關於這一集
A smart security camera takes in a high volume of video images and processes those images using a set of machine learning models. Those models can be used to identify interesting snippets of movement throughout the day, and decide which of those snippets to keep. Some of the video snippets might contain movement of birds–but other video snippets might contain footage of intruders.
As the video stream is processed on the smart security camera, and machine learning models are used to classify the entities in the video stream, some of the data gets thrown out as useless. Some of the data gets sent to the cloud for additional processing. Some of the data might trigger an alert that there is an intruder on the premises. Each piece of video data is an “event”. These events are processed and acted upon.
Modern applications are highly interactive, and have lots of “events”. Other examples of event data streams are website traffic data, self-driving car data, time-series logging data, and video game session data. Building applications that respond to these high volumes of events requires us to program triggers to react to data streams, actions to take in response to the data streams, and workflows to orchestrate what the overall picture of our application is doing as the application is consuming this large data stream.
Flogo is an event-driven ecosystem for building applications around streams of events. Leon Stigter and Matt Ellis work on Flogo at TIBCO, and they join the show to discuss event-driven application development and their work on Flogo. They also talk about the constraints of machine learning applications at the edge, and how event processing systems like Flogo can be used to handle large data streams on edge devices.
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