Search Framed as Iterative Discovery
December 17, 2012
Finally, we come across an article that puts search at the forefront of big data. The post “Big Data or Big Noise?” from Chilliad discusses a “new” approach to the process of search.
Relevancy has always been an issue in search and this article is nothing short of correct to point that out. This post also points to the idea that irrelevant results can become big noise and it would be a waste of time for users to read that noise.
Chilliad humbly suggests that users do not need to know what we are looking for, where to find it, or how to figure that out:
“In fact, reading is not the next thing I want to do, reading is the last thing I want to do. That is why we approach Big Data as an exploration and provide software that supports an approach we call Iterative Discovery. Iterative Discovery is exactly what it sounds like — I start with a hunch or hypothesis that I wish to validate and that requires exploration and iteration through massive amounts of data.”
The problem we have with this concept is that it does not need as much of an explanation as Chilliad gives it. Iterative discovery is a way of framing search, but it is nothing innovative or out of the box.
Megan Feil, December 17, 2012
Sponsored by Arnold IT.com, developer of Augmentext
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