 So let's say you have a graph where it's a million nodes. Like, for example, let's say you're Twitter. I don't know how many people have joined Twitter, but I'm sure it's more than a million. How many people have joined Twitter? They have 50 million, I don't know. Yeah, in 2009 it was about a million, and then when Obama joined and Oprah did it just went straight up. Right. It's an over 100 million. I think they, last time they went public, they said they had 80 million. Okay, so we've got enough data points. It's pushing 100 million in pattern matching. So you've got these giant graphs, right? So a lot of what you want to do on those graphs is you want to find patterns in this graph. So you want to find, for example, you want to find and give me an instance of, tell me who is connected to both Barack Obama and also Lady Gaga, right? So you want to pay a list of people who have connected to both, or who has been retweeted by both those people. That'd be an interesting query to ask. So in order to sort of issue that, so that type of query turns out to be sort of a sub-graph pattern matching query where you have a graph of who's retreating whom or who's connected to whom. And you want to sort of find some part of that graph in the larger context of much larger graph. So it turns out that you can do it pretty easily on a single node. When your graph is split across hundreds of nodes or even thousands of nodes, like, for example, Facebook or Twitter, then it becomes much more complex. And so we have some research trying to solve that problem.