An Analysis Framework Addressing the Scale and Legibility of Large Scientific Data Sets
Abstract
Much of the previous work in the large data visualization area has solely focused on handling the scale of the data. This task is clearly a great challenge and necessary, but it is not sufficient. Applying standard visualization techniques to large scale data sets often creates complicated pictures where meaningful trends are lost. A second challenge, then, is to also provide algorithms that simplify what an analyst must understand, using either visual or quantitative means. This challenge can be summarized as improving the legibility or reducing the complexity of massive data sets. Fully meeting both of these challenges is the work of many, many PhD dissertations. In this dissertation, we describe some new techniques to address both the scale and legibility challenges, in hope of contributing to the larger solution. In addition to our assumption of simultaneously addressing both scale and legibility, we add an additional requirement that the solutions considered fit well within an interoperable framework for diverse algorithms, because a large suite of algorithms is often necessary to fully understand complex data sets. For scale, we present a general architecture for handling large data, as well as details of a contractbased system for integrating advanced optimizations into amore »
 Authors:
 Univ. of California, Davis, CA (United States)
 Publication Date:
 Research Org.:
 Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
 Sponsoring Org.:
 USDOE
 OSTI Identifier:
 900438
 Report Number(s):
 UCRLTH226455
TRN: US200711%%131
 DOE Contract Number:
 W7405ENG48
 Resource Type:
 Thesis/Dissertation
 Country of Publication:
 United States
 Language:
 English
 Subject:
 97 MATHEMATICS AND COMPUTING; ALGORITHMS; ARCHITECTURE; DESIGN; SHAPE
Citation Formats
Childs, Hank R. An Analysis Framework Addressing the Scale and Legibility of Large Scientific Data Sets. United States: N. p., 2006.
Web. doi:10.2172/900438.
Childs, Hank R. An Analysis Framework Addressing the Scale and Legibility of Large Scientific Data Sets. United States. doi:10.2172/900438.
Childs, Hank R. Sun .
"An Analysis Framework Addressing the Scale and Legibility of Large Scientific Data Sets". United States.
doi:10.2172/900438. https://www.osti.gov/servlets/purl/900438.
@article{osti_900438,
title = {An Analysis Framework Addressing the Scale and Legibility of Large Scientific Data Sets},
author = {Childs, Hank R.},
abstractNote = {Much of the previous work in the large data visualization area has solely focused on handling the scale of the data. This task is clearly a great challenge and necessary, but it is not sufficient. Applying standard visualization techniques to large scale data sets often creates complicated pictures where meaningful trends are lost. A second challenge, then, is to also provide algorithms that simplify what an analyst must understand, using either visual or quantitative means. This challenge can be summarized as improving the legibility or reducing the complexity of massive data sets. Fully meeting both of these challenges is the work of many, many PhD dissertations. In this dissertation, we describe some new techniques to address both the scale and legibility challenges, in hope of contributing to the larger solution. In addition to our assumption of simultaneously addressing both scale and legibility, we add an additional requirement that the solutions considered fit well within an interoperable framework for diverse algorithms, because a large suite of algorithms is often necessary to fully understand complex data sets. For scale, we present a general architecture for handling large data, as well as details of a contractbased system for integrating advanced optimizations into a data flow network design. We also describe techniques for volume rendering and performing comparisons at the extreme scale. For legibility, we present several techniques. Most noteworthy are equivalence class functions, a technique to drive visualizations using statistical methods, and linescan based techniques for characterizing shape.},
doi = {10.2172/900438},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Sun Jan 01 00:00:00 EST 2006},
month = {Sun Jan 01 00:00:00 EST 2006}
}

This thesis centers on the use of spectral modeling techniques on data from the Sloan Digital Sky Survey (SDSS) to gain new insights into current questions in galaxy evolution. The SDSS provides a large, uniform, high quality data set which can be exploited in a number of ways. One avenue pursued here is to use the large sample size to measure precisely the mean properties of galaxies of increasingly narrow parameter ranges. The other route taken is to look for rare objects which open up for exploration new areas in galaxy parameter space. The crux of this thesis is revisitingmore »

Designing for scientific data analysis: From practice to prototype
Designers charged with creating tools for processes foreign to their own experience need a reliable source of application knowledge. This dissertation presents an empirical study of the scientific data analysis process in order to inform the design of tools for this important aspect of scientific computing. Interaction analysis and contextual inquiry methods were adapted to observe scientists analyzing their own data and to characterize the scientific dam analysis process. The characterization exposed elements of the process outside the conventional scientific visualization model that defines data analysis in terms of image generation. Scientists queried for quantitative information, made a variety ofmore » 
Timevarying Reeb Graphs: A Topological Framework Supporting the Analysis of Continuous Timevarying Data
I present timevarying Reeb graphs as a topological framework to support the analysis of continuous timevarying data. Such data is captured in many studies, including computational fluid dynamics, oceanography, medical imaging, and climate modeling, by measuring physical processes over time, or by modeling and simulating them on a computer. Analysis tools are applied to these data sets by scientists and engineers who seek to understand the underlying physical processes. A popular tool for analyzing scientific datasets is level sets, which are the points in space with a fixed data value s. Displaying level sets allows the user to study theirmore » 
Stability analysis of a nuclear power plant by largescale system Lyapunov methods
An attempt to apply modern largescale system stability theory to a complex nuclear power plant is presented. The effectiveness of both the vector and the scalar Lyapunov methods for stability analysis were examined in this application. A dynamic model was derived comprising the components of the nuclear power plant, namely, the reactor, the pressurizer, the steam generator, the turbines, the feedwater heater and the electric generator. This particular decomposition into subsystems involves a compromise between a reasonable physical description and mathematical tractability. A Lyapunov function was developed for each isolated subsystem and an exponential stability condition was obtained by settingmore » 
VLSI structures and iterative analysis for largescale computation
Problems of computation and development of VLSI structures are considered in relation to each other. In particular, two issues are addressed: (a) development of components and algorithms for standard operations, suitable for VLSI implementation; (b) largescale computation, in this case the iterative solution of large leastsquare problems in a limitedsize VLSI architecture. On standard operations, improved and new adders are presented that can be implemented in VLSI. The adders so designed are shown to be superior when compared to other existing ones. Moreover, an iterative multiplier that uses carry save adders is also presented. On largescale computation, analysis of iterativemore »