high-performance computing ➤
and DRAM as a transistorless non-volatile big memory, he said: 100 times faster than SSDs but with 10 times the density of DRAM.
Driving towards convergence Te conference heard two examples of the convergence of big data, HPC, the cloud, and the Internet of Tings that well illustrated the pressures in favour of the sort of convergence of technologies outlined by Gillich. Both examples, as it happened, related to transport and in both cases, while they were vivid illustrations that the Internet of Tings is not just marketing hype but already a reality, the ‘Tings’ in these cases about which data was being reported and analysed were actually people: in both cases about how they were moving on the streets and using roads, in the one case, the data from their mobile phones and, in the other, their motor cars were transmitting the data. Michal Piorkowski, head of big data
Insights at Swisscom, reported how powerful data analytics applied to the data already being routinely collected about the location of individuals’ mobile phones was helping local government in Switzerland develop ‘smart’ urbanisation. Switzerland faced the prospect of becoming a megalopolis with rampant urbanisation, with demands for a higher quality of life, all having to be met out of limited local government budgets. Te telecoms companies already have
the mobile network providing monitoring data that is constantly updated, he pointed out. Te scale of the data gathering is immense: 20 billion events each day. By adding powerful data analytics, ‘we turn it into mobility data for urban specialists to implement interactive city planning.’ In real time, the data from mobile phones could produce information about the movement of crowds through the cities and about movements of people from one city to another. If machine learning is added to the data analytics, he said, it was possible to predict traffic jams before they occurred. For longer term urban planning purposes, the system could provide data on a daily basis on the scale of the whole country, whereas traditional survey-based techniques yielded only a single snapshot every four years or so. Te automotive industry is working
towards an agreed protocol that would allow data from cars to be transmitted back to a central point, using car to car communication (even between cars from rival manufacturers). Again, one benefit would be advance information about traffic jams but the major application will be in driverless cars.
16 SCIENTIFIC COMPUTING WORLD
Analytics applied to data already collected from mobile phones can help urban planners Andreas Sasse, head of mobile services
and data at Volkswagen Research, explained that it was not enough to surround a driverless car with sensors – laser ranger finders, IR scanners, and radar – to scan its neighbourhood, it also needed to communicate with other cars to understand the road conditions further ahead. Moreover, it needed to have a detailed map of the roads, down to a precise location of the position of the lanes on a multi-lane highway as well as of junctions and intersections. A modern car already has 15 map-related functions incorporated within it, he said
IT IS NOT ENOUGH TO SURROUND A DRIVERLESS CAR WITH SENSORS
(thus going far beyond SatNav). However, satellite navigation data tends to be updated at best once a year, whereas a driverless car would require a detailed lane model that was updated hourly, if not by the minute, and that would have to be highly reliable. It was a huge challenge that had to be
solved, he said: the map needs to be digital but it also needs to be a ‘learning map’. ‘Te cars themselves can give us feedback. Sometimes we can process data and send it back to the cars, but oſten we need to get back to the map compiler.’ Te major
German car manufacturers recently bought a SatNav company to push the mapping forward, collectively – it clearly makes sense for all the cars to have the same map and so mapping will not be a commercial differentiator in the era of driverless cars. Similarly, car to car communication is not going to be proprietary, he said. He estimated that about 11GB would be generated per car per day. Te cars can talk to each other over a WLAN-type system with a range of about two kilometres, but because of the common protocol, an Audi, for example, does not need to wait for another Audi to be within range before it can transmit. Sasse said that VW at present did not want
to cooperate with either Google or Apple, both of whom are developing driverless cars. ‘Tey have an established ecosystem and we would end up dependent on them,’ he said. He discounted Google’s highly publicised efforts: ‘I’m less worried by Google than by Apple’s urban electric car.’ He pointed out that Google is a computing company whose business is soſtware rather than things, and that it was too far away from scalability and from an actual car. It was a nice reminder, particularly in a
gathering such as the Cloud and Big Data conference, that these IT technologies are not ends in themselves, but rather tools that serve other purposes, involving people and physical objects. l
@scwmagazine l
www.scientific-computing.com
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