Advances in computational power now enable domain scientists to address conceptual and parametric uncertainty by running simulations multiple times in order to sufficiently sample the uncertain input space. While this approach helps address conceptual and parametric uncertainties, the ensemble datasets produced by this technique present a special challenge to visualization researchers as the ensemble dataset records a distribution of possible values for each location in the domain. Contemporary visualization approaches that rely solely on summary statistics (e.g., mean and variance) cannot convey the detailed information encoded in ensemble distributions that are paramount to ensemble analysis; summary statistics provide no information about modality classification and modality persistence. To address this problem, we propose a novel technique that classifies high-variance locations based on the modality of the distribution of ensemble predictions. Additionally, we develop a set of confidence metrics to inform the end-user of the quality of fit between the distribution at a given location and its assigned class. Finally, for the special application of evaluating the stability of bimodal regions, we develop local and regional metrics.

Abstract Observing interactions among chemical species and microorganisms in the earth's sub‐surface is a common task in the field of geology. Bioremediation experiments constitute one such class of interactions which focus on getting rid of pollutants through processes such as carbon sequestration. The main goal of scientists’ observations is to analyze the dynamics of the chemical reactions and understand how they collectively affect the carbon content of the soil. In our work, we extract the high‐level goals of geologists and propose a visual analytics solution which helps scientists in deriving insights about multivariate, temporal behavior of these chemical species. Specifically, our key contributions are the following: i) characterization of the domain‐specific goals and their translation to exploratory data analysis tasks, ii) developing an analytical abstraction in the form of perceptually motivated screen‐space metrics for bridging the gap between the tasks and the visualization, and iii) realization of the tasks and metrics in the form of VIMTEX, which is a set of coordinated multiple views for letting scientists observe multivariate, temporal relationships in the data. We provide several examples and case studies along with expert feedback for demonstrating the efficacy of our solution.

The Multi-Source Signatures for Nuclear Programs project, part of Pacific Northwest National Laboratory's (PNNL's) Signature Discovery Initiative, seeks to computationally capture expert assessment of multi-type information to assess nuclear activities through a series of Bayesian network (BN) models. Information types may include text, sensor output, imagery, or audio/video files. The BN models incorporate knowledge from a diverse range of information sources to help assess a country's nuclear activities. The models span engineering topic areas, country-level indicators, and facility-specific characteristics. To illustrate the development, calibration, and use of BN models for multi-source assessment, we present a model that predicts a country's likelihood to participate in the international nuclear nonproliferation regime. We validate this model by examining the extent to which the model helps non-experts arrive at conclusions similar to those provided by nuclear proliferation experts. We also describe the PNNL-developed software used throughout the lifecycle of the Bayesian network model development.

We present a mathematical framework for assessing the quality of signature systems in terms of fidelity, risk, cost, other attributes, and utility-a method we call Signature Quality Metrics (SQM). We demonstrate the SQM approach by assessing the quality of a signature system designed to predict the culture medium used to grow a microorganism. The system consists of four chemical assays and a Bayesian network that estimates the probabilities the microorganism was grown using one of eleven culture media. We evaluated fifteen combinations of the signature system by removing one or more of the assays from the Bayes net. We show how SQM can be used to compare the various combinations while accounting for the tradeoffs among three attributes of interest: fidelity, cost, and the amount of sample material consumed by the assays.

Numerical ensemble forecasting is a powerful tool that drives many risk analysis efforts and decision making tasks. These ensembles are composed of individual simulations that each uniquely model a possible outcome for a common event of interest: e.g., the direction and force of a hurricane, or the path of travel and mortality rate of a pandemic. This paper presents a new visual strategy to help quantify and characterize a numerical ensemble's predictive uncertainty: i.e., the ability for ensemble constituents to accurately and consistently predict an event of interest based on ground truth observations. Our strategy employs a Bayesian framework to first construct a statistical aggregate from the ensemble. We extend the information obtained from the aggregate with a visualization strategy that characterizes predictive uncertainty at two levels: at a global level, which assesses the ensemble as a whole, as well as a local level, which examines each of the ensemble's constituents. Through this approach, modelers are able to better assess the predictive strengths and weaknesses of the ensemble as a whole, as well as individual models. We apply our method to two datasets to demonstrate its broad applicability.

Driven by the ability to generate ever-larger, increasingly complex data, there is an urgent need in the scientific community for scalable analysis methods that can rapidly identify salient trends in scientific data. Query-Driven Visualization (QDV) strategies are among the small subset of techniques that can address both large and highly complex data sets. This paper extends the utility of QDV strategies with a statistics-based framework that integrates nonparametric distribution estimation techniques with a new segmentation strategy to visually identify statistically significant trends and features within the solution space of a query. In this framework, query distribution estimates help users to interactively explore their query's solution and visually identify the regions where the combined behavior of constrained variables is most important, statistically, to their inquiry. Our new segmentation strategy extends the distribution estimation analysis by visually conveying the individual importance of each variable to these regions of high statistical significance. We demonstrate the analysis benefits these two strategies provide and show how they maybe used to facilitate the refinement of constraints over variables expressed in a user's query. We apply our method to data sets from two different scientific domains to demonstrate its broad applicability.

Abstract We present a dimension reduction and feature extraction method for the visualization and analysis of function field data. Function fields are a class of high‐dimensional, multi‐variate data in which data samples are one‐dimensional scalar functions. Our approach focuses upon the creation of high‐dimensional range‐space segmentations, from which we can generate meaningful visualizations and extract separating surfaces between features. We demonstrate our approach on high‐dimensional spectral imagery, and particulate pollution data from air quality simulations.

The visualization and analysis of AMR-based simulations is integral to the process of obtaining new insight in scientific research. We present a new method for performing query-driven visualization and analysis on AMR data, with specific emphasis on time-varying AMR data. Our work introduces a new method that directly addresses the dynamic spatial and temporal properties of AMR grids that challenge many existing visualization techniques. Further, we present the first implementation of query-driven visualization on the GPU that uses a GPU-based indexing structure to both answer queries and efficiently utilize GPU memory. We apply our method to two different science domains to demonstrate its broad applicability.

We present interactive techniques for identifying and extracting features in function fields. Function fields map points in n-dimensional Euclidean space to 1-dimensional scalar functions. Visual feature identification is ac- complished by interactively rendering scalar distance fields, constructed by applying a function-space distance metric over the function field. Combining visual exploration with feature extraction queries, formulated as a set of function-space constraints, facilitates quantitative analysis and annotation. Numerous application domains give rise to function fields. We present results for two-dimensional hyperspectral images, and a simulated time-varying, three-dimensional air quality dataset.

Our ability to generate ever-larger, increasingly-complex data, has established the need for scalable methods that identify, and provide insight into, important variable trends and interactions. Query-driven methods are among the small subset of techniques that are able to address both large and highly complex datasets. This paper presents a new method that increases the utility of query-driven techniques by visually conveying statistical information about the trends that exist between variables in a query. In this method, correlation fields, created between pairs of variables, are used with the cumulative distribution functions of variables expressed in a user's query. This integrated use of cumulative distribution functions and correlation fields visually reveals, with respect to the solution space of the query, statistically important interactions between any three variables, and allows for trends between these variables to be readily identified. We demonstrate our method by analyzing interactions between variables in two flame-front simulations.

Large scale scientific data is often stored in scientific data formats such as FITS, netCDF and HDF. These storage formats are of particular interest to the scientific user community since they provide multi-dimensional storage and retrieval. However, one of the drawbacks of these storage formats is that they do not support semantic indexing which is important for interactive data analysis where scientists look for features of interests such as "Find all supernova explosions where energy > 10^5 and temperature > 10^6". In this paper we present a novel approach called HDF5- FastQuery to accelerate the data access of large HDF5 files by introducing multi-dimensional semantic indexing. Our implementation leverages an efficient indexing technology called bitmap indexing that has been widely used in the database community. Bitmap indices are especially well suited for interactive exploration of large-scale readonly data. Storing the bitmap indices into the HDF5 file has the following advantages: a) Significant performance speedup of accessing subsets of multi-dimensional data and b) portability of the indices across multiple computer platforms. We will present an API that simplifies the execution of queries on HDF5 files for general scientific applications and data analysis. The design is flexible enough to accommodate the use of arbitrary indexing technology for semantic range queries. We will also provide a detailed performance analysis of HDF5-FastQuery for both synthetic and scientific data. The results demonstrate that our proposed approach for multi-dimensional queries is up to a factor of 2 faster than HDF5.