Chad A. Steed

Big data and deep data in scanning and electron microscopies: deriving functionality from multidimensional data sets

The development of electron and scanning probe microscopies in the second half of the twentieth century has produced spectacular images of the internal structure and composition of matter with nanometer, molecular, and atomic resolution. Largely, this progress was enabled by computer-assisted methods of microscope operation, data acquisition, and analysis. Advances in imaging technology in the beginning of the twenty-first century have opened the proverbial floodgates on the availability of high-veracity information on structure and functionality. From the hardware perspective, high-resolution imaging methods now routinely resolve atomic positions with approximately picometer precision, allowing for quantitative measurements of individual bond lengths and angles. Similarly, functional imaging often leads to multidimensional data sets containing partial or full information on properties of interest, acquired as a function of multiple parameters (time, temperature, or other external stimuli). Here, we review several recent applications of the big and deep data analysis methods to visualize, compress, and translate this multidimensional structural and functional data into physically and chemically relevant information.

Guided analysis of hurricane trends using statistical processes integrated with interactive parallel coordinates

This paper demonstrates the promise of augmenting interactive multivariate representations with information from statistical processes in the domain of weather data analysis. Statistical regression, correlation analysis, and descriptive statistical calculations are integrated via graphical indicators into an enhanced parallel coordinates system, called the Multidimensional Data eXplorer (MDX). These statistical indicators, which highlight significant associations in the data, are complemented with interactive visual analysis capabilities. The resulting system allows a smooth, interactive, and highly visual workflow. The systems utility is demonstrated with an extensive hurricane climate study that was conducted by a hurricane expert. In the study, the expert used a new data set of environmental weather data, composed of 28 independent variables, to predict annual hurricane activity. MDX shows the Atlantic Meridional Mode increases the explained variance of hurricane seasonal activity by 7–15% and removes less significant variables used in earlier studies. The findings and feedback from the expert (1) validate the utility of the data set for hurricane prediction, and (2) indicate that the integration of statistical processes with interactive parallel coordinates, as implemented in MDX, addresses both deficiencies in traditional weather data analysis and exhibits some of the expected benefits of visual data analysis.

Ultrascale Visualization of Climate Data

Collaboration across research, government, academic, and private sectors is integrating more than 70 scientific computing libraries and applications through a tailorable provenance framework, empowering scientists to exchange and examine data in novel ways.

AQS-20 Through-the-Sensor (TTS) performance assessment

Performance of existing and planned mine hunting sensors is dependent on the environment. When the sea floor is a flat smooth hard sandy surface with no mine like clutter on it, then sensor performance is outstanding and acoustic mine hunting is relatively easy. Introduce clutter, a rough seafloor and a soft muddy bottom, sensor performance is seriously degraded making mine hunting operations extremely difficult to impossible. One must know the environment to know sensor performance. Historical environmental data is important but not sufficient. In spite of painstaking efforts to collect, process and disseminate data, historical information is often missing, outdated or in error. To know sensor performance, near realtime environmental data must be collected to verify, supplement and refresh historical holdings. This paper describes the results of two near real-time end-to-end Through-the-Sensor (TTS) demonstrations conducted in FY05 using AQS-20 data. Critical environmental parameters were extracted from the raw tactical data stream using a TTS approach. Data collected by the AQS-20 was processed for bathymetry, sediment type and % burial. Supplemental data was fused with historical information on scene and used to calculate doctrinal bottom type in NAVOCEANOs Bottom Mapping Workstation. The information was passed to MEDAL where track spacing and hunt times were calculated. NAVOCEANO, in a fast reach back mode using TEDServices, examined the data, added value, and returned it. The impact to the mine warfare community is a true sense of sensor performance.

An interactive parallel coordinates technique applied to a tropical cyclone climate analysis

A highly interactive visual analysis system is presented that is based on an enhanced variant of parallel coordinates - a multivariate information visualization technique. The system combines many variations of previously described visual interaction techniques such as dynamic axis scaling, conjunctive visual queries, statistical indicators, and aerial perspective shading. The system capabilities are demonstrated on a hurricane climate data set. This climate study corroborates the notion that enhanced visual analysis with parallel coordinates provides a deeper understanding when used in conjunction with traditional multiple regression analysis.

Short communication: A functional test platform for the Community Land Model

The realistic representation of key biogeophysical and biogeochemical functions is the fundamental of process-based ecosystem models. A functional test platform is designed to create direct linkages between site measurements and the process-based ecosystem model within the Community Earth System Models (CESM). The platform consists of three major parts: 1) interactive user interfaces, 2) functional test models and 3) observational datasets. It provides much needed integration interfaces for both field experimentalists and ecosystem modelers to improve the models representation of ecosystem processes within the CESM framework without large software overhead. Design an ecosystem functional testing platform for process-based model-data comparison.Improve global earth system model via site-based mechanistic modeling.Bridge the gap between legacy software modeling system and in-field experimentalists.

Practical Application of Parallel Coordinates for Climate Model Analysis

The determination of relationships between climate variables and the identification of the most significant associations between them in various geographic regions is an important aspect of climate model evaluation. The EDEN visual analytics toolkit has been developed to aid such analysis by facilitating the assessment of multiple variables with respect to the amount of variability that can be attributed to specific other variables. EDEN harnesses the parallel coordinates visualization technique and is augmented with graphical indicators of key descriptive statistics. A case study is presented in which the focus is on the Harvard Forest site (42.5378N Lat, 72.1715W Lon) and the Community Land Model Version 4 (CLM4) is evaluated. It is shown that model variables such as land water runoff are more sensitive to a particular set of environmental variables than a suite of other inputs in the 88 variable analysis conducted. The approach presented here allows climate scientists to focus on the most important variables in the model evaluations.

Tropical Cyclone Trend Analysis Using Enhanced Parallel Coordinates and Statistical Analytics

This work presents, via an in-depth case study on how parallel coordinates coupled with statistical analysis can be used for more effective knowledge discovery and confirmation in complex, environmental data sets. Advanced visual interaction techniques such as dynamic axis scaling, conjunctive parallel coordinates, statistical indicators, and aerial perspective shading are combined into an interactive geovisual analytics system. Moreover, the system facilitates statistical processes such as stepwise regression and correlation analysis to assist in the identification and quantification of the most significant predictors for a particular dependent variable. Using a systematic workflow, this approach is demonstrated via a North Atlantic hurricane climate study in close collaboration with a domain expert. By revealing several important physical associations, the case study confirms that the visual analytics approach facilitates a deeper understanding of multidimensional climate data sets when compared to traditional techniques.

AQS-20 sonar processing enhancement for bathymetry estimation

Bathymetry is used to determine optimal tactics during Mine Warfare operations. Previous work demonstrated that bathymetric data could be acquired from the Volume Search Sonar (VSS) mounted on the AQS-20 system. The VSS transmitter produces a pulse at approximately one-second intervals along the track. The returning pulse from the sea-bottom is received by a group of sensors and beamformed in hardware into two fans (one pitched slightly forward and a second pitched slightly aft). A possible way to increase the accuracy of the bathymetry data is to improve the angle of arrival estimates by processing the adjacent across-track and/or along-track beam pairs. This paper employs narrow-beams monopulse techniques in order to investigate improvements to the bathymetric data over conventional processing. A comparative analysis of the experimental results for both the new and the classical technique is presented.

ParCAT: Parallel Climate Analysis Toolkit

Abstract Climate science is employing increasingly complex models and simulations to analyze the past and predict the future of Earths climate. This growth in complexity is creating a widening gap between the data being produced and the ability to analyze the datasets. Parallel computing tools are necessary to analyze, compare, and interpret the simulation data. The Parallel Climate Analysis Toolkit (ParCAT) provides basic tools to efficiently use parallel computing techniques to make analysis of these datasets manageable. The toolkit provides the ability to compute spatio-temporal means, differences between runs or differences between averages of runs, and histograms of the values in a data set. ParCAT is implemented as a command-line utility written in C. This allows for easy integration in other tools and allows for use in scripts. This also makes it possible to run ParCAT on many platforms – from laptops to supercomputers. ParCAT outputs NetCDF files so it is compatible with existing utilities such as Panoply and UV-CDAT. This paper describes ParCAT and presents results from some example runs on the Titan system at ORNL.