Serendipitous Data Connections

CNET has an article on a new Wharton Team that appears to be using k-log-like techniques to rediscover serendipitous data connections.

“Although an unprecedented amount of information about technology is now available online, Ranieri notes that “everything is set up to look for exactly what you are looking for,” rather than to assist in the process of finding crossover, innovative applications. In addition, information is “stored in silos” that are hard for non-specialists to penetrate. Until now, there has been no way to search for attributes like “lighter, faster or quicker” with technology categories, he says.

The Wharton team’s new process aims to meet this challenge by using a methodology that “combines computer research techniques with human research techniques,” MacMillan says. Kimbrough likens the new process to the methodology Google uses. Although Google’s search engine is automated, it exploits information that thousands of individuals (at no cost to Google) painstakingly collected and loaded onto their Web sites. Kimbrough explains that Google’s page-ranking algorithm “exploits tons of work (done by) people who put Java links on their Web sites; it exploits their manual labor.”

the Wharton team’s new process searches through documents and makes connections between highly technical descriptions of properties–often familiar only to narrow “silos” of technologies–and broader terms that could suggest market applications to those who work in other areas. As Ranieri describes it, “We found a clever way to make a link between attributes and markets.”

Although it’s too early for developers to discuss technical details, Kimbrough acknowledges that this new process requires a significant amount of human input. “In part, we use human beings to create databases of attributes that can be matched up.”