Distributed Sensemaking for Everyday Big Data
This week researchers from Microsoft and Carnegie Mellon University combined forces to tap the power of what they call distributed sensemaking. In essence, what their developments could yield are new ways of thinking about how average users of the web face the data deluge and the complications caused by information overload.
This effort could make the growing volumes of data that ordinary internet users need to sift through more manageable via the sheer act of maximizing the searches of days gone by.
More specifically, the team says that by using digital knowledge maps (which represent the thought processes used to make sense of info collected through searchers), the work of searches and search strings is no longer wasted following an individual’s use of the web.
“Collectively, people spend more than 70 billion hours a year trying to make sense of information they have gathered online,” said Aniket Kittur, assistant professor in Carnegie Mellon’s Human-Computer Interaction Institute. “Yet in most cases, when someone finishes a project, that work is essentially lost, benefitting no one else and perhaps even being forgotten by that person. If we could somehow share those efforts, however, all of us might learn faster.”
Following the fruitful act of searching for the causes and symptoms of a particular disease, for instance, users of the internet are left with a great deal of information for personal use, but in essence, that hour spent
During the course of that search, however, a sort of map has been spawned. In this example, the knowledge map might have begun with a particular symptom, which then led to a series of possibilities, then further down to information about the specific condition.
According to the researchers, in most cases, the organization of the knowledge map, rather than any specific content, was most useful. For instance, two people looking to start a garden might live in different climates or settings, so the types of seeds they might plant could be different, but each would benefit from elements such as “design ideas,” “how to” and so on.
Using eye tracking, the researchers showed that as knowledge maps are modified successively by multiple users, new users spend less time looking at specific content elements, shifting a greater balance of their attention to structural elements like labels. This suggests that distributed sensemaking facilitates the process of ‘schema induction,’ or forming a mental model of the information being considered.
It might take quite a chunk of your day, but the talks presented over the next hour below along these lines will provide a sense of the future of search as everyday users find themselves as pushed to the big data limits as enterprises do now.