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Statistical Analysis of Massive Data Streams: Proceedings of a Workshop
Transcript of Presentation
MR. WILKINSON: All right, this session is on mining commercial streams of data, with Lee Rhodes, Pedro Domingos and Andrew Moore. Only one of our speakers is from a company although the other two, as you know, are involved in developing procedures, high-dimensional searches, and mining and other areas that are highly relevant to what is done by businesses.
I just want to highlight the three major market shares of applications of streaming data analysis, and these are quite large. Monitoring and process control involves such applications as General Electric with its turbines worldwide. There are many, many turbines and, to shut down a turbine, can cost millions of dollars per day in their system. So, they need to maintain continuous multivariate data stream monitoring on those turbines, and they have real needs for display and alert and analysis capabilities.
E-commerce goes without saying. We all know pretty much where that lies. Many are putting e-commerce data and Web logs into databases, but Amazon and other companies are analyzing these in real-time.
Financial is another huge area for streaming data. I thought I would give you a quick illustration of how that gets used.
This is a JAVA application called Dancer that is based on the graphics algebra, and the data we are putting into it now, we happen to be offline, of course, but this data feed is simulating a natural stream coming in.
These are Microsoft stock trades, and these are coming in at roughly 5 to 10 per second. On the right, you see the list of trading houses, like Lehman Brothers, and so on. These trades, the symbol size is proportional to the volume of the trade. Up arrow is a buy, down arrow is a sell order, and then a cross trade is a rectangle. These traders want to be able to do things like alter time, back it up, and reverse it. Those of you who have seen the TiVo system for TV, video, know that these kinds of manipulations of time can be critical.
This application, by the way, is not claiming this as a visualization. It is actually doing the calculations as soon as the real-time feed comes in. Notice all the scaling is being done on the fly. You can speed up the series. If you speed this up fast enough, it is a time machine, but I won’t go into that. I will show you just one more aspect of real-time graphics, and these are the kinds of graphics that you plug into what the rest of you guys do.
When you develop algorithms, you can plug them into graphic displays of this sort. This one simulates the way I buy stock. Actually, I don’t buy stock for this reason. It is just a simple exponential forecast.
You can see the behavior. This is trading in Oracle and SBSS. This type of a forecast represents exactly what I do and probably some of you as well which is, as soon as it starts going up a little bit, buy.
What is being done here, the model is being computed in real-time. So, you get, in this kind of a system, anywhere from 10 updates a second to 10,000 data events per second, and 90 percent of the effort in developing software in this area is in the data handling. How do you buffer 10,000 events per second and then render in roughly frames per second using the graphic system? So, the rendering system is a lot simpler than the actual data handling system.