information alone, often with no training on interpretation of the visualization results. Herein we report the visualization design that supports this Visual Analytics approach to identifying high-confidence hypotheses about protein folding from protein sequence, and illustrate preliminary results from this research. Our approach entails a higher-dimensional extension of parallel coordinates which illuminates distant shared sub-tuples of the vectors representing each protein sequence when these sub-tuples occur with an over abundance compared to expectations. It simultaneously eliminates all representations of tuples which occur with frequency near the expected norm. The result is a minimally-occluded representation of outlier, and only outlier co-occurrences within the sequence families.
A visual analytics approach to model learning
Authors: Supriya Garg, I. V. Ramakrishnan, Klaus Mueller 0001
Published at VAST, 2010

The process of learning models from raw data typically requires a substantial amount of user input during the model initialization phase. We present an assistive visualization system which greatly reduces the load on the users and makes the process of model initialization and refinement more efficient, problem-driven, and engaging. Utilizing a sequence segmentation task with a Hidden Markov Model as an example, we assign each token in the sequence a feature vector based on its various properties within the sequence. These vectors are then clustered according to similarity, generating a layout of the individual tokens in form of a node link diagram where the length of the links is determined by the feature vector similarity. Users may then tune the weights of the feature vector components to improve the segmentation, which is visualized as a better separation of the clusters. Also, as individual clusters represent different classes, the user can now work at the cluster level to define token classes, instead of labelling one entry at time. Inconsistent entries visually identify themselves by locating at the periphery of clusters, and the user then helps refine the model by resolving these inconsistencies. Our system therefore makes efficient use of the knowledge of its users, only requesting user assistance for non-trivial data items. It so allows users to visually analyse data at a higher, more abstract level, improving scalability.
Adapting Daniel and Wood's modeling approach to interactive visual analytics
Authors: Justin Talbot, Pat Hanrahan
Published at VAST, 2010

This poster describes our progress in developing an interactive linear modeling system that supports the modeling approach described by Daniel and Wood. Our visual interface permits analysts to build sets of possible models and then creates appropriate visualizations to permit human-in-the-loop model comparison and selection.
ALIDA: Using machine learning for intent discernment in visual analytics interfaces
Authors: Tera Marie Green, Ross Maciejewski, Steve DiPaola
Published at VAST, 2010

In this paper, we introduce ALIDA, an Active Learning Intent Discerning Agent for visual analytics interfaces. As users interact with and explore data in a visual analytics environment they are each developing their own unique analytic process. The goal of ALIDA is to observe and record the human-computer interactions and utilize these observations as a means of supporting user exploration; ALIDA does this by using interaction to make decision about user interest. As such, ALIDA is designed to track the decision history (interactions) of a user. This history is then utilized to enhance the user's decision-making process by allowing the user to return to previously visited search states, as well as providing suggestions of other search states that may be of interest based on past exploration modalities. The agent passes these suggestions (or decisions) back to an interactive visualization prototype, and these suggestions are used to guide the user, either by suggesting searches or changes to the visualization view. Current work has tested ALIDA under the exploration of homonyms for users wishing to explore word linkages within a dictionary. Ongoing work includes using ALIDA to guide users in transfer function design for volume rendering within scientific gateways.