DevLan/vispubs huggingface dataset:
Visual Analysis and Dissemination of Scientific Literature Collections with SurVis
Authors: Fabian Beck 0001, Sebastian Koch, Daniel Weiskopf
Published at VAST, 2015

Bibliographic data such as collections of scientific articles and citation networks have been studied extensively in information visualization and visual analytics research. Powerful systems have been built to support various types of bibliographic analysis, but they require some training and cannot be used to disseminate the insights gained. In contrast, we focused on developing a more accessible visual analytics system, called SurVis, that is ready to disseminate a carefully surveyed literature collection. The authors of a survey may use our Web-based system to structure and analyze their literature database. Later, readers of the survey can obtain an overview, quickly retrieve specific publications, and reproduce or extend the original bibliographic analysis. Our system employs a set of selectors that enable users to filter and browse the literature collection as well as to control interactive visualizations. The versatile selector concept includes selectors for textual search, filtering by keywords and meta-information, selection and clustering of similar publications, and following citation links. Agreement to the selector is represented by word-sized sparkline visualizations seamlessly integrated into the user interface. Based on an analysis of the analytical reasoning process, we derived requirements for the system. We developed the system in a formative way involving other researchers writing literature surveys. A questionnaire study with 14 visual analytics experts confirms that SurVis meets the initially formulated requirements.
Visual analysis of route choice behaviour based on GPS trajectories
Authors: Min Lu 0002, Chufan Lai, Tangzhi Ye, Christy Jie Liang, Xiaoru Yuan
Published at VAST, 2015

There are often multiple routes between regions. Many factors potentially affect driver's route choice, such as expected time cost, length etc. In this work, we present a visual analysis system to explore driver's route choice behaviour based on taxi GPS trajectory data. With interactive trajectory filtering, the system constructs feasible routes between regions of interest. Using a rank-based visualization, the attributes of multiple routes are explored and compared. Based on a statistical model, the system supports to verify trajectory-related factors' impact on route choice behaviour. The effectiveness of the system is demonstrated by applying to real trajectory dataset.
Visual Analytics for Development and Evaluation of Order Selection Criteria for Autoregressive Processes
Authors: Thomas Löwe, Emmy-Charlotte Förster, Georgia Albuquerque, Jens-Peter Kreiss, Marcus A. Magnor
Published at VAST, 2015

Order selection of autoregressive processes is an active research topic in time series analysis, and the development and evaluation of automatic order selection criteria remains a challenging task for domain experts. We propose a visual analytics approach, to guide the analysis and development of such criteria. A flexible synthetic model generator-combined with specialized responsive visualizations-allows comprehensive interactive evaluation. Our fast framework allows feedback-driven development and fine-tuning of new order selection criteria in real-time. We demonstrate the applicability of our approach in three use-cases for two general as well as a real-world example.
Visual Analytics for fraud detection and monitoring
Authors: Roger A. Leite, Theresia Gschwandtner, Silvia Miksch, Erich Gstrein, Johannes Kuntner
Published at VAST, 2015

One of the primary concerns of financial institutions is to guarantee security and legitimacy in their services. Being able to detect and avoid fraudulent schemes also enhances the credibility of these institutions. Currently, fraud detection approaches still lack Visual Analytics techniques. We propose a Visual Analytics process that tackles the main challenges in the area of fraud detection.
Visual data quality analysis for taxi GPS data
Authors: Zuchao Wang, Xiaoru Yuan, Tangzhi Ye, Youfeng Hao, Siming Chen 0001, Christy Jie Liang, Qiusheng Li, Haiyang Wang, Yadong Wu
Published at VAST, 2015

We present a novel visual analysis method to systematically discover data quality problems in raw taxi GPS data. It combines semi-supervised active learning and interactive visual exploration. It helps analysts interactively discover unknown data quality problems, and automatically extract known problems. We report analysis results on Beijing taxi GPS data.
Visual Pruner: Visually guided cohort selection for observational studies
Authors: Lauren R. Samuels, Robert A. Greevy Jr.
Published at VAST, 2015

Observational studies are a widely used and challenging class of studies. A key challenge is selecting a study cohort from the available data, or “pruning” the data, in a way that produces both sufficient balance in pre-treatment covariates and an easily described cohort from which results can be generalized. Even with advanced pruning methods, it is often difficult for researchers to see how the cohort is being selected; consequently, these methods are underutilized in research. Visual Pruner is a free, easy-to-use web application that can improve both the credibility and generalizability of observational studies by letting analysts use updatable visual displays of estimated propensity scores and key baseline covariates to refine inclusion criteria. By helping researchers see how covariate distributions in their data relate to the estimated probabilities of treatment assignment, the app lets researchers make pruning decisions based on pre-treatment covariate patterns that are otherwise hard to discover. The app yields a set of inclusion criteria that can be used in conjunction with further statistical analysis in any statistical software.
Visual scalability of spatial ensemble uncertainty
Authors: Sujan Anreddy, Song Zhang 0004, Andrew Mercer 0001, Jamie L. Dyer, J. Edward Swan II
Published at VAST, 2015

Weather Research and Forecasting (WRF) models simulate weather conditions by generating 2D numerical weather prediction ensemble members either through perturbing initial conditions or by changing different parameterization schemes, e.g., cumulus and microphysics schemes. These simulations are often used by weather analysts to analyze the nature of uncertainty attributed by these simulations to forecast weather conditions with good accuracy. The number of simulations used for forecasting is growing with the advent of increase in computing power. Hence, there is a need for providing better visual insights of uncertainty with growing number of ensemble members. We propose a geo visual analytical framework that uses visual analytics approach to resolve visual scalability of these ensemble members. Our approach naturally fits with the workflow of an analyst analyzing ensemble spatial uncertainty. Meteorologists evaluated our framework qualitatively and found it to be effective in acquiring insights of spatial uncertainty associated with multiple ensemble runs that are simulated using multiple parameterization schemes.
Visually and statistically guided imputation of missing values in univariate seasonal time series
Authors: Markus Bögl, Peter Filzmoser, Theresia Gschwandtner, Silvia Miksch, Wolfgang Aigner, Alexander Rind, Tim Lammarsch
Published at VAST, 2015

Missing values are a problem in many real world applications, for example failing sensor measurements. For further analysis these missing values need to be imputed. Thus, imputation of such missing values is important in a wide range of applications. We propose a visually and statistically guided imputation approach, that allows applying different imputation techniques to estimate the missing values as well as evaluating and fine tuning the imputation by visual guidance. In our approach we include additional visual information about uncertainty and employ the cyclic structure of time inherent in the data. Including this cyclic structure enables visually judging the adequateness of the estimated values with respect to the uncertainty/error boundaries and according to the patterns of the neighbouring time points in linear and cyclic (e.g., the months of the year) time.
Visually Exploring Transportation Schedules
Authors: Cesar Palomo, Zhan Guo, Cláudio T. Silva, Juliana Freire
Published at VAST, 2015

Public transportation schedules are designed by agencies to optimize service quality under multiple constraints. However, real service usually deviates from the plan. Therefore, transportation analysts need to identify, compare and explain both eventual and systemic performance issues that must be addressed so that better timetables can be created. The purely statistical tools commonly used by analysts pose many difficulties due to the large number of attributes at tripand station-level for planned and real service. Also challenging is the need for models at multiple scales to search for patterns at different times and stations, since analysts do not know exactly where or when relevant patterns might emerge and need to compute statistical summaries for multiple attributes at different granularities. To aid in this analysis, we worked in close collaboration with a transportation expert to design TR-EX, a visual exploration tool developed to identify, inspect and compare spatio-temporal patterns for planned and real transportation service. TR-EX combines two new visual encodings inspired by Marey's Train Schedule: Trips Explorer for trip-level analysis of frequency, deviation and speed; and Stops Explorer for station-level study of delay, wait time, reliability and performance deficiencies such as bunching. To tackle overplotting and to provide a robust representation for a large numbers of trips and stops at multiple scales, the system supports variable kernel bandwidths to achieve the level of detail required by users for different tasks. We justify our design decisions based on specific analysis needs of transportation analysts. We provide anecdotal evidence of the efficacy of TR-EX through a series of case studies that explore NYC subway service, which illustrate how TR-EX can be used to confirm hypotheses and derive new insights through visual exploration.
Wavelet-based visualization of time-varying data on graphs
Authors: Paola Valdivia, Fabio Dias, Fabiano Petronetto, Cláudio T. Silva, Luis Gustavo Nonato
Published at VAST, 2015

Visualizing time-varying data defined on the nodes of a graph is a challenging problem that has been faced with different approaches. Although techniques based on aggregation, topology, and topic modeling have proven their usefulness, the visual analysis of smooth and/or abrupt data variations as well as the evolution of such variations over time are aspects not properly tackled by existing methods. In this work we propose a novel visualization methodology that relies on graph wavelet theory and stacked graph metaphor to enable the visual analysis of time-varying data defined on the nodes of a graph. The proposed method is able to identify regions where data presents abrupt and mild spacial and/or temporal variation while still been able to show how such changes evolve over time, making the identification of events an easier task. The usefulness of our approach is shown through a set of results using synthetic as well as a real data set involving taxi trips in downtown Manhattan. The methodology was able to reveal interesting phenomena and events such as the identification of specific locations with abrupt variation in the number of taxi pickups.
3D superquadric glyphs for visualizing myocardial motion
Authors: Teodora Chitiboi, Mathias Neugebauer, Susanne Schnell, Michael Markl 0001, Lars Linsen
Published at SciVis, 2015

Various cardiac diseases can be diagnosed by the analysis of myocardial motion. Relevant biomarkers are radial, longitudinal, and rotational velocities of the cardiac muscle computed locally from MR images. We designed a visual encoding that maps these three attributes to glyph shapes according to a barycentric space formed by 3D superquadric glyphs. The glyphs show aggregated myocardial motion information following the AHA model and are displayed in a respective 3D layout.
A bottom-up scheme for user-defined feature exploration in vector field ensembles
Authors: Richen Liu, Hanqi Guo 0001, Xiaoru Yuan
Published at SciVis, 2015

Most of the existing approaches to visualize vector field ensembles are achieved by visualizing the uncertainty of individual variables from different simulation runs. However, the comparison of the derived feature or user-defined feature, such as the vortex in ensemble flow is also of vital significance since they often make more sense according to the domain knowledge. In this work, we present a framework to extract user-defined feature from different simulation runs. Specially, we use a bottom-up searching scheme to help to extract vortex with a user-defined shape, and further compute the geometry information including the size, and the geo-spatial location of the extracted vortex. Finally we design some linked views to compare the feature between different runs.
A Classification of User Tasks in Visual Analysis of Volume Data
Authors: Bireswar Laha, Doug A. Bowman, David H. Laidlaw, John J. Socha
Published at SciVis, 2015

Empirical findings from studies in one scientific domain have very limited applicability to other domains, unless we formally establish deeper insights on the generalizability of task types. We present a domain-independent classification of visual analysis tasks with volume visualizations. This taxonomy will help researchers design experiments, ensure coverage, and generate hypotheses in empirical studies with volume datasets. To develop our taxonomy, we first interviewed scientists working with spatial data in disparate domains. We then ran a survey to evaluate the design participants in which were scientists and professionals from around the world, working with volume data in various scientific domains. Respondents agreed substantially with our taxonomy design, but also suggested important refinements. We report the results in the form of a goal-based generic categorization of visual analysis tasks with volume visualizations. Our taxonomy covers tasks performed with a wide variety of volume datasets.
A proposed multivariate visualization taxonomy from user data
Authors: Mark A. Livingston, Jonathan W. Decker, Zhuming Ai
Published at SciVis, 2015

We revisited past user study data on multivariate visualizations, looking at whether image processing measures offer any insight into user performance. While we find statistically significant correlations, some of the greatest insights into user performance came from variables that have strong ties to two key properties of multivariate representations. We discuss our analysis and propose a taxonomy of multivariate visualizations that arises.
A Visual Voting Framework for Weather Forecast Calibration
Authors: Hongsen Liao, Yingcai Wu, Li Chen, Thomas M. Hamill, Yunhai Wang, Kan Dai, Hui Zhang 0051, Wei Chen 0001
Published at SciVis, 2015

Numerical weather predictions have been widely used for weather forecasting. Many large meteorological centers are producing highly accurate ensemble forecasts routinely to provide effective weather forecast services. However, biases frequently exist in forecast products because of various reasons, such as the imperfection of the weather forecast models. Failure to identify and neutralize the biases would result in unreliable forecast products that might mislead analysts; consequently, unreliable weather predictions are produced. The analog method has been commonly used to overcome the biases. Nevertheless, this method has some serious limitations including the difficulties in finding effective similar past forecasts, the large search space for proper parameters and the lack of support for interactive, real-time analysis. In this study, we develop a visual analytics system based on a novel voting framework to circumvent the problems. The framework adopts the idea of majority voting to combine judiciously the different variants of analog methods towards effective retrieval of the proper analogs for calibration. The system seamlessly integrates the analog methods into an interactive visualization pipeline with a set of coordinated views that characterizes the different methods. Instant visual hints are provided in the views to guide users in finding and refining analogs. We have worked closely with the domain experts in the meteorological research to develop the system. The effectiveness of the system is demonstrated using two case studies. An informal evaluation with the experts proves the usability and usefulness of the system.
Accurate Interactive Visualization of Large Deformations and Variability in Biomedical Image Ensembles
Authors: Max Hermann, Anja C. Schunke, Thomas Schultz 0001, Reinhard Klein
Published at SciVis, 2015

Large image deformations pose a challenging problem for the visualization and statistical analysis of 3D image ensembles which have a multitude of applications in biology and medicine. Simple linear interpolation in the tangent space of the ensemble introduces artifactual anatomical structures that hamper the application of targeted visual shape analysis techniques. In this work we make use of the theory of stationary velocity fields to facilitate interactive non-linear image interpolation and plausible extrapolation for high quality rendering of large deformations and devise an efficient image warping method on the GPU. This does not only improve quality of existing visualization techniques, but opens up a field of novel interactive methods for shape ensemble analysis. Taking advantage of the efficient non-linear 3D image warping, we showcase four visualizations: 1) browsing on-the-fly computed group mean shapes to learn about shape differences between specific classes, 2) interactive reformation to investigate complex morphologies in a single view, 3) likelihood volumes to gain a concise overview of variability and 4) streamline visualization to show variation in detail, specifically uncovering its component tangential to a reference surface. Evaluation on a real world dataset shows that the presented method outperforms the state-of-the-art in terms of visual quality while retaining interactive frame rates. A case study with a domain expert was performed in which the novel analysis and visualization methods are applied on standard model structures, namely skull and mandible of different rodents, to investigate and compare influence of phylogeny, diet and geography on shape. The visualizations enable for instance to distinguish (population-)normal and pathological morphology, assist in uncovering correlation to extrinsic factors and potentially support assessment of model quality.
Adaptive Multilinear Tensor Product Wavelets
Authors: Kenneth Weiss 0001, Peter Lindstrom 0001
Published at SciVis, 2015

Many foundational visualization techniques including isosurfacing, direct volume rendering and texture mapping rely on piecewise multilinear interpolation over the cells of a mesh. However, there has not been much focus within the visualization community on techniques that efficiently generate and encode globally continuous functions defined by the union of multilinear cells. Wavelets provide a rich context for analyzing and processing complicated datasets. In this paper, we exploit adaptive regular refinement as a means of representing and evaluating functions described by a subset of their nonzero wavelet coefficients. We analyze the dependencies involved in the wavelet transform and describe how to generate and represent the coarsest adaptive mesh with nodal function values such that the inverse wavelet transform is exactly reproduced via simple interpolation (subdivision) over the mesh elements. This allows for an adaptive, sparse representation of the function with on-demand evaluation at any point in the domain. We focus on the popular wavelets formed by tensor products of linear B-splines, resulting in an adaptive, nonconforming but crack-free quadtree (2D) or octree (3D) mesh that allows reproducing globally continuous functions via multilinear interpolation over its cells.
An evaluation of three methods for visualizing uncertainty in architecture and archaeology
Authors: Scott Houde, Sheila Bonde, David H. Laidlaw
Published at SciVis, 2015

This project explores the representation of uncertainty in visualizations for archaeological research and provides insights obtained from user feedback. Our 3D models brought together information from standing architecture and excavated remains, surveyed plans, ground penetrating radar (GPR) data from the Carthusian monastery of Bourgfontaine in northern France. We also included information from comparative Carthusian sites and a bird's eye representation of the site in an early modern painting. Each source was assigned a certainty value which was then mapped to a color or texture for the model. Certainty values between one and zero were assigned by one subject matter expert and should be considered qualitative. Students and faculty from the fields of architectural history and archaeology at two institutions interacted with the models and answered a short survey with four questions about each. We discovered equal preference for color and transparency and a strong dislike for the texture model. Discoveries during model building also led to changes of the excavation plans for summer 2015.
AnimoAminoMiner: Exploration of Protein Tunnels and their Properties in Molecular Dynamics
Authors: Jan Byska, Mathieu Le Muzic, M. Eduard Gröller, Ivan Viola, Barbora Kozlíková
Published at SciVis, 2015

In this paper we propose a novel method for the interactive exploration of protein tunnels. The basic principle of our approach is that we entirely abstract from the 3D/4D space the simulated phenomenon is embedded in. A complex 3D structure and its curvature information is represented only by a straightened tunnel centerline and its width profile. This representation focuses on a key aspect of the studied geometry and frees up graphical estate to key chemical and physical properties represented by surrounding amino acids. The method shows the detailed tunnel profile and its temporal aggregation. The profile is interactively linked with a visual overview of all amino acids which are lining the tunnel over time. In this overview, each amino acid is represented by a set of colored lines depicting the spatial and temporal impact of the amino acid on the corresponding tunnel. This representation clearly shows the importance of amino acids with respect to selected criteria. It helps the biochemists to select the candidate amino acids for mutation which changes the protein function in a desired way. The AnimoAminoMiner was designed in close cooperation with domain experts. Its usefulness is documented by their feedback and a case study, which are included.