Interesting presentations
alf5071h- Were you part of this study? I really enjoyed the powerpoints and wonder if the entire presentation has been taped or transcribed? I believe I understood most of the data and conclusions, but it would be nice to fill in the dots.
This appears rather targeted, and I'm assuming that the dataset was large, however, I'm also assuming that datamining per se wasn't used... so it's probably significantly different than what I am talking about (however your example is probably more useful and likely to add value in the long run)-
The reason I say this is that glidesope, touch down point, and flap settings are exactly the variables that we would imagine might impact overruns and it does make sense to clean up the data, look for predictive behaviors, and use the outcome for training or designing systems.
To me, this is a lot like using doppler for windshear, or stickshakers for stalls- If the problem is big enough, it's likely that out of normal parameters will be identified and something will be done in the long haul. However, it is very point specific.
On the other hand, data mining provides the opportunity to find relationships that are far less intuitive. There is no way I can realistically imagine a strange enough scenario, however, for the sake of argument, let's pretend that wheels out a second later than average, plus wind gusts of x%, + one radio call repeat in the last 90 seconds before touchdown, increases overruns by y%.
Any one of those variables is unlikely to be a problem in itself, so they would fall within the 'normal' profile until all 3 show up together.
Of course this is a bad example because unless the relationship was extreme (perhaps a 25% increase in overruns) I can't imagine wanting to communicate with a pilot at such a critical point in the flight (again assuming that the odds are highly favorable that the pilot will make a safe landing).
But what if there is a 4th, seemingly unrelated variable that pushes the overun odds beyond 25%? And what about the thousands of other 'holes in the cheese' where we never imagined cause and effect?
Again, there probably aren't any immediate applications for what I am talking about. However, when aviation goes primarily autonomous, it seems very likely that the virtual pilot will be provided with some kind of similar feed because the virtual pilot will have the bandwidth to deal with it.
So this gets me thinking in another direction... I wonder if data mining is in use with autonomous cars that are already on the road?
Thanks again for the thought experiments!