[vegabook]

In finance this is a classic problem and is why you see traders with proliferations of monitors, trying to track everything, and they are anyway outmatched by the machines. The irony is that the vast majority of tick data points do not contain much incremental information. They're very correlated. Think how most stocks tend to move in lockstep to the index.

We use principal components analysis a lot to cut thousands of feeds and get the "big picture" in usually 4-6 "global" variables, and then we use PCA regression to find the "outliers" in the rest of the data and show those. Thus we get at least 2-3 orders of magnitude less data that allows mere humans to actually interpret - big picture + outliers - and it's very rare that using this simple technique we ever miss much. And it can literally cut thousands of feeds into a couple of dozen. We've found this to be much more effective than creating animated "dot swarms" which look beautiful but are very poor at conveying rich information.