Real-time digital twins offer unique advantages for intelligent, real-time monitoring, especially for thousands of telemetry streams, such as from car/truck fleets, intrusion sensors, or other IoT applications. By maintaining dynamic context information for all data sources and benefiting from automatic event correlation, digital twins can track and respond to these telemetry streams in milliseconds. In addition, digital twins provide a basis for aggregate analysis that can identify developing trends in seconds and prioritize the data sources that need immediate attention. In short, the use of real-time digital twins for streaming analytics offers a powerful new tool for boosting “situational awareness” in applications that perform real-time monitoring.
Consider a rental car or truck fleet that easily can comprise more than 100K vehicles. Real-time digital twins can monitor and continuously assess each vehicle’s progress and condition with important context information about both the vehicle and driver, such as intended route, maintenance history, and the driver’s profile. This enables more intelligent introspection on incoming telemetry if a vehicle is delayed, becomes lost, or behaves erratically.
In addition, aggregate analysis across all vehicles quickly pinpoints emerging trends, such as highway blockages due to accidents or floods, that may require immediate attention or feedback to the vehicles. The ability to triage the state of an entire fleet within a few seconds enables monitoring personnel to immediately focus attention on the most important problems and not overlook critical issues due to the sheer volume of incoming telemetry.
ScaleOut Software’s in-memory computing technology makes this all possible. ScaleOut StreamServer® provides a scalable, highly available software platform for hosting real-time digital twin models created in C#, Java, or JavaScript using the ScaleOut Digital Twin Builder™ software toolkit. By hosting these digital twins as memory-based objects and automatically correlating incoming telemetry by data source, this platform enables application developers to easily construct real-time analytics code which can introspect on incoming telemetry. The use of digital twins ensures immediate access to dynamic state information for each data source. Typical event processing times are less than 5 milliseconds. In addition, data-parallel analytics can perform MapReduce analytics across all digital twins every few seconds to identify emerging trends and boost situational awareness. These capabilities create important breakthroughs in software technology for real-time stream-processing, enabling simplified design, deeper introspection, and integrated, data-parallel computing for improved situational awareness.