Research may relieve 'info overload' in online data

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Georgia Tech is exploring visual data mining
Georgia Tech is exploring visual data mining

One challenge facing online retailers is analyzing the mass of avail­able consumer data, to better market their product offerings.

Now, research being done by the Georgia Institute of Technology, with a $3 million joint grant from the National Science Foundation and Department of Homeland Security, aims to make the science of analytics and mining large data­bases more effective for marketers.

The five-year grant will allow the Georgia Tech research team, led by Prof. Haesun Park, to investigate how to improve the visual analytics of mas­sive data sets through the use of machine learning, numerical algorithms and opti­mization, and computational statistics.

Dr. Richard Fujimoto, chair of the Computational Science and Engineer­ing Division of Georgia Tech's College of Computing, said this research could greatly benefit online marketers.

“From an e-commerce perspective, a marketer has specific questions he [needs] to have answered; he wants to get inside the mind of the consumer and discover what are they thinking and what they are looking to buy,” Fujimoto said.

Data are collected and processed, then relevant information must be presented using visual and interactive means.

“We are trying to establish this as a bona fide scientific discipline,” said Park. “Taking the computational aspect of data analysis and combining it with the visual­ization part, so it can be presented.”

Eric Peterson, founder of Web Analytics Demystified, feels this research could help shed light on an unknown discipline.

“Retailers are adept at using data in an offline channel, but online is still very much the Wild West,” said Peterson.

Online retailers face significant chal­lenges with so much data generated in that channel, he said, and “struggle with which measures to look at, and how to use that to drive business.”

Scott Silverman, executive director of, said visual analytics “will help to determine which data is most helpful at improving business.”


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