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How Data Science Powered the Search for MH370 (hpe.com)
"In the absence of physical evidence, scientists are employing powerful computational tools to attempt to solve the greatest aviation mystery of our time: the disappearance of flight MH370." Slashdot reader Esther Schindler shared this article from HPE Insights:
Satellite communications provider Inmarsat announced it had found recorded signals in its archives that MH370 had sent for another six hours after it disappeared. The plane had been aloft and flying for that whole time -- but where had it gone? As Inmarsat scientists examined the signals, they saw that what they had was not data such as text messages or location information. Rather, the signals contained metadata: information about the signal itself. This was recorded as the satellite automatically contacted the plane's communications system every hour to see if it was still logged on. Bafflingly, whoever had taken the plane hadn't used the satcom system to communicate with the outside world, but had switched it off and then on again, leaving it able to exchange hourly "pings" with the satellite. Some of the metadata related to extremely subtle variations in the frequency of the signal. "We're talking about changes as big as one part in a billion," says Inmarsat scientist Chris Ashton.
Nobody had tried to use this kind of data to try to locate an airplane before. At first, Ashton's team didn't know if the attempt would work. But painstakingly, over the course of weeks, the team figured out how the movement of the plane, the orbital wobble of the satellite, and the electronics within the satcom system all interacted to create the data values that had been received. "We had to create the model from scratch," Ashton says. Their work revealed that the plane had flown into the remote southern Indian Ocean. They didn't know where exactly. But since there are no islands in that part of the world, it was impossible that anyone could have survived. For the first time in history, hundreds of people were declared legally dead based on mathematics alone.
Then mathematician Dr. Neil Gordon led a team from the Defense Science and Technology Group "to extract a path from a subset of the Inmarsat data called the Burst Timing Offset. This measured how quickly the aircraft responded each time the satellite pinged it, and was used to determine the distance between the satellite and the plane." They ultimately generate "a probabilistic 'heat map' of the plane's most likely resting places using a technique called Bayesian analysis. These calculations allowed the DSTG team to draw a box 400 miles long and 70 miles across, which contained about 90 percent of the total probability distribution.
Nobody had tried to use this kind of data to try to locate an airplane before. At first, Ashton's team didn't know if the attempt would work. But painstakingly, over the course of weeks, the team figured out how the movement of the plane, the orbital wobble of the satellite, and the electronics within the satcom system all interacted to create the data values that had been received. "We had to create the model from scratch," Ashton says. Their work revealed that the plane had flown into the remote southern Indian Ocean. They didn't know where exactly. But since there are no islands in that part of the world, it was impossible that anyone could have survived. For the first time in history, hundreds of people were declared legally dead based on mathematics alone.
Then mathematician Dr. Neil Gordon led a team from the Defense Science and Technology Group "to extract a path from a subset of the Inmarsat data called the Burst Timing Offset. This measured how quickly the aircraft responded each time the satellite pinged it, and was used to determine the distance between the satellite and the plane." They ultimately generate "a probabilistic 'heat map' of the plane's most likely resting places using a technique called Bayesian analysis. These calculations allowed the DSTG team to draw a box 400 miles long and 70 miles across, which contained about 90 percent of the total probability distribution.
This is an old story, and I think the analysis has shown it to be more conjecture that a real model. There is simply too many assumptions that need to be made, the variables are too complex.
The analysis was interesting, and if it had worked would have a great feat of data analysis. But over a year ago drift analysis of the wreckage indicated that any probably location was nowhere near the IMSAT estimated locations.
"She's a scientist and a lesbian. She's not going to let it slide." Orphan Black
I think /pol/ is getting bored of ruining Shia's life. A week ago he put the flag on top of some music hall in France and all they've done so far is launch a flaming drone at it in a half-hearted attempt to set it on fire.
How about declared dead after they couldn't find the plane for over 9 months and no one had established contact?
This article is a load of crap. It's an example of how these data models have failed to achieve anything useful. Firstly after almost 2 years they announced that they were looking in the wrong place: http://www.aljazeera.com/news/... and also that they were confident that after spending $200m the plane was not in the search area they established. https://www.theguardian.com/wo...
Good work big data!