Breakthrough on Big Data? Maybe You'll Use It Soon

These are the young geniuses working in visual analytics at Virginia Tech.

I'm not sure this will have the impace of the internet going global in its use--filtering down to people like me from the government, academic and professional users who had it exclusively for a while--but Big Data is Big News these days.

This is a Virginia Tech press release outlining how a significant effort is geing made to bring Big Data to the great unwashed. I have no idea how this will play out, but not many of us imagined how much the internet would change the way we live our daily lives in the late 1970s, early 1980s, either. And you know it was huge.

Here's the release (remember: academics talk funny):

Big Data: Everyone wants to use it; but few can. A team of researchers at Virginia Tech is trying to change that.

In an effort to make Big Data analytics usable and accessible to nonspecialist, professional, and student users, the team is fusing human-computer interaction research with complex statistical methods to create something that is both scalable and interactive.

“Gaining big insight from big data requires big analytics, which poses big usability problems,” said Chris North, a professor of computer science and associate director of the Institute for Critical Technology and Applied Science’s Discovery Analytics Center (http://dac.cs.vt.edu/).

With a $1 million from the National Science Foundation, North and his team are working to make vast amounts of data usable by changing the way people see it.

Yong Cao, an assistant professor with the Department of Computer Science in the College of Engineering (https://www.eng.vt.edu/), along with Leanna House, an assistant professor, and Scotland Leman, an associate professor, both with the Department of Statistics of the College of Science (http://www.science.vt.edu), are working with North to bring large data clouds down to manageable working sets.

“The platform, known as Andromeda, relies on a spatial metaphor that places similar objects (text documents, multidimensional data vectors, etc.) in closer proximity,” Leman said. 

When people reorganize some objects in the space, the system is able to learn which data features express relevant patterns of similarity.

For instance, in order to remember your wallet, you might set it down next to your keys. Andromeda would be able to recognize this pattern and put your phone next to these items on the table so you won’t forget it, either.

“What makes this system unique is that users do not need to have a preformed hypothesis in order to interact with the data. In this model, the tasks of organization and discovery can occur simultaneously,” North said.

The user gains insights by observing the updated structure of the visualization, as well as learning which features are most responsible for their injected feedback.