Publications

Results 1–25 of 66
Skip to search filters

Data intensive computing at Sandia

Wilson, Andrew T.

Data-Intensive Computing is parallel computing where you design your algorithms and your software around efficient access and traversal of a data set; where hardware requirements are dictated by data size as much as by desired run times usually distilling compact results from massive data.

More Details
TYPE Conference YEAR 2010
OSTI

Data Visualization Saliency Model: A Tool for Evaluating Abstract Data Visualizations

IEEE Transactions on Visualization and Computer Graphics

Matzen, Laura E.; Haass, Michael J.; Divis, Kristin; Wang, Zhiyuan; Wilson, Andrew T.

Evaluating the effectiveness of data visualizations is a challenging undertaking and often relies on one-off studies that test a visualization in the context of one specific task. Researchers across the fields of data science, visualization, and human-computer interaction are calling for foundational tools and principles that could be applied to assessing the effectiveness of data visualizations in a more rapid and generalizable manner. One possibility for such a tool is a model of visual saliency for data visualizations. Visual saliency models are typically based on the properties of the human visual cortex and predict which areas of a scene have visual features (e.g. color, luminance, edges) that are likely to draw a viewer's attention. While these models can accurately predict where viewers will look in a natural scene, they typically do not perform well for abstract data visualizations. In this paper, we discuss the reasons for the poor performance of existing saliency models when applied to data visualizations. We introduce the Data Visualization Saliency (DVS) model, a saliency model tailored to address some of these weaknesses, and we test the performance of the DVS model and existing saliency models by comparing the saliency maps produced by the models to eye tracking data obtained from human viewers. Finally, we describe how modified saliency models could be used as general tools for assessing the effectiveness of visualizations, including the strengths and weaknesses of this approach.

More Details
TYPE Journal Article YEAR 2018
ScopusOSTIDOI

Evaluating parallel relational databases for medical data analysis

Wilson, Andrew T.; Rintoul, Mark D.

Hospitals have always generated and consumed large amounts of data concerning patients, treatment and outcomes. As computers and networks have permeated the hospital environment it has become feasible to collect and organize all of this data. This raises naturally the question of how to deal with the resulting mountain of information. In this report we detail a proof-of-concept test using two commercially available parallel database systems to analyze a set of real, de-identified medical records. We examine database scalability as data sizes increase as well as responsiveness under load from multiple users.

More Details
TYPE SAND Report YEAR 2012
OSTIDOI

Exploring 2D tensor fields using stress nets

Wilson, Andrew T.; Brannon, Rebecca M.

In this article we describe stress nets, a technique for exploring 2D tensor fields. Our method allows a user to examine simultaneously the tensors eigenvectors (both major and minor) as well as scalar-valued tensor invariants. By avoiding noise-advection techniques, we are able to display both principal directions of the tensor field as well as the derived scalars without cluttering the display. We present a CPU-only implementation of stress nets as well as a hybrid CPU/GPU approach and discuss the relative strengths and weaknesses of each. Stress nets have been used as part of an investigation into crack propagation. They were used to display the directions of maximum shear in a slab of material under tension as well as the magnitude of the shear forces acting on each point. Our methods allowed users to find new features in the data that were not visible on standard plots of tensor invariants. These features disagree with commonly accepted analytical crack propagation solutions and have sparked renewed investigation. Though developed for a materials mechanics problem, our method applies equally well to any 2D tensor field having unique characteristic directions.

More Details
TYPE Conference YEAR 2005
OSTI

Exploring Explicit Uncertainty for Binary Analysis (EUBA)

Leger, Michelle A.; Darling, Michael C.; Jones, Stephen T.; Matzen, Laura E.; Stracuzzi, David J.; Wilson, Andrew T.; Bueno, Denis B.; Christentsen, Matthew C.; Ginaldi, Melissa J.; Hannasch, David A.; Heidbrink, Scott H.; Howell, Breannan C.; Leger, Chris; Reedy, Geoffrey E.; Rogers, Alisa N.; Williams, Jack A.

Reverse engineering (RE) analysts struggle to address critical questions about the safety of binary code accurately and promptly, and their supporting program analysis tools are simply wrong sometimes. The analysis tools have to approximate in order to provide any information at all, but this means that they introduce uncertainty into their results. And those uncertainties chain from analysis to analysis. We hypothesize that exposing sources, impacts, and control of uncertainty to human binary analysts will allow the analysts to approach their hardest problems with high-powered analytic techniques that they know when to trust. Combining expertise in binary analysis algorithms, human cognition, uncertainty quantification, verification and validation, and visualization, we pursue research that should benefit binary software analysis efforts across the board. We find a strong analogy between RE and exploratory data analysis (EDA); we begin to characterize sources and types of uncertainty found in practice in RE (both in the process and in supporting analyses); we explore a domain-specific focus on uncertainty in pointer analysis, showing that more precise models do help analysts answer small information flow questions faster and more accurately; and we test a general population with domain-general sudoku problems, showing that adding "knobs" to an analysis does not significantly slow down performance. This document describes our explorations in uncertainty in binary analysis.

More Details
TYPE SAND Report YEAR 2021
OSTIDOI

Facilitation of Forensic Analysis Using a Narrative Template

Procedia Manufacturing

Hopkins, Shelby; Wilson, Andrew T.; Silva, Austin R.; Forsythe, James C.

Criminal forensic analysis involves examining a collection of clues to construct a plausible account of the events associated with a crime. In this paper, a study is presented that assessed whether software tools designed to encourage construction of narrative accounts would facilitate cyber forensic analysis. Compared to a baseline condition (i.e., spreadsheet with note-taking capabilities) and a visualization condition, subjects performed best when provided tools that emphasized established components of narratives. Specifically, features that encouraged subjects to identify suspected entities, and their activities and motivations proved beneficial. It is proposed that software tools developed to facilitate cyber forensic analysis and training of cyber security professionals incorporate techniques that facilitate a narrative account of events.

More Details
TYPE Conference Poster YEAR 2015
ScopusOSTI
Results 1–25 of 66
Results 1–25 of 66