Jeff Jonas is Chief Scientist, IBM Entity Analytics Group and an IBM Distinguished Engineer. The IBM Entity Analytics Group was formed based on technologies developed by Systems Research & Development (SRD), founded by Jonas in 1984, and acquired by IBM in January, 2005.
In the first portion of this event, Mr. Jonas will speak about his latest research in the area of high volume data analytics. There is a series of macro trends playing out that will force organizations to re-think how they make sense of data and compete. While organizations already recognize that information overload results in inefficiency, missed opportunities and unidentified risks, the problem is only going to get worse as the volume of available data increases. What if it is possible to drive down false positives, drive down false negatives, and make faster predictions over more data ... at the same time?
As large collections of data come together, some very exciting and somewhat unexpected things happen. As data grows, the quality of predictions improves (less false positives, less false negatives). Additionally, poor quality data starts to become more helpful and computation can actually get faster as the number of records grows. Now, add to this, the “space-time-travel” data about how people move that is being created by billions of mobile devices and what becomes computable is outright amazing. As it turns out, geospatial data is analytic super-food.
In part two of this event, Mr. Jonas will cover his vision for “G2” and how this two-year "Skunk Works" software development effort relates to other IBM technologies such as Streams, Netezza and Watson. This breakthrough technology is designed to make sense of the world as observations present themselves, fast enough to do something about it while the observations are happening. G2 is designed to find the obvious by locating related observations that, when viewed together, point to something of interest. The unique quality of the G2 engine is its ability to perform this activity in real time over very large data volumes, beyond a human’s ability to locate this obvious relevance by hand.
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