Could data mining help with the automation vs. hand flying debate?
As much as there are difficulties in determining what the aircraft did/doing, there are also as many external parameters which are just as difficult to establish. For landing, any difference between the reported runway friction and that experienced, similarly the wind speed. Then there are problems of human interaction, ability vs achievement, or the choice of braking level or any change when on the runway; why. Landing distances already have a 60% margin for ‘normal’ variability (depending on application and conditions), but accidents appear to indicate that the combination of variability is more significant; a good target for data mining.
Thus where data may help with retrospective analysis, there are considerable limitations in real time use.
I prefer a more strategic view, aiding organisational factors which can be very powerful, but this should not exclude ‘strategic’ aiding during operations. As an example the landing data could indicate the level of risk in the crew’s choice during a pre landing briefing – expected landing distance, braking action, windspeed; although these data are not known precisely, previous data mining may be able to indicate the risks from their variability, at a particular time/location – a sort of ‘have you considered this’, or ‘add some safety margin’. I can imagine a flight deck display where if ‘3 lemons’ line up the situation requires a diversion, but even then with the variability of human perception, some people’s lemons are oranges.
I am not involved with any these activities, but take interest in ‘technological safety’. E.g. the effectiveness of ‘reactive’ warnings (accuracy permitting) vs proactive, strategic awareness. Reactive systems might encourage complacency and a dependency on the alerts, whereas proactive information might guide thoughts – ‘should we be doing this’ vs ‘how can we do it’; frame the anticipated situation - dynamic awareness.
This opens up alternative views of data use – not just number crunching, but the formation of meaningful data (information) for human use (reactive/proactive), and all within an operating environment (context); man-machine-environment. Thus we should be aware of any narrowing views – ‘not how to solve this problem’, instead ‘what characteristics identify these problems’, and can these be assessed in context and time.
Fresh of the press; with links to previous conferences at the bottom of the page: EASA - 3rd Conference of the European Operators Flight Data Monitoring forum (EOFDM)
Thus where data may help with retrospective analysis, there are considerable limitations in real time use.
I prefer a more strategic view, aiding organisational factors which can be very powerful, but this should not exclude ‘strategic’ aiding during operations. As an example the landing data could indicate the level of risk in the crew’s choice during a pre landing briefing – expected landing distance, braking action, windspeed; although these data are not known precisely, previous data mining may be able to indicate the risks from their variability, at a particular time/location – a sort of ‘have you considered this’, or ‘add some safety margin’. I can imagine a flight deck display where if ‘3 lemons’ line up the situation requires a diversion, but even then with the variability of human perception, some people’s lemons are oranges.
I am not involved with any these activities, but take interest in ‘technological safety’. E.g. the effectiveness of ‘reactive’ warnings (accuracy permitting) vs proactive, strategic awareness. Reactive systems might encourage complacency and a dependency on the alerts, whereas proactive information might guide thoughts – ‘should we be doing this’ vs ‘how can we do it’; frame the anticipated situation - dynamic awareness.
This opens up alternative views of data use – not just number crunching, but the formation of meaningful data (information) for human use (reactive/proactive), and all within an operating environment (context); man-machine-environment. Thus we should be aware of any narrowing views – ‘not how to solve this problem’, instead ‘what characteristics identify these problems’, and can these be assessed in context and time.
Fresh of the press; with links to previous conferences at the bottom of the page: EASA - 3rd Conference of the European Operators Flight Data Monitoring forum (EOFDM)
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In medicine "Data Mining" is referred to as epidemiology and it's as old as the hills even predating computers. It is quite a difficult study to get right and employs many scientific disciplines.
Epidemiology provides numerous health benefits to the community and in some respects the same principles can be also applied to aircraft/engine health.
However I'm having trouble understanding any tangible benefit out of such a non-deterministic system that is being proposed here. If the system said "Don't fly with cheapskate airline X in February because that is when they are most likely to crash" then nobody will fly with them in February and they won't crash.
Epidemiology provides numerous health benefits to the community and in some respects the same principles can be also applied to aircraft/engine health.
However I'm having trouble understanding any tangible benefit out of such a non-deterministic system that is being proposed here. If the system said "Don't fly with cheapskate airline X in February because that is when they are most likely to crash" then nobody will fly with them in February and they won't crash.
Piltdown, when humans are able to determine where the aircraft will end up, and explain why, then perhaps the programmers might be able to code this and use the processing power.
Also, remember that for any meaningful output there has to be a corresponding accurate input, otherwise it’s just a guess.
In most circumstances an experienced human provides a very good ‘guess’ (but rarely, not always good enough); however if data-mining could provide a more accurate guess based many previous successful human guesses (humans don’t learn/forget or are corrupted by bias) then the computation might be able to advise the human that they will not end up where they plan to (providing the human declares the intention).
At the other end of the scale there are situations where the human will excel; here the computer should not intervene. Thus there is problem to determining which situation applies (context). Data-mining might first focus on establishing the situation (requires sensor accuracy), then indicate the level of risk which is in a choice of action, bearing in mind that humans tend to underestimate risk (operators and programmers).
Also, remember that for any meaningful output there has to be a corresponding accurate input, otherwise it’s just a guess.
In most circumstances an experienced human provides a very good ‘guess’ (but rarely, not always good enough); however if data-mining could provide a more accurate guess based many previous successful human guesses (humans don’t learn/forget or are corrupted by bias) then the computation might be able to advise the human that they will not end up where they plan to (providing the human declares the intention).
At the other end of the scale there are situations where the human will excel; here the computer should not intervene. Thus there is problem to determining which situation applies (context). Data-mining might first focus on establishing the situation (requires sensor accuracy), then indicate the level of risk which is in a choice of action, bearing in mind that humans tend to underestimate risk (operators and programmers).
Thread Starter
Good question
cattletruck-
I had to think about your example for a while because a lot of my time is healthcare oriented- but I would have never imagined what I think of as near real time recommendations base on data mining at this point in hearth care-
The reason is that healthcare data hasn't been readily available (stuck in charts without incentives to make operational data available) and algorithms need volume to throw out the outliers to build the normal model.
But even with trends to change data capture, standards, and create a sharing infrastructure, I'm not sure that I would imagine the near real time analysis that we are talking about in the near future in health care- Heath providers have a lot more control of their clinical environment and they intentionally limit a lot of variables that are unrealistic in aviation.
I get the idea of data mining in health care- model building, identifying unimagined causality, it makes total sense, but I would have imagined this would be used for training and developing better procedures because I would imagine that heathcare outcomes are also far more deterministic.
On the other hand, if aviation had the equivalent to millions of labrats (= aircraft) that could be tested to the point of failure over and over for hundreds of years, and that data was collected, I would think the comparison would be closer?
In a way, that is what I am imagining... if thousands of drones are delivering pizza, we're going to see a lot more failures, the data sets get bigger, and cause or failure might lead to more traditional fixes (training and developing better procedures).
No idea if any of this fits your example, but I appreciate the analogy...
I had to think about your example for a while because a lot of my time is healthcare oriented- but I would have never imagined what I think of as near real time recommendations base on data mining at this point in hearth care-
The reason is that healthcare data hasn't been readily available (stuck in charts without incentives to make operational data available) and algorithms need volume to throw out the outliers to build the normal model.
But even with trends to change data capture, standards, and create a sharing infrastructure, I'm not sure that I would imagine the near real time analysis that we are talking about in the near future in health care- Heath providers have a lot more control of their clinical environment and they intentionally limit a lot of variables that are unrealistic in aviation.
I get the idea of data mining in health care- model building, identifying unimagined causality, it makes total sense, but I would have imagined this would be used for training and developing better procedures because I would imagine that heathcare outcomes are also far more deterministic.
On the other hand, if aviation had the equivalent to millions of labrats (= aircraft) that could be tested to the point of failure over and over for hundreds of years, and that data was collected, I would think the comparison would be closer?
In a way, that is what I am imagining... if thousands of drones are delivering pizza, we're going to see a lot more failures, the data sets get bigger, and cause or failure might lead to more traditional fixes (training and developing better procedures).
No idea if any of this fits your example, but I appreciate the analogy...
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Mr Strat2, no doubt your idea got one thinking so therefore it must be a good one, just the focus and end result may not be right, perhaps the world isn't quite ready for it.
In the immediate term, I can see a use for this kind of analysis in providing high level consultative information. For example, my friend runs a company that provides high level and well researched executive briefs on what's happening in the fast paced telecommunication industry, of which he has clients paying a premium for it. His motto is "Distilling market noise into market sense".
Create something similar in the aviation industry and you'd be on a winner.
In the immediate term, I can see a use for this kind of analysis in providing high level consultative information. For example, my friend runs a company that provides high level and well researched executive briefs on what's happening in the fast paced telecommunication industry, of which he has clients paying a premium for it. His motto is "Distilling market noise into market sense".
Create something similar in the aviation industry and you'd be on a winner.
