The National Science Foundation has awarded a three-year $499,753 grant to Elke Rudensteiner of the Worcester Polytechnic Institute (WPI) to develop the next-generation event trend analysis tool called Scalable Event Trend Analytics (SETA) that will allow users to find patterns in and make sense of high volume data streams.
Sharon Gaudin explains why SETA could presumably transform the data analytics industry in this post from the WPI website:
SETA could enable large businesses, social media sites, fraud detection centers, autonomous vehicle networks, governments, and other users to harness the continuous flow of big data as it streams in and transform it into actionable insights that could allow them to be increasingly responsive and competitive. “In a world where big data is continuously accelerating in volume and velocity, real-time streaming data analysis has become increasingly critical,” said Rundensteiner, an internationally recognized expert in scalable data stream processing.
Event processing is a way to track and analyze incoming streams of information, such as online purchases, the rise and fall of a stock price, the length of time users remain on a website, or whether healthcare workers wash their hands before entering patients’ rooms. It’s all about flagging important events in the incoming data, so an organization can respond to them in real time. SETA will be able to handle complex queries and analytics, while providing users summarized insights cheaper and faster than is currently possible.
Most existing data analysis tools are not designed to work with streaming data, Rundensteiner noted. Instead, information must be stored in a static database before it can be analyzed, introducing a delay that might prevent the fast detection, for example, of the start of an infectious disease outbreak in a hospital. Rundensteiner’s tools operate on the data as it is being generated, allowing even complex patterns to be spotted in real time, so critical decisions can be made quickly.
“Data streams are increasing at a dramatic rate, overwhelming businesses that can’t make sense of their data in real time,” Rundensteiner said. “By finding ways to handle these live streams, we are breaking new ground in data analytics. You could stick all this big data into a static database and look at it later, but if you want to catch a fraudulent credit card purchase as it’s happening or alert a network of autonomous cars about an accident ahead, you need to analyze that information as it’s streaming in at the rate of tens of thousands of pieces of data per microsecond.”
