As more devices become connected and the internet of things grows, there is an increasing need for sophisticated but lightweight analytics at the edge. Decisions have to be made quickly with data coming from multiple devices. But when patterns of interest are detected, do you store data for later analysis or act now? And what course of action should you take?
Evan Guarnaccia walks you through a multiphase analytics approach to IoT data, analyzing data at rest to discover patterns of interest and develop analytical models that can be easily deployed into a streaming analytics engine out at the edge, in the fog, or in the cloud.
This session is sponsored by SAS.
Evan Guarnaccia is a solutions architect in the Internet of Things Division at SAS, where he specializes in real-time analytics and internet of things (IoT) applications using SAS Event Stream Processing and helps customers understand the capabilities of SAS real-time solutions and how they can derive business value with streaming analytics. He also provides internal enablement and training regarding how to position and develop Event Stream Processing projects. Evan holds a PhD in experimental particle physics from Virginia Tech. His research involved collider physics and neutrino detection experiments, and his thesis was on the modeling and measurement of the cosmic muon flux at underground sites.
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