The growth of high velocity, real-time digital, audio and video streaming data present new challenges to industrial analytics. Traditional procedural programming isn’t well suited to rapidly changing conditions, multi-stream correlations and inferences. Foghorn’s complex event processor uses a “reactive expression” approach that links the flow of program execution to and within the analytic to the streaming data available to it.
Our language, Vel, provides a syntax that can describe reactions to events in streaming data in a simple and logical fashion. This simplicity reduces the amount of coding required for streaming edge analytics, which also improves its maintainability. The complex event processor, in turn, is tightly integrated with data consumption, publications, and machine learning modules that complete the Foghorn edge computing platform.
The reactive approach enables a host of new benefits for real-world applications such as predictive maintenance, condition monitoring, yield optimization, and anomaly detection.
This session provides insight into:
• Reactive expressions as applied to unbounded streaming data
• The advantages of reactive expressions over procedural programming in industrial edge analytics
• Real-world use cases detailing where the technology is being applied today