A research team at the Georgia Tech Research Institute (GTRI) has developed
a software tool that enables users to perform in-depth analysis of modeling and
simulation data, then visualize the results on screen. The new data analysis
and visualization tool offers improved ease of use compared to similar tools,
the researchers say, and could be readily adapted for use with existing data
sets in a variety of disciplines.
The data analysis and visualization tool is a subset of the Test Matrix Tool
(TMT), a multicomponent system developed by GTRI for designing, executing, and
analyzing large-scale modeling and simulation data sets. The visualization
capability offers a graphical user interface that provides both on-screen
data-manipulation features like filters and the ability to see query results in
the form of graphical images almost instantly.
“Data visualization supports data analysis by letting users pose
data-related questions onscreen with ease and then view the answers in ways
that go far beyond ordinary table formats,” said Edward Clarkson, a GTRI
research scientist who is leading the data visualization work. “A picture can
be worth a thousand numbers, because visualizing data in a graph allows us to
see patterns that might not be apparent from purely numerical results.”
Development of the Test Matrix Tool and its components is being led by Greg
Rohling, a GTRI principal research engineer. Rohling’s team developed the TMT
to support modeling and simulation investigations into the effectiveness and
optimization of numerous U.S. defense systems, including electronic warfare
equipment used to protect military aircraft. The work is supported by the
Warner Robins Air Logistics Center at Robins Air Force Base.
In developing a simulation test, Test Matrix Tool users can specify desired
variations in input parameters using multiple data filters. The TMT system
executes all possible combinations of those parameters, creating a test matrix.
It then executes the simulations on a Sun/Oracle Grid Engine and stores the
resulting simulation output data in a MySQL database.
At that point, TMT’s data analysis component, which includes the data
visualization tool, helps users evaluate the often complex test results. By
collating the test matrix input and output, the data analysis tools allow users
to efficiently filter and visualize test matrix data.
The Test Matrix Tool is designed for use on personal computers. It
works under the Linux and Microsoft Windows operating systems.
Numerous Potential Applications
Some TMT capabilities, including the data analysis and visualization
components, could be useful for scrutinizing information gathered in many
disciplines, Clarkson said. He mentioned health care as one field where a
multitude of existing data sets could be mined for new insights.
“For example, there’s an enormous amount of data out there on heart
patients,” he said. “Our data tools could be used to investigate existing
patient information and seek significant trends in the data.”
Clarkson explained that users would face the challenge of organizing legacy
data sets into formats that the GTRI data analysis software can exploit. But
that task, he added, is generally straightforward and can be performed with
automated tools in many cases.
The data format required by the TMT tools, he explained, is not particularly
complex. What’s needed is a standard database setup in which the information
fields are organized into tabular formats. Moreover, any required metadata—special
data that tell the system how to deal with a particular data set—would likely
present few development issues.
Clarkson recently demonstrated the capabilities of the data analysis and
visualization tool using an existing database: baseball statistics. This
particular demonstration involved the use of 40 different data filters
available onscreen; the TMT system allows for 300 or more such filters.
In a random query of the 46,000 National League players from the past, an
onscreen graph unexpectedly revealed an interesting anomaly during the
demonstration. The data indicate that players’ height and weight increased in
every past decade except the 1920s and 1930s, when it stayed inexplicably flat.
“That’s the beauty of this kind of tool—it can find the unknown unknowns,”
Clarkson observed. “Details show up in graphs that aren’t obvious when you’re
looking at just the numbers.”
The TMT data visualization tool, he explained, bears some similarities to
the data filtering features found on some websites. For example, many shopping
sites let users search for products by using filters to select desired
qualities such as size, color, and brand name.
However, Clarkson said, TMT’s capabilities are considerably more advanced.
Whereas commercial systems stop at the filtering stage, the TMT data analysis
tools allow fundamental manipulation of the data. Using filters, investigators
can transform the data mathematically, a process that makes unique insights and
“Data analysis and visualization are great for finding many things you want
to know,” Clarkson said. “But another real advantage is that they can detect
what you perhaps don’t want to know—the bugs and the anomalies—the things
that just aren’t right and have to be fixed.”
Source: Georgia Institute of Technology