PORTLAND, Ore.—Space weather forecasts predicting communications disruptions caused by sun-spots will begin later this month by IBM, Uppsala University (Sweden) and the Swedish Institute of Space Physics computing. By integrating the signals from geographically dispersed 3D radio antennas, clusters of blades will issue space weather forecasts for the next 18-to-24 hours from the recent state of the Sun.
This space weather forecasting capability is aimed at mitigating interference from Sun-spot Cycle 24—an 11-year cycle slated to begin this year. Sun-spot Cycle 24 is expected to disrupt satellite and terrestrial communications here on Earth as well as some landline communications and even the power grid. Early warning is theoretically possible, but the data processing task is enormous, since over 6 Gbytes of data needs to be sifted every second.
"We solve problems where you have lots and lots of data coming in and you need to extract a conclusion from that data," said Nagui Halim, IBM's Director Stream Computing Research and the creator of IBM's InfoSphere Streams software.
For the sunspot evaluation problem, the conclusion to be extracted from solar antennas is whether Earth-based communications systems—especially satellites—should be warned about impending interference from peculiarly powerful sunspot activity which is expected over the next few years. For instance, during the 22nd Sun-spot Cycle in 1989 an electromagnetic storm whipped up by the solar wind disrupted power throughout most of Quebec as well as causing auroras (Northern lights) as far south as Texas. Solar coronal mass ejections, coronal holes and solar flares cause a solar wind shock wave which can cause geomagnetic storms in the Earth's magnetosphere.
And assessing massive amounts data streaming from the sun is just one of many modern applications that are coping with data overload from multiple inexpensive sensors today. IBM's InfoSphere Streams software, which was just announced earlier this year, was crafted to solve information overload problems in any field, for instance it is also being used for real-time defect detection during semiconductor wafer processing.
"In general, the big challenge for streaming analytic applications is to construct a computing platform that can cope by supporting applications that are adaptive and which process the data in a way that is intelligent—not over-computing not under-computing—but adjusting in a way that's easy and inexpensive."
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