InfoWorld: What are you going to do with the connected data?
Eifrem: Let's take one example, which is search. Search, pre-1999, basically worked the same way for all the 20, 30, 50 firms that tried to do Web search, which is all of them downloaded the entirety of the Web into their data centers, and then they searched into every individual document. If you search for Paul, it would look into every individual document and find if that document mentions Paul. And then they would serve that. Pretty simple. We call that atomic data. They use data only about every individual entity.
Then in 1999, along comes Google, which says that -- hey, on top of this atomic data, we're also going to look at how these pages are connected to one another and they call that the Link Graph. And they called the algorithm Page Rank, and that invention was enough to make them the most dominant company, I think, of the last decade. And they did that based on -- let's leverage this connected data rather than just atomic data.
And then, of course, 2012 and 2013, search made its next discontinuous leap, which was when Google announced their Knowledge Graph, which is not just how pages are related to other pages but they also start to model the actual entities in these pages. For example, if you have a Web page about a movie, previously Google only recorded what other pages this page linked to, but now they also look into the page and see that -- hey, this is a page about "Apollo 13," and "Apollo 13" actually stars Kevin Bacon, and Kevin Bacon has also starred in these other movies. And they build up this big connected data structure they call the Knowledge Graph.
InfoWorld: What is the purpose of a Knowledge Graph or a graph database in an enterprise?
Eifrem: In an enterprise, the point is that it's going to help you deliver a lot -- much better search results. You're going to be able to look through information in a way [that] makes it much more targeted and much easier to find out relevant things.
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